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Journal articles on the topic 'Autonomous vehicles'
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1
Tulsyan, Ansh, Anshul Bhardwaj, Pranjal Shukla, Piyush Gautam, and Tushar Singh. "Autonomous Vehicle Simulation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (January 5, 2025): 1–9. https://doi.org/10.55041/ijsrem40459.
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Autonomous vehicles represent a revolutionary advancement in transportation technology, relying heavily on sophisticated simulations for development and testing. This abstract presents an approach to autonomous vehicle simulation utilizing the Udacity Self-Driving Car Nanodegree platform coupled with Convolutional Neural Networks (CNNs) for perception tasks. The simulation environment provided by Udacity offers a realistic representation of urban driving scenarios, allowing developers to test and train autonomous vehicle algorithms in a virtual setting before deploying them on real-world roads. This environment includes various elements such as traffic lights, pedestrians, and other vehicles, providing a comprehensive testbed for algorithmic validation. To enhance the perception capabilities of autonomous vehicles within this simulated environment, Convolutional Neural Networks (CNNs) are employed. CNNs have proven effective in image recognition tasks, making them well-suited for tasks like object detection and classification crucial to autonomous driving. The neural network is trained on a diverse dataset, enabling it to accurately identify and interpret the surrounding environment through sensor data, such as camera images. The integration of CNNs with the Udacity simulation platform enables the autonomous vehicle to make informed decisions based on its perception of the environment. The trained CNN serves as a crucial component in the overall perception pipeline, enhancing the vehicle's ability to recognize and respond to dynamic and complex scenarios. Keywords—Autonomous vehicle simulation, Artificial Intelligence, Object Recognition, Real-Time Processing, convolutional neural networks.
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PRATA, Tiago, Miguel MIRA DA SILVA, Flávia SANTORO, and António REIS PEREIRA. "ASSESSING THE DEVELOPMENT OF AUTONOMOUS CARS." Transport Problems 19, no. 1 (March 30, 2024): 209–18. http://dx.doi.org/10.20858/tp.2024.19.1.17.
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With recent innovations regarding autonomous vehicles and the fact that several vehicle brands have started to deploy autonomous driving functionalities, it is still unknown what these innovations may offer to social lives. Owing to the ability to autonomously drive from one location to another, the concept of shared autonomous vehicles was created to let an individual turn their assets into a source of income while other individuals could use this service without having to own a vehicle. The development of this emerging concept was aided by an evaluation of an ontology already presented regarding the topic of shared autonomous vehicles performed by three different frameworks (OQuaRE, OntoMetrics and OOPS) that generally agreed with the validity of the proposed ontology.
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Kim, JongBae. "Deep Learning-Based Vehicle Type and Color Classification to Support Safe Autonomous Driving." Applied Sciences 14, no. 4 (February 17, 2024): 1600. http://dx.doi.org/10.3390/app14041600.
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This technology can prevent accidents involving large vehicles, such as trucks or buses, by selecting an optimal driving lane for safe autonomous driving. This paper proposes a method for detecting forward-driving vehicles within road images obtained from a vehicle’s DashCam. The proposed method also classifies the types and colors of the detected vehicles. The proposed method uses a YOLO deep learning network for vehicle detection based on a pre-trained ResNet-50 convolutional neural network. Additionally, a Resnet-50 CNN-based object classifier, using transfer learning, was used to classify vehicle types and colors. Vehicle types were classified into four categories based on size whereas vehicle colors were classified into eight categories. During autonomous driving, vehicle types are used to determine driving lanes, whereas vehicle colors are used to distinguish the road infrastructure, such as lanes, vehicles, roads, backgrounds, and buildings. The datasets used for learning consisted of road images acquired in various driving environments. The proposed method achieved a vehicle detection accuracy of 91.5%, vehicle type classification accuracy of 93.9%, and vehicle color classification accuracy of 94.2%. It accurately detected vehicles and classified their types and colors. These can be applied to autonomous and safe driving support systems to enhance the safety of autonomous vehicles.
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Hasan, Hasnawiya, Faizal Arya Samman, Muh Anshar, and Rhiza S. Sadjad. "Autonomous vehicle tracking control for a curved trajectory." Bulletin of Electrical Engineering and Informatics 13, no. 3 (June 1, 2024): 1535–45. http://dx.doi.org/10.11591/eei.v13i3.6060.
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Recently, research about trajectory tracking of autonomous vehicles has significantly contributed to the development of autonomous vehicle technology, particularly with novel control methods. However, tracking a curved trajectory is still a challenge for autonomous vehicles. This research proposes a state feedback linearization with observer feedback to overcome some difficulties arising from such a path. This approach suits a complex nonlinear system such as an autonomous vehicle. This method also has been compared with the linear-quadratic regulator (LQR) method. So, the goal of this research is to improve the control system performance of autonomous vehicles that are stable enough to navigate a curved path. Moreover, the study shows that the developed control law can track the curved path and solve existing problems. However, improvements are still necessary for the vehicle's performance and robustness.
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Alsuwian, Turki, Mian Hamza Usman, and Arslan Ahmed Amin. "An Autonomous Vehicle Stability Control Using Active Fault-Tolerant Control Based on a Fuzzy Neural Network." Electronics 11, no. 19 (October 1, 2022): 3165. http://dx.doi.org/10.3390/electronics11193165.
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Due to instability issues in autonomous vehicles, the risk of danger is increasing rapidly. These problems arise due to unwanted faults in the sensor or the actuator, which decrease vehicle efficiency. In this modern era of autonomous vehicles, the risk factor is also increased as the vehicles have become automatic, so there is a need for a fault-tolerant control system (FTCS) to avoid accidents and reduce the risk factors. This paper presents an active fault-tolerant control (AFTC) for autonomous vehicles with a fuzzy neural network that can autonomously identify any wheel speed problem to avoid instability issues in an autonomous vehicle. MATLAB/Simulink environment was used for simulation experiments and the results demonstrate the stable operation of the wheel speed sensors to avoid accidents in the event of faults in the sensor or actuator if the vehicle becomes unstable. The simulation results establish that the AFTC-based autonomous vehicle using a fuzzy neural network is a highly reliable solution to keep cars stable and avoid accidents. Active FTC and vehicle stability make the system more efficient and reliable, decreasing the chance of instability to a minimal point.
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Bhavsar, Parth, Plaban Das, Matthew Paugh, Kakan Dey, and Mashrur Chowdhury. "Risk Analysis of Autonomous Vehicles in Mixed Traffic Streams." Transportation Research Record: Journal of the Transportation Research Board 2625, no. 1 (January 2017): 51–61. http://dx.doi.org/10.3141/2625-06.
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The introduction of autonomous vehicles in the surface transportation system could improve traffic safety and reduce traffic congestion and negative environmental effects. Although the continuous evolution in computing, sensing, and communication technologies can improve the performance of autonomous vehicles, the new combination of autonomous automotive and electronic communication technologies will present new challenges, such as interaction with other nonautonomous vehicles, which must be addressed before implementation. The objective of this study was to identify the risks associated with the failure of an autonomous vehicle in mixed traffic streams. To identify the risks, the autonomous vehicle system was first disassembled into vehicular components and transportation infrastructure components, and then a fault tree model was developed for each system. The failure probabilities of each component were estimated by reviewing the published literature and publicly available data sources. This analysis resulted in a failure probability of about 14% resulting from a sequential failure of the autonomous vehicular components alone in the vehicle’s lifetime, particularly the components responsible for automation. After the failure probability of autonomous vehicle components was combined with the failure probability of transportation infrastructure components, an overall failure probability related to vehicular or infrastructure components was found: 158 per 1 million mi of travel. The most critical combination of events that could lead to failure of autonomous vehicles, known as minimal cut-sets, was also identified. Finally, the results of fault tree analysis were compared with real-world data available from the California Department of Motor Vehicles autonomous vehicle testing records.
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Raiyn, Jamal. "Data and Cyber Security in Autonomous Vehicle Networks." Transport and Telecommunication Journal 19, no. 4 (December 1, 2018): 325–34. http://dx.doi.org/10.2478/ttj-2018-0027.
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Abstract An autonomous vehicle (AV) is a vehicle that operates and performs tasks under its own power. Some features of autonomous vehicle are sensing the environment, collecting information and managing communication with other vehicles. Many autonomous vehicles in development use a combination of cameras, sensors, GPS, radar, LiDAR, and on-board computers. These technologies work together to map the vehicle’s position and its proximity to everything around it. Because of their reliance on these sorts of technologies, which are easily accessible to tampering, a autonomous vehicles are susceptible to cyber attacks if an attacker can discover a weakness in a certain type of vehicle or in a company’s electronic system. This lack of information security can lead to criminal and terrorist acts that eventually cost lives. This paper gives an overview of cyber attack scenarios relating to autonomous vehicles. The cyber security concept proposed here uses biometric data for message authentication and communication, and projects stored and new data based on iris recognition. Iris recognition system can provide other knowledge about drivers as well, such as how tired and sleepy they might be while driving, and they are designed to encrypt the vehicle-to-vehicle and vehicle-to-environment communication based on encryption security mechanisms.
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Gao, Kai, Di Yan, Fan Yang, Jin Xie, Li Liu, Ronghua Du, and Naixue Xiong. "Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario." Sensors 19, no. 19 (September 27, 2019): 4199. http://dx.doi.org/10.3390/s19194199.
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Car-following is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is problematic, and will significantly affect the car-following control of the autonomous vehicle, especially when the vehicle changing lanes is only a connected unintelligent vehicle without expensive and accurate sensors. Autonomous vehicles suffer from adjacent vehicles’ abrupt lane changes, which may reduce ride comfort and increase energy consumption, and even lead to a collision. A machine learning-based lane change intention prediction and real time autonomous vehicle controller is proposed to respond to this problem. First, an interval-based support vector machine is designed to predict the vehicles’ lane change intention utilizing limited low-level vehicle status through vehicle-to-vehicle communication. Then, a conditional artificial potential field method is used to design the car-following controller by incorporating the lane-change intentions of the vehicle. Experimental results reveal that the proposed method can estimate a vehicle’s lane change intention more accurately. The autonomous vehicle avoids collisions with a lane-changing connected unintelligent vehicle with reliable safety and favorable dynamic performance.
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Bautista-Camino, Pedro, Alejandro I. Barranco-Gutiérrez, Ilse Cervantes, Martin Rodríguez-Licea, Juan Prado-Olivarez, and Francisco J. Pérez-Pinal. "Local Path Planning for Autonomous Vehicles Based on the Natural Behavior of the Biological Action-Perception Motion." Energies 15, no. 5 (February 27, 2022): 1769. http://dx.doi.org/10.3390/en15051769.
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Local path planning is a key task for the motion planners of autonomous vehicles since it commands the vehicle across its environment while avoiding any obstacles. To perform this task, the local path planner generates a trajectory and a velocity profile, which are then sent to the vehicle’s actuators. This paper proposes a new local path planner for autonomous vehicles based on the Attractor Dynamic Approach (ADA), which was inspired by the behavior of movement of living beings, along with an algorithm that takes into account four acceleration policies, the ST dynamic vehicle model, and several constraints regarding the comfort and security. The original functions that define the ADA were modified in order to adapt it to the non-holonomic vehicle’s constraints and to improve its response when an impact scenario is detected. The present approach is validated in a well-known simulator for autonomous vehicles under three representative cases of study where the vehicle was capable of generating local paths that ensure the security of the vehicle in such cases. The results show that the approach proposed in this paper is a promising tool for the local path planning of autonomous vehicles since it is able to generate trajectories that are both safe and efficient.
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Vu, Trieu Minh, Reza Moezzi, Jindrich Cyrus, and Jaroslav Hlava. "Model Predictive Control for Autonomous Driving Vehicles." Electronics 10, no. 21 (October 24, 2021): 2593. http://dx.doi.org/10.3390/electronics10212593.
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The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subject to hard and softened constraints. Nonlinear model predictive control subject to softened constraints provides a higher probability of the controller finding the optimal control actions and maintaining system stability. Different parameters of the nonlinear model predictive controller are simulated and analyzed. Results show that nonlinear model predictive control with softened constraints can considerably improve the ability of autonomous driving vehicles to track exactly on different trajectories.
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Pyo, Jeong-Won, Sang-Hyeon Bae, Sung-Hyeon Joo, Mun-Kyu Lee, Arpan Ghosh, and Tae-Yong Kuc. "Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas." Sensors 22, no. 23 (November 23, 2022): 9094. http://dx.doi.org/10.3390/s22239094.
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Autonomous driving and its real-world implementation have been among the most actively studied topics in the past few years. In recent years, this growth has been accelerated by the development of advanced deep learning-based data processing technologies. Moreover, large automakers manufacture vehicles that can achieve partially or fully autonomous driving for driving on real roads. However, self-driving cars are limited to some areas with multi-lane roads, such as highways, and self-driving cars that drive in urban areas or residential complexes are still in the development stage. Among autonomous vehicles for various purposes, this paper focused on the development of autonomous vehicles for garbage collection in residential areas. Since we set the target environment of the vehicle as a residential complex, there is a difference from the target environment of a general autonomous vehicle. Therefore, in this paper, we defined ODD, including vehicle length, speed, and driving conditions for the development vehicle to drive in a residential area. In addition, to recognize the vehicle’s surroundings and respond to various situations, it is equipped with various sensors and additional devices that can notify the outside of the vehicle’s state or operate it in an emergency. In addition, an autonomous driving system capable of object recognition, lane recognition, route planning, vehicle manipulation, and abnormal situation detection was configured to suit the vehicle hardware and driving environment configured in this way. Finally, by performing autonomous driving in the actual experimental section with the developed vehicle, it was confirmed that the function of autonomous driving in the residential area works appropriately. Moreover, we confirmed that this vehicle would support garbage collection works through the experiment of work efficiency.
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KAYIN, Tolga, and Çağatay Berke ERDAŞ. "End-to-end, real time and robust behavioral prediction module with ROS for autonomous vehicles." Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 66, no. 1 (August 5, 2023): 1–25. http://dx.doi.org/10.33769/aupse.1292652.
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In the world where urbanization and population density are increasing, transportation methods are also diversifying and the use of unmanned vehicles is becoming widespread. In order for unmanned vehicles to perform their tasks autonomously, they need to be able to perceive their own position, the environment and predict the possible movements/routes of environmental factors, similar to living things. In autonomous vehicles, it is extremely important for the safety of the vehicle and the surrounding factors to be able to predict the future position of the objects around it with high performance so that the vehicle can plan correctly. Due to the stated reasons, the behavioral prediction module is a very important component for autonomous vehicles, especially in moving environments. In this study, fast and successful robotic behavioral prediction module has been developed to enable the autonomous vehicle to plan more safely and successfully.
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Feys, Manon, Evy Rombaut, and Lieselot Vanhaverbeke. "Does a Test Ride Influence Attitude towards Autonomous Vehicles? A Field Experiment with Pretest and Posttest Measurement." Sustainability 13, no. 10 (May 12, 2021): 5387. http://dx.doi.org/10.3390/su13105387.
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Autonomous vehicles have the potential to disrupt the mobility system. Therefore, it is important to understand attitude formation towards autonomous vehicles. The focus of this study is on the private user’s technology acceptance of an autonomous vehicle. The study applies the determinants of technology acceptance to capture users’ attitude towards and intention to adopt autonomous vehicles. A field experiment with 27 participants was conducted to assess changes in determinants before and after a test ride with a level 2 automated vehicle. The automated vehicle was equipped with technology that allowed a hands-off, feet-off experience on a public road in real traffic. The results show that a ride has a positive and significant effect on attitudes towards autonomous vehicles. Additionally, participants with higher ratings of technology anxiety show a remarkable increase in attitude towards autonomous vehicles after the ride compared to participants with lower levels of technology anxiety. These findings indicate that experience with a partially automated vehicle has a potentially positive effect on the acceptance of autonomous vehicles. As such, our study illustrates the importance of continuous pilot testing with private automated vehicles to increase future user acceptance of autonomous vehicles.
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Moreno, Ana T., Andrzej Michalski, Carlos Llorca, and Rolf Moeckel. "Shared Autonomous Vehicles Effect on Vehicle-Km Traveled and Average Trip Duration." Journal of Advanced Transportation 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/8969353.
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Intermediate modes of transport, such as shared vehicles or ride sharing, are starting to increase their market share at the expense of traditional modes of car, public transport, and taxi. In the advent of autonomous vehicles, single occupancy shared vehicles are expected to substitute at least in part private conventional vehicle trips. The objective of this paper is to estimate the impact of shared autonomous vehicles on average trip duration and vehicle-km traveled in a large metropolitan area. A stated preference online survey was designed to gather data on the willingness to use shared autonomous vehicles. Then, commute trips and home-based other trips were generated microscopically for a synthetic population in the greater Munich metropolitan area. Individuals who traveled by auto were selected to switch from a conventional vehicle to a shared autonomous vehicle subject to their willingness to use them. The effect of shared autonomous vehicles on urban mobility was assessed through traffic simulations in MATSim with a varying autonomous taxi fleet size. The results indicated that the total traveled distance increased by up to 8% after autonomous fleets were introduced. Current travel demand can still be satisfied with an acceptable waiting time when 10 conventional vehicles are replaced with 4 shared autonomous vehicles.
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Duan, Bo. "Sensor and sensor fusion technology in autonomous vehicles." Applied and Computational Engineering 52, no. 1 (March 27, 2024): 132–37. http://dx.doi.org/10.54254/2755-2721/52/20241470.
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The perception and navigation of autonomous vehicles heavily rely on the utilization of sensor technology and the integration of sensor fusion techniques, which play an essential role in ensuring a secure and proficient understanding of the vehicle's environment.This paper highlights the significance of sensors in autonomous vehicles and how sensor fusion techniques enhance their capabilities. Firstly, the paper introduces the different types of sensors commonly used in autonomous vehicles and explains their principles of operation, strengths, and limitations in capturing essential information about the vehicles environment. Next, the paper discusses various sensor fusion algorithms, such as Kalman filters and particle filters. Furthermore, the paper explores the challenges associated with sensor fusion and addresses the issue of handling sensor failures or uncertainties. The benefits of sensor fusion technology in autonomous vehicles are also presented. These include improved perception of the environment, enhanced object recognition and tracking, better trajectory planning, and enhanced safety through redundancy and fault tolerance. Lastly, the paper discusses the advancements and highlights the integration of artificial intelligence and machine learning techniques to optimize sensor fusion algorithms and improve the overall autonomy of the vehicle. Following thorough analysis, the deduction can be made that sensor and sensor fusion technology assume a critical function in facilitating efficient and secure autonomous vehicle navigation within intricate surroundings.
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Zhou, Yunya, Yang He, Yu Yan, Fang Li, Neng Li, Chaofeng Zhang, Zijian Lu, and Zhiyong Yang. "Autonomous charging docking control method for unmanned vehicles based on vision and infrared." Journal of Physics: Conference Series 2584, no. 1 (September 1, 2023): 012065. http://dx.doi.org/10.1088/1742-6596/2584/1/012065.
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Abstract Unmanned vehicle charging is part of the autonomous workflow of unmanned vehicles. Most existing unmanned vehicles mostly rely on manual battery change or manual charging, which cannot realize autonomous charging. In order to achieve simpler and safer autonomous charging for unmanned vehicles, this paper proposes a new intelligent unmanned vehicle autonomous charging docking method based on infrared guidance and vision assistance. Firstly, the autonomous charging device and intelligent charging stand for unmanned vehicles are designed, and for the unmanned vehicle, charging is not easy to align and easy to detach when charging. The camber-type electric core and charging adsorption device are designed, respectively, and the autonomous charging docking device is designed. Secondly, in order to ensure the accuracy of the docking between the unmanned vehicle and the intelligent charging stand, the unmanned vehicle autonomous charging method is proposed. The combination method of infrared and vision adjusts the posture of the unmanned vehicle. Finally, a protection method of autonomous charging docking based on ultrasonic ranging of unmanned vehicles is proposed. The communication and ranging modules on the unmanned vehicle and intelligent charging stand are designed to prevent mistouching and obstacle avoidance to ensure the safety of the system. The experiment results show that the docking method of this unmanned vehicle autonomous charging system is accurate, efficient, and safe, which can satisfy the demand for unmanned vehicle autonomous charging.
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Noorvand, Hossein, Guru Karnati, and B. Shane Underwood. "Autonomous Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2640, no. 1 (January 2017): 21–28. http://dx.doi.org/10.3141/2640-03.
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With ongoing technological improvements and research in the field of autonomous vehicles, it is becoming evident that the technology has the potential to substantially affect the transportation sector. Although the potential benefits with respect to productivity increases, cost decreases, and safety are evident, the potential for these vehicles to negatively or positively affect the transportation infrastructure is unclear. In this study, the influence of truck loadings positioning on the long-term performance of transportation infrastructure was estimated by carrying out performance simulations of pavement structures. Scenarios considering both full and partial use by autonomous trucks were considered. In all cases, performance was estimated with respect to rutting, fatigue cracking, and overall pavement smoothness, and the results were compiled in terms of reduced pavement thickness. It was found that if controlled appropriately, autonomous trucks could be highly beneficial for the pavement infrastructure design, and they would be most effective when they represented more than 50% of the total truck traffic. It was also found that in the absence of appropriate control, specifically by repeatedly positioning trucks in the same location, the amount of damage could be highly detrimental, and noticeable influences may occur at autonomous truck volumes as low as 10%.
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Sarmah, Deepangshu Dev. "Autonomous Vehicles." Auto Tech Review 4, no. 6 (June 2015): 1. http://dx.doi.org/10.1365/s40112-015-0921-x.
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Meyrowitz, A. L., D. R. Blidberg, and R. C. Michelson. "Autonomous vehicles." Proceedings of the IEEE 84, no. 8 (1996): 1147–64. http://dx.doi.org/10.1109/5.533960.
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Sharma, Preeti, Brijnandan Meena, and Vaibhav Lakhwat. "AUTONOMOUS VEHICLES." Journal of Analysis and Computations 17, no. 2 (December 1, 2023): 139–46. http://dx.doi.org/10.30696/jac.xvii.2.2023.139-146.
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Saber, O., and T. Mazri. "SECURITY OF AUTONOMOUS VEHICLES: 5G IOV (INTERNET OF VEHICLES) ENVIRONMENT." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W3-2022 (December 2, 2022): 157–63. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w3-2022-157-2022.
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Abstract. An autonomous vehicle is designed to move partially or totally without the intervention of the driver. It is a system equipped with sensors, communication and processing units that make it able to monitor and analyse traffic information in real time to improve road safety. In addition, Internet of Vehicles is the latest technology dedicated to autonomous vehicles, the integration of this technology with 5G serves as a platform to interconnect sensors, vehicles, infrastructure, pedestrian, and network. Hence, the 5G Internet of Vehicles environment provides significant benefits, including increased security, high reliability, wide communication coverage and low service latency. On the other hand, due to the ubiquity of network connectivity, it also presents serious confidentiality, integrity, and availability issues for autonomous vehicles. This paper provides an overview of the autonomous vehicle concept by highlighting its technologies and the 5G IoV environment, presenting some susceptible attacks that can touch the security of this environment, and some good practices to ensure the autonomous vehicle security.
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Alsulami, Abdulaziz A., Qasem Abu Al-Haija, Ali Alqahtani, and Raed Alsini. "Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model." Symmetry 14, no. 7 (July 15, 2022): 1450. http://dx.doi.org/10.3390/sym14071450.
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Technological advancement has transformed traditional vehicles into autonomous vehicles. Autonomous vehicles play an important role since they are considered an essential component of smart cities. The autonomous vehicle is an intelligent vehicle capable of maintaining safe driving by avoiding crashes caused by drivers. Unlike traditional vehicles, which are fully controlled and operated by humans, autonomous vehicles collect information about the outside environment using sensors to ensure safe navigation. Autonomous vehicles reduce environmental impact because they usually use electricity to operate instead of fossil fuel, thus decreasing the greenhouse gasses. However, autonomous vehicles could be threatened by cyberattacks, posing risks to human life. For example, researchers reported that Wi-Fi technology could be vulnerable to cyberattacks through Tesla and BMW autonomous vehicles. Therefore, further research is needed to detect cyberattacks targeting the control components of autonomous vehicles to mitigate their negative consequences. This research will contribute to the security of autonomous vehicles by detecting cyberattacks in the early stages. First, we inject False Data Injection (FDI) attacks into an autonomous vehicle simulation-based system developed by MathWorks. Inc. Second, we collect the dataset generated from the simulation model after integrating the cyberattack. Third, we implement an intelligent symmetrical anomaly detection method to identify false data cyber-attacks targeting the control system of autonomous vehicles through a compromised sensor. We utilize long short-term memory (LSTM) deep networks to detect False Data Injection (FDI) attacks in the early stage to ensure the stability of the operation of autonomous vehicles. Our method classifies the collected dataset into two classifications: normal and anomaly data. The experimental result shows that our proposed model’s accuracy is 99.95%. To this end, the proposed model outperforms other state-of-the-art models in the same study area.
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Zhao, Kun, and Jialun Yin. "ICOPP: A Highway Autonomous Driving Planner Based on Interactive Collaborative Optimization and POMDP." Journal of Physics: Conference Series 2868, no. 1 (October 1, 2024): 012003. http://dx.doi.org/10.1088/1742-6596/2868/1/012003.
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Abstract In this paper, we propose a planner for autonomous vehicles in complex highway scenarios, which can efficiently plan the trajectory of autonomous vehicles. The method aims to improve the intelligence of autonomous driving under the uncertainty of the perception and prediction system. The method employs a POMDP framework to address the challenges brought about by uncertainty. Firstly, this method divides the entire planning cycle into several discrete decision-making processes. In each process, the vehicle’s lateral and longitudinal behaviors are semantically processed to obtain a limited number of decision sequences, thereby addressing the curse of dimensionality in state space and observation space in POMDP problems. In each decision-making process, different simulation scenarios are constructed based on the decision of the self-vehicle and probability distribution of the predicting behaviors of surrounding vehicles. In each scenario, key vehicles are selected for collaborative optimal planning among multiple vehicles, resulting in the optimal drivable trajectories for both the ego vehicle and other vehicles under the given decision. Finally, an evaluation of the policy is conducted in terms of safety, comfort, and efficiency to select the optimal policy. In simulation experiments, the ICOPP demonstrates smoother and more stable autonomous driving performance compared to traditional methods.
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Zou, Shengquan, Dayong Han, Wei Wang, and Ran Cao. "Effect of Autonomous Vehicles on Fatigue Life of Orthotropic Steel Decks." Sensors 22, no. 23 (December 1, 2022): 9353. http://dx.doi.org/10.3390/s22239353.
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The fatigue life of orthotropic steel decks (OSDs) is significantly affected by vehicle loads, and the local stress response of OSDs is sensitive to the transverse position of vehicle loads. However, the presence of autonomous vehicles is likely to change the transverse distribution of vehicles within the lane, thereby affecting vehicle-induced fatigue damage to OSDs. Therefore, it is necessary to evaluate the potential effect of autonomous vehicles on the fatigue life of OSDs so that appropriate strategies can be implemented to control the transverse positions of autonomous vehicles passing the bridge deck. To this end, fatigue damages of several typical fatigue details in a conventional OSD (COSD) and a lightweight composite OSD (LWCD) induced by vehicle loads were calculated based on finite element analysis, and their fatigue lives were evaluated based on Miner’s Rule, in which different transverse distribution patterns of autonomous vehicles and their proportions in the mixed traffic flow were considered. The results indicate that fatigue lives of both the COSD and the LWCD can be negatively affected by autonomous vehicles traveling across the bridge without any constraints on the transverse distribution, especially when their proportion in the mixed traffic flow exceeds 30%. Compared to the scenario without autonomous vehicles, the fatigue damage of most fatigue details in OSDs may increase by 51% to 210% in the most unfavorable case due to the presence of autonomous vehicles. Nevertheless, it is feasible to extend the fatigue life of OSDs by optimizing the transverse distribution of autonomous vehicles. Specifically, the fatigue life of most fatigue details in the COSD could be extended by more than 86% in the most favorable case when a bimodal Gaussian distribution is adopted as the transverse distribution pattern of autonomous vehicles. Moreover, both the negative and positive effects of autonomous vehicles on the fatigue life of the COSD are more significant than those of the LWCD in most cases. The results can provide references for the maintenance of OSDs under the action of autonomous vehicles.
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Naz, Neelma, Muhammad Khurram Ehsan, Muhammad Rizwan Amirzada, Md Yeakub Ali, and Muhammad Aasim Qureshi. "Intelligence of Autonomous Vehicles: A Concise Revisit." Journal of Sensors 2022 (April 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/2690164.
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Artificial intelligence- (AI-) empowered machines are devised to mimic human actions. In the automotive industry, AI plays a significant role in the development of vehicular technology. AI joins hands with the field of mechatronics to assist in the accurate execution of the vehicle functionalities. Autonomous vehicles get the scene information by using onboard sensors such as laser, radar, lidar, Global Positioning System (GPS), and vehicular communication networks. The data obtained is then used for various path planning and control techniques to make the vehicles capable of autonomously driving in complex environments. Autonomous vehicles use very up-to-date AI algorithms to localize themselves in known and unknown environments. AI algorithms are also exploited for perception, path planning, and motion control. A concise review of the state-of-the-art techniques to improve the performance of autonomous vehicles is presented.
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Rosemadi, Julia Farhana, Dennis W. K. Khong, and Gita Radhakrishna. "CIVIL LIABILITY OF AUTONOMOUS VEHICLES: A REVIEW OF LITERATURE." IIUM Law Journal 30, no. 2 (December 30, 2022): 155–79. http://dx.doi.org/10.31436/iiumlj.v30i2.740.
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Autonomous vehicles are seen as a recent trend that has brought about changes in conventional driving. Although autonomous vehicles may bring a significant increase in safety, mobility, and productivity, it is not entirely fool proof and there can still be unfortunate incidents. In a normal car, the driver oversees the vehicle and may be liable both criminally and civilly if he fails to control his vehicle. However, in cases involving autonomous vehicles, the driver alone may not be in control of the vehicle; instead, the autonomous vehicle software navigates the car. This paper adopts a doctrinal methodology to conduct an issue-based literature review pertaining to civil liability issues for road accidents involving autonomous vehicles. The issues identified are: liability, the standard of liability, the safety regulation of autonomous vehicles, and insurance as the compensation mechanism for accidents. The review helps to organise the literature according to the themes and to derive lessons learnt from the literature.
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Naeem, Awad Bin, Biswaranjan Senapati, Md Sakiul Islam Sudman, Kashif Bashir, and Ayman E. M. Ahmed. "Intelligent Road Management System for Autonomous, Non-Autonomous, and VIP Vehicles." World Electric Vehicle Journal 14, no. 9 (September 1, 2023): 238. http://dx.doi.org/10.3390/wevj14090238.
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Currently, autonomous vehicles, non-autonomous vehicles, and VIP (emergency) autonomous cars are using intelligent road management techniques to interact with one another and enhance the effectiveness of the traffic system. All sorts of vehicles are managed and under control using the intersection management unit approach. This study focuses on transportation networks where VIP cars are a major disruption, accounting for 40% of accidents and 80% of delays. Intelligent Mobility (IM) is a strategy promoted in this study that proposes setting up intelligent channels for all vehicle communication. As part of its function, the IM unit keeps tabs on how often each junction is used so that it may notify drivers on traffic conditions and ease their workload. The suggested layout may drastically cut average wait times at crossings, as shown in SUMO simulations. The entrance of a VIP car should disrupt all traffic, but the IM (intersection management) unit effectively manages all traffic by employing preemptive scheduling and non-preemptive scheduling techniques for all types of vehicles. We are employing Nishtar roads, the M4 motorway, Mexico, and Washington roads in our scenario. In comparison to all other routes, the simulation results demonstrate that the Washington road route is better able to manage all vehicle kinds. Washington’s traffic delays for 50 cars of all sorts are 4.02 s for autonomous vehicles, 3.62 s for VIP autonomous vehicles, and 4.33 s for non-autonomous vehicles.
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Helmut Pflugfelder, Ehren. "Autonomous Vehicles and Gender." Transfers 8, no. 1 (March 1, 2018): 104–11. http://dx.doi.org/10.3167/trans.2018.080108.
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Several recent surveys report a gap between how men and women feel about autonomous vehicles. While such binaries may have limited usefulness, female respondents rank autonomous technology as less trustworthy and are less likely than men to report feeling safe in an autonomous car. This comment frames such results within the articles for this special section on autonomous vehicles, showing how reported gender divisions are resultant from discursive formations that frame user experience and individual performed experiences. These discursive-material dynamics generate persuasive configurations of power that thoughtful research and action in autonomous vehicle development could help mitigate. After summarizing survey diff erences, this comment off ers a brief commentary on how they might be addressed, focusing on material rhetoric and vehicle design.
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Tanveer, Muhammad, Faizan Ahmad Kashmiri, Huimin Yan, Tianshi Wang, and Huapu Lu. "A Cellular Automata Model for Heterogeneous Traffic Flow Incorporating Micro Autonomous Vehicles." Journal of Advanced Transportation 2022 (January 28, 2022): 1–21. http://dx.doi.org/10.1155/2022/8815026.
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Despite the fact that significant research efforts have been made to the traffic flow theory of autonomous vehicles and manual vehicles, few existing studies have incorporated different modes of both vehicles in their analysis. In this study, we develop a cellular automata simulation model to investigate the impact of different modes of autonomous vehicles (autonomous car, autonomous bus, and autonomous micro car) and conventional vehicles (manual car, manual bus, and manual micro car) on the characteristics of traffic flow. A new type of autonomous mode, i.e., autonomous micro car, is investigated in the model to study the effects of this vehicle mode on the overall capacity of the network. Furthermore, two types of lane-changing behavior, i.e., aggressive lane changing and polite lane changing, are incorporated into the model. The results reveal that micro cars (manual and autonomous) have the potential to reduce traffic congestions and increase the capacity or flow rate (vehicles/hour) of the road. Where the average vehicle occupancy is less than 2, if autonomous micro cars are deployed alongside autonomous cars, the flow rate (vehicles/hour) can be increased significantly. The results highlight the significance of the autonomous micro cars to traffic flow, passenger occupancy, and road capacity.
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Li, Xiaohu, Zehong Cao, and Quan Bai. "A Novel Mountain Driving Unity Simulated Environment for Autonomous Vehicles." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 16075–77. http://dx.doi.org/10.1609/aaai.v35i18.18016.
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The simulated driving environment provides a low cost and time-saving platform to test the performance of the autonomous vehicle by linkage with existing machine learning approaches. However, most of existing simulated driving environments focus on building flat roads in urban areas. Still, they neglected to endeavour the tough steep, curvy hill roads, such as mountain paths around suburban areas. In this study, by deploying in Unity engine, we developed the first complex mountain driving simulated environment with characterizing continuous curves and up/downhill. Then, two state-of-art reinforcement learning (RL) algorithms are used to train a vehicle agent and test the performance of autonomous vehicles in our developed simulated environment. Also, we set 5 different levels of vehicle's speeds and observe the cumulative rewards during the vehicle agent training. Our demonstration presents the developed environment supports for complex mountain scenario configurations and RL-based autonomous vehicles, and our findings show that the vehicle agent could achieve high cumulative rewards during the training stage, suggesting that our work is a potential new simulation environment for autonomous vehicles research. The demonstration video can be viewed via the link: https://youtu.be/0wSqGeCn-NU.
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Mega Permatasari, Shervind Maharani, Bambang Lelono Widjiantoro, and Katherin Indriawati. "Design of Predictive Control System for Lane Change in Autonomous Vehicle." International Journal of Artificial Intelligence & Robotics (IJAIR) 6, no. 1 (May 31, 2024): 8–18. http://dx.doi.org/10.25139/ijair.v6i1.8141.
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The automobile business is introducing a lot of autonomous vehicles in the modern day. Lane changes are one of the most complicated urban scenarios in which autonomous vehicles are used. Self-driving automobiles must thus interact with human-driven vehicles in a certain way. In this work, we concentrate on the autonomous vehicle's lane-changing control system for obstacle avoidance. This study employs a predictive control system as its methodology. The vehicle's next movements can be predicted by this control system. The vehicle's position, which is adjusted by the steering angle, is the controllable variable. The vehicle's position, which is adjusted by the steering angle, is the controllable variable. It is clear from the numerical simulation results that the predictive control system executes control actions on lane changes correctly, avoiding collisions with the running vehicle obstacles. RMSE (Root-Mean Square Error) is a performance metric that is derived from the difference between the vehicle's lateral position and the reference trajectory value. The RMSE of the planned predictive control is 0.9681.
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Wu, Biao, Zhixiong Ma, Xichan Zhu, and Yu Lin. "Research on the Vehicle-Behavior Boundary of Intersection Traffic Based on Naturalistic Driving Data Study." Applied Sciences 14, no. 8 (April 18, 2024): 3432. http://dx.doi.org/10.3390/app14083432.
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With the development and application of vehicle-infrastructure cooperative technology, the traffic regional safety related to intelligent connected vehicles (ICVs) has become the hotspot of the intelligent transportation system (ITS), and the integration of mixed autonomous and non-autonomous vehicles that are not cooperative in intersection areas has become a significant challenge due to the rapid advancement of autonomous vehicle technology. Autonomous vehicles in intersections with strong-structure and weak-rule characteristics pose a potential hazard in complex traffic situations. Studying the driving behavior of vehicles in intersections is of great significance due to the complex traffic environment, frequent traffic signals, and traffic violations, which can optimize the vehicle driving behavior and improve the safety and efficiency of intersection traffic. By using naturalistic driving data from the DAIR V2X-Seq dataset and general vehicle dynamic parameters, it is possible to obtain the joint-probability-density distribution of the bivariate dynamic parameters of a vehicle. This distribution represents the driving characteristics of vehicles in intersection traffic. The three vehicle dynamic parameters that have an impact on vehicles driving through the intersection area are velocity, angular velocity, and acceleration. The driving behavior characteristics of human-driven vehicles (HVs) and autonomous vehicles (AVs) were analyzed using the multivariate kernel density estimation (MKDE) method to establish the vehicle-behavior boundary. The assessment of the boundary model showed that it accurately characterizes the driving characteristics of HVs and AVs. This boundary can be used to improve the safety detection of intersection areas, enhancing the performance of autonomous vehicles and optimizing intersection traffic.
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Thieme, Christoph Alexander, and Ingrid Bouwer Utne. "A risk model for autonomous marine systems and operation focusing on human–autonomy collaboration." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 4 (August 2017): 446–64. http://dx.doi.org/10.1177/1748006x17709377.
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Autonomous marine systems, such as autonomous ships and autonomous underwater vehicles, gain increased interest in industry and academia. Expected benefits of autonomous marine system in comparison to conventional marine systems are reduced cost, reduced risk to operators, and increased efficiency of such systems. Autonomous underwater vehicles are applied in scientific, commercial, and military applications for surveys and inspections of the sea floor, the water column, marine structures, and objects of interest. Autonomous underwater vehicles are costly vehicles and may carry expensive payloads. Hence, risk models are needed to assess the mission success before a mission and adapt the mission plan if necessary. The operators prepare and interact with autonomous underwater vehicles to carry out a mission successfully. Risk models need to reflect these interactions. This article presents a Bayesian belief network to assess the human–autonomy collaboration performance, as part of a risk model for autonomous underwater vehicle operation. Human–autonomy collaboration represents the joint performance of the human operators in conjunction with an autonomous system to achieve a mission aim. A case study shows that the human–autonomy collaboration can be improved in two ways: (1) through better training and inclusion of experienced operators and (2) through improved reliability of autonomous functions and situation awareness of vehicles. It is believed that the human–autonomy collaboration Bayesian belief network can improve autonomous underwater vehicle design and autonomous underwater vehicle operations by clarifying relationships between technical, human, and organizational factors and their influence on mission risk. The article focuses on autonomous underwater vehicle, but the results should be applicable to other types of autonomous marine systems.
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Geng, Dongliang, Meng Wang, Zhiqiang Zhang, and Juan Shi. "Research on visual sensor occlusion test tools for autonomous vehicles." Journal of Physics: Conference Series 2902, no. 1 (November 1, 2024): 012008. https://doi.org/10.1088/1742-6596/2902/1/012008.
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Abstract In the research on the safety of intelligent driving vehicles, it is essential to test and verify the safety redundancy characteristics of the vehicle’s autonomous driving system. The safety redundancy capability of autonomous vehicles comes from the redundancy at the vehicle control level on the one hand, and the redundancy of environmental recognition sensors on the other. Among them, the camera sensors of the vehicle are generally distributed at multiple points inside and outside the vehicle to identify the external environment of the vehicle and the status of the driver in the vehicle. When some camera sensors on the outside of the vehicle are blocked while the vehicle is moving, the autonomous driving system should have a strategy to deal with it to avoid danger. For the verification of the above test scenarios, it is necessary to use the visual sensor occlusion tool to complete. In this paper, how the designed exterior vision sensor occlusion tool works normally under emergency braking conditions is studied.
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Pisarov, Jelena, and Gyula Mester. "The future of autonomous vehicles." FME Transactions 49, no. 1 (2021): 29–35. http://dx.doi.org/10.5937/fme2101029p.
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The future of the modern world faces the appearance of different ways of mobility. Huge strive in today's world have gained autonomous vehicles. The paper explains how autonomous vehicles function as well as their advantages and disadvantages. New developments in autonomous vehicles are being accomplished and introduced to the user's demands. Many car companies have developed their own driverless vehicles and detected the problems within them. The major flaw of autonomous vehicles is cyber security because hackers are still able to break into the car's software system and disrupt it. This is a major issue which is still being dealt with. Autonomous vehicles have modernized the mobility of people, which means that people no longer have to come to the vehicle but the vehicle comes to them and are able to share transportation and thus lowering the traffic congestion and cost. Smartphone applications have been developed facilitating the carsharing system. Users consider these cars comfortable and stylish but expect high level of security. Autonomous vehicles enable the elderly, the disabled and physically limited people move much easier. Furthermore, autonomous vehicles reduce pollution and are environmentally friendly. It is anticipated that autonomous vehicles will take over the roads and are the future of transportation. They offer comfort, safety and good driving conditions. Hereafter, this paper represents important characteristics and features of autonomous cars.
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G, Sathisha, C. K. Subbaraya, and Ravikumar G K. "Automated Speed and Lane Change decision-making Model using Support Vector Machine." International Journal of Communication Networks and Information Security (IJCNIS) 15, no. 3 (December 4, 2023): 42–56. http://dx.doi.org/10.17762/ijcnis.v15i3.6222.
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One of the major obstacles that the auto industry must overcome is the rise of autonomous vehicles. The study of lane-changing is an important part of this problem. Previous studies on autonomous vehicle lane changes have predominantly focused on lane change path planning and path monitoring, with limited attention given to the autonomous vehicle's lane change decision-making process. This paper introduces a novel Lane Change Decision-Making Model for autonomous vehicles using the Support Vector Machine (SVM) method. The suggested model employs real-time sensor data to assess whether or not a lane change is possible, taking into account the proximity of other vehicles (cars, buses, motorbikes), and adjusting speed as necessary to ensure a seamless transition. Researching the various facets of lane changes in autonomous vehicles allows for decision-making that is grounded in utility, safety, and tolerance. The implementation of a support vector machine (SVM) technique with Bayesian parameter optimization is used to deal with the non-linearity and complexity of the process of autonomous lane change decision-making. Finally, we compare the suggested SVM model against a rule-based lane change model using the test data. The SVM-based strategy is shown to improve lane change decision-making in a comprehensive simulation exercise, which in turn improves the safety and efficiency of autonomous driving systems. The experiment also use a real vehicle to gauge the efficacy of the underlying decision-making model.
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Yaakub, Salma, and Mohammed Hayyan Alsibai. "A Review on Autonomous Driving Systems." International Journal of Engineering Technology and Sciences 5, no. 1 (June 20, 2018): 1–16. http://dx.doi.org/10.15282/ijets.v5i1.2800.
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Autonomous vehicles are one of the promising solutions to reduce traffic crashes and improve mobility and traffic system. An autonomous vehicle is preferable because it helps in reducing the need for redesigning the infrastructure and because it improves the vehicle power efficiency in terms of cost and time taken to reach the destination. Autonomous vehicles can be divided into 3 types: Aerial vehicles, ground vehicles and underwater vehicles. General, four basic subsystems are integrated to enable a vehicle to move by itself which are: Position identifying and navigation system, surrounding environment situation analysis system, motion planning system and trajectory control system. In this paper, a review on autonomous vehicles and their related technological applications is presented to highlight the aspects of this industry as a part of industry 4.0 concept. Moreover, the paper discusses the best autonomous driving systems to be applied on our wheelchair project which aims at converting a manual wheelchair to a smart electric wheelchair
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Seo, Hyoduck, Kyesan Lee, and Kyujin Lee. "Investigating the Improvement of Autonomous Vehicle Performance through the Integration of Multi-Sensor Dynamic Mapping Techniques." Sensors 23, no. 5 (February 21, 2023): 2369. http://dx.doi.org/10.3390/s23052369.
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The emergence of autonomous vehicles marks a shift in mobility. Conventional vehicles have been designed to prioritize the safety of drivers and passengers and increase fuel efficiency, while autonomous vehicles are developing as convergence technologies with a focus on more than just transportation. With the potential for autonomous vehicles to serve as an office or leisure space, the accuracy and stability of their driving technology is of utmost importance. However, commercializing autonomous vehicles has been challenging due to the limitations of current technology. This paper proposes a method to build a precision map for multi-sensor-based autonomous driving to improve the accuracy and stability of autonomous vehicle technology. The proposed method leverages dynamic high-definition maps to enhance the recognition rates and autonomous driving path recognition of objects in the vicinity of the vehicle, utilizing multiple sensors such as cameras, LIDAR, and RADAR. The goal is to improve the accuracy and stability of autonomous driving technology.
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Tranter, Kieran Mark. "The Challenges of Autonomous Motor Vehicles for Queensland Road and Criminal Laws." QUT Law Review 16, no. 2 (June 17, 2016): 59. http://dx.doi.org/10.5204/qutlr.v16i2.626.
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<p><em>This article examines the challenges of autonomous motor vehicles for Queensland road and criminal laws</em>. <em>Autonomous vehicles refer to motor vehicles where driver decision making has been augmented or replaced by intelligent systems. Proponents of autonomous vehicles argue that they will virtually eliminate road accidents, boost productivity and provide significant environmental benefits. The key issue is that autonomous vehicles challenge the notion of human responsibility which lies at the core of Queensland’s road and criminal laws. The road rules are predicated on a driver in control of the vehicle, the intoxication regime is concerned with the person in charge of the vehicle and the dangerous driving offences require a person who operates a vehicle. Notwithstanding this challenge, it can be seen that much of Queensland’s law is adaptable to autonomous vehicles. However, there are some identifiable anomalies that require reform.</em></p>
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Rojas-Rueda, David, Mark J. Nieuwenhuijsen, Haneen Khreis, and Howard Frumkin. "Autonomous Vehicles and Public Health." Annual Review of Public Health 41, no. 1 (April 2, 2020): 329–45. http://dx.doi.org/10.1146/annurev-publhealth-040119-094035.
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Autonomous vehicles (AVs) have the potential to shape urban life and significantly modify travel behaviors. “Autonomous technology” means technology that can drive a vehicle without active physical control or monitoring by a human operator. The first AV fleets are already in service in US cities. AVs offer a variety of automation, vehicle ownership, and vehicle use options. AVs could increase some health risks (such as air pollution, noise, and sedentarism); however, if proper regulated, AVs will likely reduce morbidity and mortality from motor vehicle crashes and may help reshape cities to promote healthy urban environments. Healthy models of AV use include fully electric vehicles in a system of ridesharing and ridesplitting. Public health will benefit if proper policies and regulatory frameworks are implemented before the complete introduction of AVs into the market.
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Chen, Hong Yun, Yan Qiang Li, Zi Hui Zhang, and Yong Wang. "Test Method for Decision Planning of Autonomous Vehicles Based on DQN Algorithm." E3S Web of Conferences 253 (2021): 03022. http://dx.doi.org/10.1051/e3sconf/202125303022.
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In February 2020, Beijing, China andCalifornia, USA respectively released road test reports of 2019 for autonomous vehicles. Beijing and California respectively represent the highest level of testing and application of autonomous vehicles in the two countries. This article will compare the test items, evaluation criteria and technical defects of each autonomous vehicle company in the road test reports of China and the United States, also analyze the existing problems, and propose an idea for the construction of a comprehensive test site for autonomous vehicles. This article aims to solve the prominently exposed problems in decision-making and planning in autonomous vehicles with DQN algorithm-base vehicle fleet, and to look forward to the future development trend of autonomous driving testing.
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MRITHIP, Mr R., Mr T. MEIDHARSHANSRI, Mr K. ROHIT, Mr M. SACHIN, and Mrs M. REVATHI. "DRIVERLESS TRACTOR." International Scientific Journal of Engineering and Management 04, no. 01 (January 19, 2025): 1–6. https://doi.org/10.55041/isjem02225.
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Driverless Tractor into agriculture.The field of autonomous automation is of interest to researchers, and much has been accomplished in this area, of which this paper presents a detailed chronology. This paper can help one understand the trends in autonomous vehicle technology for the past, present, and future. We see a drastic change in autonomous vehicle technology since 1920s, when the first radio controlled vehicles were designed. In the subsequent decades, we see fairly autonomous electric cars powered by embedded circuits in the roads. By 1960s, autonomous cars having similar electronic guide systems came into picture. 1980s saw vision guided autonomous vehicles, which was a major milestone in technology and till date we use similar or modified forms of vision and radio guided technologies. Various semi-autonomous features introduced in modern cars such as lane keeping, automatic braking and adaptive cruise control are based on such systems. Extensive network guided systems in conjunction with vision guided features is the future of autonomous vehicles. It is predicted that most companies will launch fully autonomous vehicles by the advent of next decade. The future of autonomous vehicles is an ambitious era of safe and comfortable transportation. KEY WORDS: autonomous vehicles,adaptive cruise control,embedded circuits.
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Ham, Sungil, Junhyuck Im, Minjun Kim, and Kuk Cho. "Construction and Verification of a High-Precision Base Map for an Autonomous Vehicle Monitoring System." ISPRS International Journal of Geo-Information 8, no. 11 (November 6, 2019): 501. http://dx.doi.org/10.3390/ijgi8110501.
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For autonomous driving, a control system that supports precise road maps is required to monitor the operation status of autonomous vehicles in the research stage. Such a system is also required for research related to automobile engineering, sensors, and artificial intelligence. The design of Google Maps and other map services is limited to the provision of map support at 20 levels of high-resolution precision. An ideal map should include information on roads, autonomous vehicles, and Internet of Things (IOT) facilities that support autonomous driving. The aim of this study was to design a map suitable for the control of autonomous vehicles in Gyeonggi Province in Korea. This work was part of the project “Building a Testbed for Pilot Operations of Autonomous Vehicles”. The map design scheme was redesigned for an autonomous vehicle control system based on the “Easy Map” developed by the National Geography Center, which provides free design schema. In addition, a vector-based precision map, including roads, sidewalks, and road markings, was produced to provide content suitable for 20 levels. A hybrid map that combines the vector layer of the road and an unmanned aerial vehicle (UAV) orthographic map was designed to facilitate vehicle identification. A control system that can display vehicle and sensor information based on the designed map was developed, and an environment to monitor the operation of autonomous vehicles was established. Finally, the high-precision map was verified through an accuracy test and driving data from autonomous vehicles.
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Liu, Zongwei, Hao Jiang, Hong Tan, and Fuquan Zhao. "An Overview of the Latest Progress and Core Challenge of Autonomous Vehicle Technologies." MATEC Web of Conferences 308 (2020): 06002. http://dx.doi.org/10.1051/matecconf/202030806002.
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The mass production of autonomous vehicle is coming, thanks to the rapid progress of autonomous driving technology, especially the recent breakthroughs in LiDAR sensors, GPUs, and deep learning. Many automotive and IT companies represented by Waymo and GM are constantly promoting their advanced autonomous vehicles to hit public roads as early as possible. This paper systematically reviews the latest development and future trend of the autonomous vehicle technologies, discusses the extensive application of AI in ICV, and identifies the key problems and core challenges facing the commercialization of autonomous vehicle. Based on the review, it forecasts the prospects and conditions of autonomous vehicle’s mass production and points out the arduous, long-term and systematic nature of its development.
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Vida, Gábor, and Péter Váradi. "Irregular operation of autonomous vehicles." Production Engineering Archives 20, no. 20 (September 1, 2018): 8–11. http://dx.doi.org/10.30657/pea.2018.20.02.
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Abstract Today, with the spread of autonomous functions in vehicles, the role accountability for causing accidents is emphasized. Self-guided functions work in certain traffic situations, but accidents happen, and, therefore, the following article presents an analysis of the issue. Its purpose is to show that vehicles with self-drive functionality do not provide the driver's level of safety that vehicle manufacturers suggest. In this article, four recent events and an analysis whether these accidents could have been avoided a human driver or how they could have happened with appropriate self-drive function. In each of the investigated cases, vehicles equipped with self-drive function are involved. Based on the evaluation and assessment of accidents, conclusions are drawn whether current self-propelled vehicles provide the safety level that drivers and society expect from these vehicles. The reconstruction of the accident process is illustrated with the help of a vehicle simulation program, with the resultant parameters being given a special emphasis, in particular to the avoidance of the accident.
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Koul, Sahil, and Ali Eydgahi. "The Impact of Social Influence, Technophobia, and Perceived Safety on Autonomous Vehicle Technology Adoption." Periodica Polytechnica Transportation Engineering 48, no. 2 (April 16, 2019): 133–42. http://dx.doi.org/10.3311/pptr.11332.
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The objective of this study was to determine whether there was a relationship between social influence, technophobia, perceived safety of autonomous vehicle technology, number of automobile-related accidents and the intention to use autonomous vehicles. The methodology was a descriptive, cross-sectional, correlational study. Theory of Planned Behavior provided the underlying theoretical framework. An online survey was the primary method of data collection. Pearson’s correlation and multiple linear regression were used for data analysis. This study found that both social influence and perceived safety of autonomous vehicle technology had significant, positive relationships with the intention to use autonomous vehicles. Additionally, a significant negative relationship was found among technophobia and intention to use autonomous vehicles. However, no relationship was found between the number of automobile-related accidents and intention to use autonomous vehicles. This study presents several original and significant findings as a contribution to the literature on autonomous vehicle technology adoption and proposes new dimensions of future research within this emerging field.
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Hong, Won-Ki. "The Effect of Consumer Lifestyle on the Acceptance Intention of Fully Autonomous Vehicles: Focusing on the Mediating Effect of Safety." Korea International Trade Research Institute 19, no. 5 (October 31, 2023): 127–45. http://dx.doi.org/10.16980/jitc.19.5.202310.127.
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Purpose - This research aims to analyze the effect of consumer lifestyle on the acceptance intention of fully autonomous vehicles, and the mediating effect of the safety of fully autonomous vehicles as the commercialization of fully autonomous vehicles approaches.
Design/Methodology/Approach - This research consisted of an online survey of 353 male and female adults in their 20s and order. The main variables are lifestyle, acceptance intention, and safety. To analyze the influence relationship, regression analysis was performed through SPSS, and mediation effect analysis was performed using Process Macro Model 4.
Findings - Consumer lifestyle affects fully autonomous vehicle acceptance intention and safety, and it has been proven that the safety of a fully autonomous vehicle has a mediating effect on the effect of consumer lifestyle on the intention to accept the fully autonomous vehicle.
Research Implications - First, the results proved that lifestyle is a new key variable in research on fully autonomous vehicles. Second, since safety has a mediating effect in the relationship between lifestyle and intention to accept fully autonomous vehicles, it was confirmed that safety is also an important factor. Third, in order to increase willingness to accept fully autonomous vehicles, it is necessary to segment the market based on consumer lifestyle above all else, and to develop a marketing strategy tailored to the characteristics of each lifestyle. Forth, in order to increase consumer awareness of the safety of fully autonomous vehicles, it is necessary to provide consumers with experiences based on Augmented Reality and XR technologies.
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Lisowski, Józef. "A Synthesis of Algorithms Determining a Safe Trajectory in a Group of Autonomous Vehicles Using a Sequential Game and Neural Network." Electronics 12, no. 5 (March 4, 2023): 1236. http://dx.doi.org/10.3390/electronics12051236.
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This paper presents a solution to the problem of providing an autonomous vehicle with a safe control task when moving around many other autonomous vehicles. This is achieved by developing an appropriate computer control algorithm that takes into account the possible risk of a collision resulting from both the impact of environmental disturbances and the imperfection of the rules of maneuvering in situations where many vehicles pass each other, giving the control process a decisive character. For this purpose, three types of algorithms were synthesized: kinematic and dynamic optimization with neural domains, as well as sequential game control of an autonomous vehicle. The control algorithms determine a safe trajectory, which is implemented by the actuators of the autonomous vehicle. Computer simulations of the control algorithms in the Matlab/Simulink software allow for their comparative analysis in terms of meeting the criteria for the optimality and safety of an autonomous vehicle when passing a larger number of other autonomous vehicles. For this purpose, scenarios of multidirectional and one-way traffic of autonomous vehicles were used.
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Balkmar, Dag, and Ulf Mellström. "Masculinity and Autonomous Vehicles." Transfers 8, no. 1 (March 1, 2018): 44–63. http://dx.doi.org/10.3167/trans.2018.080105.
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This article addresses the anthropomorphization and interpellative experience of cars and trucks, in order to meet future mobility challenges. Autonomous vehicles offer an emancipatory opportunity within a wider movement of degendering and regendering motor vehicles. We argue that autonomous vehicles can challenge the foundations of a gendered economy founded on masculinity, speed, pleasure, and embodiment. Rather than thinking in terms of a process of demasculinization, this article anticipates a regendering and resegregation through which certain forms of masculine gendered economies of pleasure will lose ground and others will gain. A core question in this article asks who will be in the driver’s seat of future systems of automobility as the control of the vehicle is gradually being transferred from the driver to digital control systems and intelligent roads.
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Almusawi, Ali, Mustafa Albdairi, and Syed Shah Sultan Mohiuddin Qadri. "Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control." Applied Sciences 14, no. 19 (October 1, 2024): 8851. http://dx.doi.org/10.3390/app14198851.
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The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors—cautious, normal, aggressive, and platooning—affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability.