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Artículos de revistas sobre el tema "Autonomous vehicles"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

Liu, Shuangrui, Hao Yang, and Lizao Wang. "Research on autonomous drift control for vehicles." Journal of Physics: Conference Series 3019, no. 1 (2025): 012066. https://doi.org/10.1088/1742-6596/3019/1/012066.

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Abstract The stability control of vehicle movement directly affects the safety and reliability of autonomous vehicles. When a vehicle is in extreme conditions, it is easy to lose stability and lead to accidents. Based on the drift control of racing vehicles under extreme conditions, this paper studies the emergency rescue control strategy for autonomous driving vehicles under extreme conditions. An algorithm similar to the drift control ability of racing drivers is designed. By predicting the vehicle’s driving trajectory and combining automatic steering, clutch, and throttle technologies, the

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2

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 (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

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3

PRATA, Tiago, Miguel MIRA DA SILVA, Flávia SANTORO, and António REIS PEREIRA. "ASSESSING THE DEVELOPMENT OF AUTONOMOUS CARS." Transport Problems 19, no. 1 (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 regar

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4

Kim, JongBae. "Deep Learning-Based Vehicle Type and Color Classification to Support Safe Autonomous Driving." Applied Sciences 14, no. 4 (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

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5

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 (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 researc

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6

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 (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 av

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7

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 (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

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8

Raiyn, Jamal. "Data and Cyber Security in Autonomous Vehicle Networks." Transport and Telecommunication Journal 19, no. 4 (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 suscept

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9

Gao, Kai, Di Yan, Fan Yang, et al. "Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario." Sensors 19, no. 19 (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 consump

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10

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 (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 sever

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11

Maeng, Joon Young. "Autonomous Vehicle and Civil Liability Standard ―Legalization of Autonomous Driving and Evaluation Thereof―." Korean Association of Civil Law 110 (March 31, 2025): 407–48. https://doi.org/10.52554/kjcl.2025.110.407.

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With the introduction of autonomous vehicles, various aspects of autonomous driving are being legislated in laws and subordinate rules in our legal system. Motor Vehicle Management Act broadly defines autonomous vehicles, while the Road Traffic Act broadly divides autonomous driving systems into fully autonomous driving systems and partial autonomous driving systems. Regarding the autonomous driving stage, autonomous vehicles are defined as partially autonomous vehicles and fully autonomous vehicles, and in the subordinate rule of the Motor Vehicle Management Act, autonomous driving systems is

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12

Vu, Trieu Minh, Reza Moezzi, Jindrich Cyrus, and Jaroslav Hlava. "Model Predictive Control for Autonomous Driving Vehicles." Electronics 10, no. 21 (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 subj

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13

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 (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 auton

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14

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 (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

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15

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 (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 th

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16

Pagale, Madhuri, Rahul Sharma, and Anuradha Thakare. "A review for autonomous vehicles technologies." Multiagent and Grid Systems: An International Journal of Data Science and Artificial Intelligence 20, no. 3-4 (2024): 245–64. https://doi.org/10.1177/15741702241296428.

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Nowadays, scientific developments in the vehicle and infrastructure automation sectors are developing steadily to benefit end users in ways like reducing traffic congestion, enhancing safety, facilitating stress-free travel, reducing fuel costs, and enabling smart parking. Autonomous vehicles sometimes known as self-driving cars, can entirely replace conventional automobiles to accomplish safe and reliable intelligent transportation systems (ITS). Autonomous vehicles have improved sensing capabilities and can drive on roads that would be difficult for humans. Software, technology, and people,

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17

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 a

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18

Zhou, Yunya, Yang He, Yu Yan, et al. "Autonomous charging docking control method for unmanned vehicles based on vision and infrared." Journal of Physics: Conference Series 2584, no. 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 eas

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19

Duan, Bo. "Sensor and sensor fusion technology in autonomous vehicles." Applied and Computational Engineering 52, no. 1 (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

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20

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 (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

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21

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, inclu

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22

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 (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 limi

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23

Zou, Shengquan, Dayong Han, Wei Wang, and Ran Cao. "Effect of Autonomous Vehicles on Fatigue Life of Orthotropic Steel Decks." Sensors 22, no. 23 (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 t

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24

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 (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 doctr

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25

Noorvand, Hossein, Guru Karnati, and B. Shane Underwood. "Autonomous Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2640, no. 1 (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 simulatio

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26

Sarmah, Deepangshu Dev. "Autonomous Vehicles." Auto Tech Review 4, no. 6 (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 (2023): 139–46. http://dx.doi.org/10.30696/jac.xvii.2.2023.139-146.

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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 driv

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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 investi

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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 (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.

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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 (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

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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 (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 reinfo

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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 (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 paylo

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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 (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 contr

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Helmut Pflugfelder, Ehren. "Autonomous Vehicles and Gender." Transfers 8, no. 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 o

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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 (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

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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

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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 (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 m

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40

Tranter, Kieran Mark. "The Challenges of Autonomous Motor Vehicles for Queensland Road and Criminal Laws." QUT Law Review 16, no. 2 (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

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41

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 (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

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42

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 (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 veh

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43

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 p

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44

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 (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

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45

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 (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

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46

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 commercializati

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47

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 (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

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48

Rojas-Rueda, David, Mark J. Nieuwenhuijsen, Haneen Khreis, and Howard Frumkin. "Autonomous Vehicles and Public Health." Annual Review of Public Health 41, no. 1 (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 pr

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49

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 (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 us

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50

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 (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 n

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