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Relevant bibliographies by topics / Autonomous vehicles

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Academic literature on the topic 'Autonomous vehicles'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2025

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Journal articles on the topic "Autonomous vehicles"

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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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Dissertations / Theses on the topic "Autonomous vehicles"

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Manuzzi, Nicolas. "Autonomous Vehicle and Internet on Vehicles." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9211/.

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Pérez, Tellez Adriel, and Jonas Roth. "Mobile autonomous ground vehicles." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199348.

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Arutselvan, Kuralamudhan. "Assistive Autonomous Ground Vehicles." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200530.

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Anderson, Jonathan D. "Semi Autonomous Vehicle Intelligence: Real Time Target Tracking For Vision Guided Autonomous Vehicles." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1750.pdf.

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Rahman, Md Mahbubar. "Two-Echelon Vehicle Routing Problems Using Unmanned Autonomous Vehicles." Thesis, North Dakota State University, 2017. https://hdl.handle.net/10365/28423.

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In this thesis, we investigate new multi-echelon vehicle routing problems for logistics operations using unmanned autonomous vehicles. This can provide immediate tangible outcomes, especially in high-demand areas that are otherwise difficult or costly to serve. This type of problem differs from the commonly used multi-echelon supply chain management systems in that here there exist no intermediate facilities that consolidate/separate products for delivery; instead all decisions are made on a per-vehicle basis. We describe here how we can obtain the necessary parameters (data collection) to evaluate the performance of such multi-echelon systems. We also provide three mathematical formulations based on different assumptions and case scenarios. We then study the differences between the three models in practice, as far as routing cost and duration of operations are concerned. We finally show that there are savings to be had by properly employing unmanned vehicles for logistics operations.

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Dowd, Garrett E. "Improving Autonomous Vehicle Safety using Communicationsand Unmanned Aerial Vehicles." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574861007798385.

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Pedreira, Carabel Carlos Javier. "Terrain Mapping for Autonomous Vehicles." Thesis, KTH, Datorseende och robotik, CVAP, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174132.

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Autonomous vehicles have become the forefront of the automotive industry nowadays, looking to have safer and more efficient transportation systems. One of the main issues for every autonomous vehicle consists in being aware of its position and the presence of obstacles along its path. The current project addresses the pose and terrain mapping problem integrating a visual odometry method and a mapping technique. An RGB-D camera, the Kinect v2 from Microsoft, was chosen as sensor for capturing information from the environment. It was connected to an Intel mini-PC for real-time processing. Both pieces of hardware were mounted on-board of a four-wheeled research concept vehicle (RCV) to test the feasibility of the current solution at outdoor locations. The Robot Operating System (ROS) was used as development environment with C++ as programming language. The visual odometry strategy consisted in a frame registration algorithm called Adaptive Iterative Closest Keypoint (AICK) based on Iterative Closest Point (ICP) using Oriented FAST and Rotated BRIEF (ORB) as image keypoint extractor. A grid-based local costmap rolling window type was implemented to have a two-dimensional representation of the obstacles close to the vehicle within a predefined area, in order to allow further path planning applications. Experiments were performed both offline and in real-time to test the system at indoors and outdoors scenarios. The results confirmed the viability of using the designed framework to keep tracking the pose of the camera and detect objects in indoor environments. However, outdoor environments evidenced the limitations of the features of the RGB-D sensor, making the current system configuration unfeasible for outdoor purposes.
Autonoma fordon har blivit spetsen för bilindustrin i dag i sökandet efter säkrare och effektivare transportsystem. En av de viktigaste sakerna för varje autonomt fordon består i att vara medveten om sin position och närvaron av hinder längs vägen. Det aktuella projektet behandlar position och riktning samt terrängkartläggningsproblemet genom att integrera en visuell distansmätnings och kartläggningsmetod. RGB-D kameran Kinect v2 från Microsoft valdes som sensor för att samla in information från omgivningen. Den var ansluten till en Intel mini PC för realtidsbehandling. Båda komponenterna monterades på ett fyrhjuligt forskningskonceptfordon (RCV) för att testa genomförbarheten av den nuvarande lösningen i utomhusmiljöer. Robotoperativsystemet (ROS) användes som utvecklingsmiljö med C++ som programmeringsspråk. Den visuella distansmätningsstrategin bestod i en bildregistrerings-algoritm som kallas Adaptive Iterative Closest Keypoint (AICK) baserat på Iterative Closest Point (ICP) med hjälp av Oriented FAST och Rotated BRIEF (ORB) som nyckelpunktsutvinning från bilder. En rutnätsbaserad lokalkostnadskarta av rullande-fönster-typ implementerades för att få en tvådimensionell representation av de hinder som befinner sig nära fordonet inom ett fördefinierat område, i syfte att möjliggöra ytterligare applikationer för körvägen. Experiment utfördes både offline och i realtid för att testa systemet i inomhus- och utomhusscenarier. Resultaten bekräftade möjligheten att använda den utvecklade metoden för att spåra position och riktning av kameran samt upptäcka föremål i inomhusmiljöer. Men utomhus visades begränsningar i RGB-D-sensorn som gör att den aktuella systemkonfigurationen är värdelös för utomhusbruk.

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Dever, Christopher W. (Christopher Walden) 1972. "Parametrized maneuvers for autonomous vehicles." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/30328.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.
Includes bibliographical references (p. 197-209).
This thesis presents a method for creating continuously parametrized maneuver classes for autonomous vehicles. These classes provide useful tools for motion planners, bundling sets of related vehicle motions based on a low-dimensional parameter vector that describes the fundamental high-level variations within the trajectory set. The method follows from a relaxation of nonlinear parametric programming necessary conditions that discards the objective function, leaving a simple coordinatized feasible space including all dynamically admissible vehicle motions. A trajectory interpolation algorithm uses projection and integration methods to create the classes, starting from arbitrary user-provided maneuver examples, including those obtained from standard nonlinear optimization or motion capture of human-piloted vehicle flights. The interpolation process, which can be employed for real-time trajectory generation, efficiently creates entire maneuver sets satisfying nonlinear equations of motion and nonlinear state and control constraints without resorting to iterative optimization. Experimental application to a three degree-of-freedom rotorcraft testbed and the design of a stable feedforward control framework demonstrates the essential features of the method on actual hardware. Integration of the trajectory classes into an existing hybrid system motion planning framework illustrates the use of parametrized maneuvers for solving vehicle guidance problems. The earlier relaxation of strict optimality conditions makes possible the imposition of affine state transformation constraints, allowing maneuver sets to fit easily into a mixed integer-linear programming path planner.
(cont.) The combined scheme generalizes previous planning techniques based on fixed, invariant representations of vehicle equilibrium states and maneuver elements. The method therefore increases the richness of available guidance solutions while maintaining problem tractability associated with hierarchical system models. Application of the framework to one and two-dimensional path planning examples demonstrates its usefulness in practical autonomous vehicle guidance scenarios.
by Christopher Walden Dever.
Ph.D.

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RAHMAN, SHAHNUR. "Visual Perception in Autonomous Vehicles." Thesis, KTH, Hållbarhet och industriell dynamik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189346.

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The human factor accounts for nine out of ten out of all traffic accidents, and because more vehicles are being deployed on the roads, the number of accidents will increase. Because of this, various automated functions have been implemented in vehicles in order to minimize the human factor in driving. In recent year, this development has accelerated and vehicles able to perform the complete driving task without any human assistance have begun to emerge from different projects around the world. However, the autonomous vehicle still has many barriers to overcome before safe driving in traffic becomes a reality. One of these barriers is the difficulty to visually perceive the surrounding. This is partly because of the fact that something can cover the camera sensors, but it is also problematic to translate the perceived data, that the sensors are collecting, into something valuable for the passenger. The situation could be improved if wireless communications were available to the autonomous vehicle. Instead of trying to understand the surrounding by the use of camera sensors, the autonomous vehicle could obtain the necessary data via wireless communication, which was the subject of this study. The study showed that wireless communication will be significant for the autonomous vehicle in the future. The conclusion is based on the fact that wireless communication was a solution in other transport systems that have had the similar barrier as for the autonomous vehicle. There are also plans on managing the barrier via wireless communication in pilot projects related to autonomous vehicles.
Den mänskliga faktorn står för nio av tio utav alla trafikolyckor, och eftersom att allt fler fordon kommer ut på vägarna så leder det till att olycksantalet ökar. På grund av detta så har olika automatiserade funktioner applicerats i fordonet för att undvika den mänskliga faktorn i körningen. Denna utveckling har accelererat och fordon som ska kunna utföra hela det dynamiska framförandet utan mänsklig assistans har börjat utvecklas i olika projekt runt om i världen. Dock så har det autonoma fordonet många barriärer kvar att övervinna, för säkert framförande, varav en av dessa barriärer är fordonets förmåga att visuellt uppfatta omgivningen. Dels genom att något kan täcka kamerasensorerna men även att kunna omsätta det sensorerna uppfattar till något värdefullt för passageraren. Situationen skulle dock kunna förbättras om trådlös kommunikation gjordes tillgänglig för det autonoma fordonet. Istället för att försöka uppfatta omgivningen via kamerasensorer, skulle det autonoma fordonet kunna få den information som behövs via trådlös kommunikation, vilket är vad denna studie behandlade. Studien visade att trådlös kommunikation kommer att ha en betydelse för det autonoma fordonet i framtiden. Slutsatsen grundar sig på att trådlös kommunikation varit en lösning inom andra transportsystem som haft en liknande barriär som för det autonoma fordonet. Man planerar dessutom på att hantera det autonoma fordonets barriär via trådlös kommunikation i pilotprojekt i dagsläget

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Alhuttaitawi, Saif. "Intersection coordination for Autonomous Vehicles." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20936.

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Connected Autonomous Vehicles require intelligent autonomous intersection management for safe and efficient operation. Given the uncertainty in vehicle trajectory, intersection management techniques must consider a safety buffer among the vehicles, which must also account for the network and computational delay, queue and determine the best solution to avoid traffic congestions (smart intersection management), in this paper we model traffic by using Poisson distribution method then add a birth-death processes for each state and combine both two in one queuing system (The Markovian chain) to model the traffic.Also, this paper will compare some autonomous vehicles communication techniques in intersections to draw the best scenario for autonomous vehicle network communication in order to reduce the traffic congestion in an intersection.The Connected Autonomous Vehicles and a normal autonomous vehicle, as well from the third line of the intersection a mix between the both will be provided into the intersection.The last section is about applying the results from the first and second research question into a simulator and compare the simulation results to approve the advantage of using the next generation of transportation technology (The connected autonomous vehicles) over the normal conventional vehicles.

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Books on the topic "Autonomous vehicles"

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Van Uytsel, Steven, and Danilo Vasconcellos Vargas, eds. Autonomous Vehicles. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9255-3.

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Yan, Jing, Xian Yang, Haiyan Zhao, Xiaoyuan Luo, and Xinping Guan. Autonomous Underwater Vehicles. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6096-2.

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Cox, Ingemar J., and Gordon T. Wilfong, eds. Autonomous Robot Vehicles. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4613-8997-2.

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Berns, Karsten, and Ewald von Puttkamer. Autonomous Land Vehicles. Wiesbaden: Vieweg+Teubner, 2009. http://dx.doi.org/10.1007/978-3-8348-9334-5.

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Fazlollahtabar, Hamed, and Mohammad Saidi-Mehrabad. Autonomous Guided Vehicles. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14747-5.

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Cheng, Hong. Autonomous Intelligent Vehicles. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2280-7.

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Cruz, Nuno A. Autonomous underwater vehicles. Rijeka, Croatia: InTech, 2011.

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Cox, I. J. Autonomous Robot Vehicles. New York, NY: Springer New York, 1990.

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J, Cox I., and Wilfong Gordon Thomas 1958-, eds. Autonomous robot vehicles. New York: Springer-Verlag, 1990.

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Sethi, Ishwar K. Autonomous Vehicles and Systems. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781032629537.

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Book chapters on the topic "Autonomous vehicles"

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Van Uytsel, Steven, and Danilo Vasconcellos Vargas. "Challenges for and with Autonomous Vehicles: An Introduction." In Autonomous Vehicles, 1–17. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9255-3_1.

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Fenwick, Mark, and Erik P. M. Vermeulen. "Organizing-for-Innovation and New Models of Corporate Governance in the Automobile Firm of the Future." In Autonomous Vehicles, 199–223. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9255-3_10.

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van Wees, Kiliaan A. P. C. "Technology in the Driver’s Seat: Legal Obstacles and Regulatory Gaps in Road Traffic Law." In Autonomous Vehicles, 21–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9255-3_2.

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Van Uytsel, Steven. "Testing Autonomous Vehicles on Public Roads: Facilitated by a Series of Alternative, Often Soft, Legal Instruments." In Autonomous Vehicles, 39–64. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9255-3_3.

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Van Uytsel, Steven. "Different Liability Regimes for Autonomous Vehicles: One Preferable Above the Other?" In Autonomous Vehicles, 67–92. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9255-3_4.

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He, Shanshan. "Who is Liable for the UBER Self-Driving Crash? Analysis of the Liability Allocation and the Regulatory Model for Autonomous Vehicles." In Autonomous Vehicles, 93–111. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9255-3_5.

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Muckenhuber, Stefan, Kenan Softic, Anton Fuchs, Georg Stettinger, and Daniel Watzenig. "Sensors for Automated Driving." In Autonomous Vehicles, 115–46. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9255-3_6.

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Vasconcellos Vargas, Danilo. "Learning Systems Under Attack—Adversarial Attacks, Defenses and Beyond." In Autonomous Vehicles, 147–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9255-3_7.

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Gopalswamy, Swaminathan. "Infrastructure Enabled Autonomy—Autonomy as a Service." In Autonomous Vehicles, 165–84. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9255-3_8.

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Sarvi, Majid, Saeed Asadi, and Steven Van Uytsel. "New Fixes for Old Traffic Problems: Connected Transport Systems and AIMES." In Autonomous Vehicles, 185–96. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9255-3_9.

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Conference papers on the topic "Autonomous vehicles"

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Short, M. "Smart Cities and the role of Mobile." In Autonomous Passenger Vehicles. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0058.

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Reed, N. "GATEway - Greenwich Automated Transport Environment." In Autonomous Passenger Vehicles. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0059.

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Edwards, T. "Connected and automated vehicles: Concepts of V2x communications and cooperative driving." In Autonomous Passenger Vehicles. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0060.

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Avery, M. "Autonomy - A key contributor to road safety." In Autonomous Passenger Vehicles. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0061.

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Stevens, A. "Automated Platooning." In Autonomous Passenger Vehicles. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0062.

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King, R. "Traffic Management in a Connected or Autonomous Vehicle Environment." In Autonomous Passenger Vehicles. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0063.

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Strong, A., and S. Baker. "How will Autonomous Vehicle technologies affect driver liability and overall insurance?" In Autonomous Passenger Vehicles. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0064.

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Blythe, P. "Autonomous Vehicles: Some thoughts on Consumer Engagement." In Autonomous Passenger Vehicles. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0065.

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Langari, Reza. "Autonomous vehicles." In 2017 American Control Conference (ACC). IEEE, 2017. http://dx.doi.org/10.23919/acc.2017.7963571.

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Gangadharaiah, Rakesh, Lauren Mims, Yunyi Jia, and Johnell Brooks. "Opinions from Users Across the Lifespan about Fully Autonomous and Rideshare Vehicles with Associated Features." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0673.

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<div class="section abstract"><div class="htmlview paragraph">Fully autonomous vehicles have the potential to fundamentally transform the future transportation system. While previous research has examined individuals’ perceptions towards fully autonomous vehicles, a complete understanding of attitudes and opinions across the lifespan is unknown. Therefore, individuals’ awareness, acceptance, and preferences towards autonomous vehicles were obtained from 75 participants through interviews with three diverse groups of participants: 20 automotive engineering graduate students who were building an autonomous concept vehicle, 21 non-technical adults, and 34 senior citizens. The results showed that regardless of age, an individual’s readiness to ride in a fully autonomous vehicle and the vehicle’s requirements were influenced by the users’ understanding of autonomous vehicles. All of the engineering students understand what a fully autonomous vehicle is and this group was the most willing to ride especially compared to the seniors, where only half of the seniors knew what a fully autonomous vehicle is and 58.8% were not at all ready to ride one. The desire to have a manual control option or the ability to override the vehicle was common (90% of the engineering students, 95.2% of the adults, and 82.4% of the seniors), especially for individuals who reported not being ready to ride in a fully autonomous vehicle. The majority of all three groups of participants (85% of the engineering students, 81% of the adults, and 52.9% of the seniors) considered it essential that the vehicle should convey information about the vehicle’s status and intended behavior. Diagnostic information about the vehicle was desired by the engineering students (71.4%), who had a technical understanding of autonomous vehicles and current automotive related technologies. When autonomous vehicles are available, most participants anticipate preferring to use them as a rideshare service model (75% of the engineering students, 38% of the adults, and 27% of the seniors) rather than owning (5% of the engineering students, 19% of the adults, and 21% of the seniors) the autonomous vehicle themselves. Regarding the topic of sharing rides with strangers, both the automotive engineering students (90%) and the adults (52.6%) were comfortable with the idea of pooled rideshare in comparison to the seniors (29.4%). In future efforts, it will be important to include potential autonomous vehicle users of a wide age range as well as physical, cognitive, and visual abilities.</div></div>

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Reports on the topic "Autonomous vehicles"

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Smith, Emma, Julie Webster, and Annette Stumpf. Autonomous Transport Innovation : the regulatory environment of autonomous vehicles. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42025.

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This technical note series under the Autonomous Transport Innovation research program is intended to be a primer on autonomous vehicles (AVs), their testing, and associated infrastructure. A review of the regulatory environment for autonomous vehicles is necessary to define rules imposed on technology or operations of autonomous vehicles in various capacities. Acknowledging such regulation will aid in productive closed-course site development by structuring the course based on what autonomous vehicle developers and manufacturers must program their vehicles to adhere to in a given setting.

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Rolufs, Angela, Amelia Trout, Kevin Palmer, Clark Boriack, Bryan Brilhart, and Annette Stumpf. Autonomous Transport Innovation (ATI) : integration of autonomous electric vehicles into a tactical microgrid. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42160.

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The objective of the Autonomous Transport Innovation (ATI) technical research program is to investigate current gaps and challenges then develop solutions to integrate emerging electric transport vehicles, vehicle autonomy, vehicle-to-grid (V2G) charging and microgrid technologies with military legacy equipment. The ATI research area objectives are to: identify unique military requirements for autonomous transportation technologies; identify currently available technologies that can be adopted for military applications and validate the suitability of these technologies to close need gaps; identify research and operational tests for autonomous transport vehicles; investigate requirements for testing and demonstrating of bidirectional vehicle charging within a tactical environment; develop requirements for a sensored, living laboratory that will be used to assess the performance of autonomous innovations; and integrate open standards to promote interoperability and broad-platform compatibility. The research performed resulted in an approach to develop a sensored, living laboratory with operational testing capability to assess the safety, utility, interoperability, and resiliency of autonomous electric transport and V2G technologies in a tactical microgrid. The living laboratory will support research and assessment of emerging technologies and determine the prospect for implementation in defense transport operations and contingency base energy resilience.

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Moorehead, Stewart. Unsettled Topics in Obstacle Detection for Autonomous Agricultural Vehicles. SAE International, December 2021. http://dx.doi.org/10.4271/epr2021029.

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Agricultural vehicles often drive along the same terrain day after day or year after year. Yet, they still must detect if a moveable object, such as another vehicle or an animal, happens to be on their path or if environmental conditions have caused muddy spots or washouts. Obstacle detection is one of the major missing pieces that can remove humans from highly automated agricultural machines today and enable the autonomous vehicles of the future. Unsettled Topics in Obstacle Detection for Autonomous Agricultural Vehicles examines the challenges of environmental object detection and collision prevention, including air obscurants, holes and soft spots, prior maps, vehicle geometry, standards, and close contact with large objects.

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Abdul Hamid, Umar Zakir. Responder-to-Vehicle Technologies for Connected and Autonomous Vehicles. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, May 2023. http://dx.doi.org/10.4271/epr2023010.

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<div class="section abstract"><div class="htmlview paragraph">Recently, there has been a slight increase in interest in the use of responder-to-vehicle (R2V) technology for emergency vehicles, such as ambulances, fire trucks, and police cars. R2V technology allows for the exchange of information between different types of responder vehicles, including connected and automated vehicles (CAVs). It can be used in collision avoidance or emergency situations involving CAV responder vehicles. The benefits of R2V are not limited to fully autonomous vehicles (e.g., SAE Level 4), but can also be used in Level 2 CAV scenarios. However, despite the potential benefits of R2V, discussions on this topic are still limited.</div><div class="htmlview paragraph"><b>Responder-to-Vehicle Technologies for Connected and Autonomous Vehicles</b> aims to provide an overview of R2V technology and its applications for CAV systems, particularly in the context of collision avoidance features. The responder vehicles in question can be autonomous or non-autonomous. It is hoped that it will provide valuable information and knowledge on vehicle connectivity and automation in the current automotive and mobility ecosystem, enabling the development of safer and more reliable autonomous driving technology. The report is intended for both industrial and academic experts and is expected to stimulate further discussions on the development and standardization of R2V technology.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>

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Yang, Shuhan. Backdoor attack in autonomous vehicles. Ames (Iowa): Iowa State University, December 2023. http://dx.doi.org/10.31274/cc-20240624-276.

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Wang, Shenlong, and David Forsyth. Safely Test Autonomous Vehicles with Augmented Reality. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-015.

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This work exploits augmented reality to safely train and validate autonomous vehicles’ performance in the real world under safety-critical scenarios. Toward this goal, we first develop algorithms that create virtual traffic participants with risky behaviors and seamlessly insert the virtual events into real images perceived from the physical world. The resulting composed images are photorealistic and physically grounded. The manipulated images are fed into the autonomous vehicle during testing, allowing the self-driving vehicle to react to such virtual events within either a photorealistic simulator or a real-world test track and real hardware systems. Our presented technique allows us to develop safe, hardware-in-the-loop, and cost-effective tests for self-driving cars to respond to immersive safety-critical traffic scenarios.

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Moorehead, Stewart. Unsettled Issues Regarding the Commercialization of Autonomous Agricultural Vehicles. SAE International, February 2022. http://dx.doi.org/10.4271/epr2022003.

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Autonomous agricultural vehicles are entering the marketplace, performing jobs that current equipment cannot do or are too dangerous for humans to perform. They offer the prospect of greater farm productivity, and they will help to feed the world’s growing population. This report looks at several topics that impact the commercial success of autonomous agricultural vehicles. The economic benefit that an autonomous system brings to a farm will be discussed alongside machine utilization rates, job quality, and labor savings. The need for standards and regulations to help promote the development of safe systems—as well as to define the language around autonomous agriculture—is also considered. Additionally, this report will highlight the importance of reliability in agricultural machinery and how successful commercialization of autonomy will depend on the ability to do the job correctly and consistently. A critical part of commercial success is how the autonomous agricultural vehicle fits into existing farm processes to provide a complete solution for the farmer. It is hoped that this report will help developers interested in commercializing autonomous agricultural vehicles consider more than just the technical problems to solve and make choices beneficial to market adoption.

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Hover, Franz S. Maneuvering Performance of Autonomous Underwater Vehicles. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada446746.

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Ruff, Heath A., and Gloria L. Calhoun. Human Supervision of Multiple Autonomous Vehicles. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada606578.

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Varaiya, Pravin. Data Provisioning Systems for Autonomous Vehicles. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada369431.

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