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Journal articles on the topic 'Autonomus driving'

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Published: 25 June 2021
Last updated: 2 February 2022

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1

Knabl, Florian, and Lars Mesow. "Autonomes Fahren im Kleinformat Audi Autonomous Driving Cup." Sonderprojekte ATZ/MTZ 22, S2 (December 2017): 26–29. http://dx.doi.org/10.1007/s41491-017-0006-z.

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STAYTON, ERIK, MELISSA CEFKIN, and JINGYI ZHANG. "Autonomous Individuals in Autonomous Vehicles: The Multiple Autonomies of Self-Driving Cars." Ethnographic Praxis in Industry Conference Proceedings 2017, no. 1 (November 2017): 92–110. http://dx.doi.org/10.1111/1559-8918.2017.01140.

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3

Ansari, Hashim Shakil, and Goutam R. "Autonomous Driving using Deep Reinforcement Learning in Urban Environment." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1573–75. http://dx.doi.org/10.31142/ijtsrd23442.

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4

Salow, Holger. "Autonomous driving." ATZ worldwide 110, no. 1 (January 2008): 14–18. http://dx.doi.org/10.1007/bf03224976.

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Walch, Marcel, Kristin Mühl, Martin Baumann, and Michael Weber. "Autonomous Driving." International Journal of Mobile Human Computer Interaction 9, no. 2 (April 2017): 58–74. http://dx.doi.org/10.4018/ijmhci.2017040104.

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Autonomous vehicles will need de-escalation strategies to compensate when reaching system limitations. Car-driver handovers can be considered one possible method to deal with system boundaries. The authors suggest a bimodal (auditory and visual) handover assistant based on user preferences and design principles for automated systems. They conducted a driving simulator study with 30 participants to investigate the take-over performance of drivers. In particular, the authors examined the effect of different warning conditions (take-over request only with 4 and 6 seconds time budget vs. an additional pre-cue, which states why the take-over request will follow) in different hazardous situations. Their results indicated that all warning conditions were feasible in all situations, although the short time budget (4 seconds) was rather challenging and led to a less safe performance. An alert ahead of a take-over request had the positive effect that the participants took over and intervened earlier in relation to the appearance of the take-over request. Overall, the authors' evaluation showed that bimodal warnings composed of textual and iconographic visual displays accompanied by alerting jingles and spoken messages are a promising approach to alert drivers and to ask them to take over.
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Fuchs, Andreas. "Autonomous Driving." ATZoffhighway worldwide 11, no. 1 (March 2018): 3. http://dx.doi.org/10.1007/s41321-018-0013-3.

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7

Poledna, S., F. Eichler, and P. Schöggl. "Autonomous Driving." Sonderprojekte ATZ/MTZ 24, S1 (August 2019): 47. http://dx.doi.org/10.1007/s41491-019-0029-8.

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Mühl, Kristin, Christoph Strauch, Christoph Grabmaier, Susanne Reithinger, Anke Huckauf, and Martin Baumann. "Get Ready for Being Chauffeured." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 8 (September 9, 2019): 1322–38. http://dx.doi.org/10.1177/0018720819872893.

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Objective We investigated passenger’s trust and preferences using subjective, qualitative, and psychophysiological measures while being driven either by human or automation in a field study and a driving simulator experiment. Background The passenger’s perspective has largely been neglected in autonomous driving research, although the change of roles from an active driver to a passive passenger is incontrovertible. Investigations of passenger’s appraisals on self-driving vehicles often seem convoluted with active manual driving experiences instead of comparisons with being driven by humans. Method We conducted an exploratory field study using an autonomous research vehicle ( N = 11) and a follow-up experimental driving simulation ( N = 24). Participants were driven on the same course by a human and an autonomous agent sitting on a passenger seat. Skin conductance, trust, and qualitative characteristics of the perceived driving situation were assessed. In addition, the effect of driving style (defensive vs. sporty) was evaluated in the simulator. Results Both investigations revealed a close relation between subjective trust ratings and skin conductance, with increased trust and by trend reduced arousal for human compared with automation in control. Even though driving behavior was equivalent in the simulator when being driven by human and automation, passengers most preferred and trusted the human-defensive driver. Conclusion Individual preferences for driving style and human or autonomous vehicle control influence trust and subjective driving characterizations. Application The findings are applicable in human-automation research, reminding to not neglect subjective attributions and psychophysiological reactions as a result of ascribed control duties in relation to specific execution characteristics.
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Li, W., C. W. Pan, R. Zhang, J. P. Ren, Y. X. Ma, J. Fang, F. L. Yan, et al. "AADS: Augmented autonomous driving simulation using data-driven algorithms." Science Robotics 4, no. 28 (March 27, 2019): eaaw0863. http://dx.doi.org/10.1126/scirobotics.aaw0863.

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Simulation systems have become essential to the development and validation of autonomous driving (AD) technologies. The prevailing state-of-the-art approach for simulation uses game engines or high-fidelity computer graphics (CG) models to create driving scenarios. However, creating CG models and vehicle movements (the assets for simulation) remain manual tasks that can be costly and time consuming. In addition, CG images still lack the richness and authenticity of real-world images, and using CG images for training leads to degraded performance. Here, we present our augmented autonomous driving simulation (AADS). Our formulation augmented real-world pictures with a simulated traffic flow to create photorealistic simulation images and renderings. More specifically, we used LiDAR and cameras to scan street scenes. From the acquired trajectory data, we generated plausible traffic flows for cars and pedestrians and composed them into the background. The composite images could be resynthesized with different viewpoints and sensor models (camera or LiDAR). The resulting images are photorealistic, fully annotated, and ready for training and testing of AD systems from perception to planning. We explain our system design and validate our algorithms with a number of AD tasks from detection to segmentation and predictions. Compared with traditional approaches, our method offers scalability and realism. Scalability is particularly important for AD simulations, and we believe that real-world complexity and diversity cannot be realistically captured in a virtual environment. Our augmented approach combines the flexibility of a virtual environment (e.g., vehicle movements) with the richness of the real world to allow effective simulation.
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Manawadu, Udara Eshan, Masaaki Ishikawa, Mitsuhiro Kamezaki, and Shigeki Sugano. "Analysis of Preference for Autonomous Driving Under Different Traffic Conditions Using a Driving Simulator." Journal of Robotics and Mechatronics 27, no. 6 (December 18, 2015): 660–70. http://dx.doi.org/10.20965/jrm.2015.p0660.

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<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/08.jpg"" width=""300"" /> Driving simulator</div>Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the preferences and individual driving experiences of novice and experienced drivers of autonomous and conventional vehicles under different traffic and road conditions. We first developed a simplified driving simulator that could connect to different driver-vehicle interfaces (DVI). We then created virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and we implemented fully autonomous driving. We then conducted experiments to clarify how the autonomous driving experience differed for the two groups. The results showed that experienced drivers opt for conventional driving overall, mainly due to the flexibility and driving pleasure it offers, while novices tend to prefer autonomous driving due to its inherent ease and safety. A further analysis indicated that drivers preferred to use both autonomous and conventional driving methods interchangeably, depending on the road and traffic conditions.
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11

Köster, Oliver. "Mandatory Autonomous Driving?" ATZelectronics worldwide 14, no. 3 (March 2019): 66. http://dx.doi.org/10.1007/s38314-019-0016-6.

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Schockenhoff, Ferdinand, Hannes Nehse, and Markus Lienkamp. "Maneuver-Based Objectification of User Comfort Affecting Aspects of Driving Style of Autonomous Vehicle Concepts." Applied Sciences 10, no. 11 (June 6, 2020): 3946. http://dx.doi.org/10.3390/app10113946.

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Driving maneuvers try to objectify user needs regarding the driving dynamics for a vehicle concept. As autonomous vehicles will not be driven by people, the driving style that merges the individual aspects of driving dynamics, like user comfort, will be part of the vehicle concept itself. New driving maneuvers are, therefore, necessary to objectify the driving style of autonomous vehicle concepts with all its interdependencies relating to the individual aspects. This paper presents a methodology to design such driving maneuvers and includes a pilot study and a user study. As an example, the methodology was applied to the parameters of user comfort and travel time. The driven maneuvers resulted in statistical equations to objectify the interdependencies of these two aspects. Finally, this paper provides an outlook for needed maneuvers in order to tackle the entire driving style with its multidimensional facets.
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Kruse, E. "Geräusch- und Vibrationsbekämpfung in modernen Fahrzeugen/Noise and vibration control in modern vehicles and new challenges due to the rise of e-mobility." Lärmbekämpfung 15, no. 06 (2020): 194–98. http://dx.doi.org/10.37544/1863-4672-2020-06-24.

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Zusammenfassung Weniger Kraftstoffverbrauch bei besseren Fahrleistungen, mehr Komfort und Fahrsicherheit ohne Mehrkosten sowie der Wandel zur Elektromobilität und zum autonomen Fahren: All diese Herausforderungen halten die Automobilindustrie konstant in Bewegung. Neben der technischen Umsetzung haben die Veränderungen Einfluss auf erzeugte Geräusche und Vibrationen und so auf den Komfort und das Wohlbefinden aller Fahrzeuginsassen. Automobilhersteller stehen daher gleich vor mehreren Zielkonflikten. Hier sind neue Lösungsansätze der automobilen Schwingungstechnik gefragt, um auch in der elektrifizierten und autonomen Zukunft komfortabel, entspannt und sicher anzukommen. SUMMARY Lower fuel consumption with better driving performance, more comfort and driving safety without additional costs as well as the change to electromobility and autonomous driving: All these challenges keep the automotive industry constantly on the move. In addition to the technical implementation, the changes have an impact on generated noise and vibrations and thus on the comfort and well-being of all vehicle occupants. Vehicle manufacturers are therefore faced with several conflicting goals. This calls for new approaches in automotive vibration control technology in order to arrive comfortably, relaxed and safely in the electrified and autonomous future.
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Rathour, S. S., Ali Boyali, Lyu Zheming, Seiichi Mita, and Vijay John. "A Map-based Lateral and Longitudinal DGPS/DR Bias Estimation Method for Autonomous Driving." International Journal of Machine Learning and Computing 7, no. 4 (October 2017): 67–71. http://dx.doi.org/10.18178/ijmlc.2017.7.4.622.

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15

Manawadu, Udara, Masaaki Ishikawa, Mitsuhiro Kamezaki, and Shigeki Sugano. "Scenario Authoring for a Driving Simulator to Evaluate Driver Experience in Intelligent Autonomous Vehicles." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2015.6 (2015): 94–95. http://dx.doi.org/10.1299/jsmeicam.2015.6.94.

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16

Baber, J., J. Kolodko, T. Noel, M. Parent, and L. Vlacic. "Cooperative autonomous driving - Intelligent vehicles sharing city roads cooperative autonomous driving." IEEE Robotics & Automation Magazine 12, no. 1 (March 2005): 44–49. http://dx.doi.org/10.1109/mra.2005.1411418.

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17

Arshad, Saba, Muhammad Sualeh, Dohyeong Kim, Dinh Van Nam, and Gon-Woo Kim. "Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment." Sensors 20, no. 18 (September 5, 2020): 5053. http://dx.doi.org/10.3390/s20185053.

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In recent years, research and development of autonomous driving technology have gained much interest. Many autonomous driving frameworks have been developed in the past. However, building a safely operating fully functional autonomous driving framework is still a challenge. Several accidents have been occurred with autonomous vehicles, including Tesla and Volvo XC90, resulting in serious personal injuries and death. One of the major reasons is the increase in urbanization and mobility demands. The autonomous vehicle is expected to increase road safety while reducing road accidents that occur due to human errors. The accurate sensing of the environment and safe driving under various scenarios must be ensured to achieve the highest level of autonomy. This research presents Clothoid, a unified framework for fully autonomous vehicles, that integrates the modules of HD mapping, localization, environmental perception, path planning, and control while considering the safety, comfort, and scalability in the real traffic environment. The proposed framework enables obstacle avoidance, pedestrian safety, object detection, road blockage avoidance, path planning for single-lane and multi-lane routes, and safe driving of vehicles throughout the journey. The performance of each module has been validated in K-City under multiple scenarios where Clothoid has been driven safely from the starting point to the goal point. The vehicle was one of the top five to successfully finish the autonomous vehicle challenge (AVC) in the Hyundai AVC.
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M R, Prajwal. "Self-Driving Autonomous Car." International Journal for Research in Applied Science and Engineering Technology 8, no. 8 (August 31, 2020): 260–63. http://dx.doi.org/10.22214/ijraset.2020.30866.

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Chen, Shu-Ching. "Multimedia for Autonomous Driving." IEEE MultiMedia 26, no. 3 (July 1, 2019): 5–8. http://dx.doi.org/10.1109/mmul.2019.2935397.

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Perez, Manuel. "Autonomous driving in NMR." Magnetic Resonance in Chemistry 55, no. 1 (November 17, 2016): 15–21. http://dx.doi.org/10.1002/mrc.4546.

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Tian, Jilei, Alvin Chin, and Halim Yanikomeroglu. "Connected and Autonomous Driving." IT Professional 20, no. 6 (November 1, 2018): 31–34. http://dx.doi.org/10.1109/mitp.2018.2876928.

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22

Franke, U., D. Gavrila, S. Gorzig, F. Lindner, F. Puetzold, and C. Wohler. "Autonomous driving goes downtown." IEEE Intelligent Systems 13, no. 6 (November 1998): 40–48. http://dx.doi.org/10.1109/5254.736001.

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23

Reichenbach, Michael. "Autonomous Driving and Digitization." ATZ worldwide 120, no. 1 (January 2018): 16–17. http://dx.doi.org/10.1007/s38311-017-0175-0.

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V S, Amar. "Autonomous Driving using CNN." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3633–36. http://dx.doi.org/10.22214/ijraset.2021.35771.

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Human beings are currently addicted to automation and robotics technologies. The state-of-the-art in deep learning technologies and AI is the subject of this autonomous driving. Driving with automated driving systems promises to be safe, enjoyable, and efficient.. It is preferable to train in a virtual environment first and then move to a real-world one. Its goal is to enable a vehicle to recognise its surroundings and navigate without the need for human intervention. The raw pixels from a single front-facing camera were directly transferred to driving commands using a convolution neural network (CNN). This end-to-end strategy proved to be remarkably effective, The system automatically learns internal representations of the essential processing stages such as detecting useful road components using only the human steering angle as the training signal. We never expressly taught it to recognise the contour of roadways, for example. In comparison to explicit issue decomposition, such as lane marking detection, Our end-to-end solution optimises all processing processes at the same time, including path planning and control. We believe that this will lead to improved performance and smaller systems in the long run. Internal components will self-optimize to maximise overall system performance, resulting in improved performance.
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Lee, Joey, Benedikt Groß, and Raphael Reimann. "Who wants to be a self-driving car?" Information Design Journal 25, no. 1 (December 31, 2019): 21–27. http://dx.doi.org/10.1075/idj.25.1.02lee.

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Abstract Self-driving cars and autonomous transportation systems are projected to create radical societal changes, yet public understanding and trust of self-driving cars and autonomous systems is limited. The authors present a new mixed-reality experience designed to provide its users with insights into the ways that self-driving cars operate. A single-person vehicle equipped with sensors provides its users with data driven visual feedback in a virtual reality headset to navigate in physical space. The authors explore how immersive experiences might provide ‘conceptual affordances’ that lower the entry barrier for diverse audiences to discuss complex topics.
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López, Joaquín, Pablo Sánchez-Vilariño, Rafael Sanz, and Enrique Paz. "Implementing Autonomous Driving Behaviors Using a Message Driven Petri Net Framework." Sensors 20, no. 2 (January 13, 2020): 449. http://dx.doi.org/10.3390/s20020449.

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Most autonomous car control frameworks are based on a middleware layer with several independent modules that are connected by an inter-process communication mechanism. These modules implement basic actions and report events about their state by subscribing and publishing messages. Here, we propose an executive module that coordinates the activity of these modules. This executive module uses hierarchical interpreted binary Petri nets (PNs) to define the behavior expected from the car in different scenarios according to the traffic rules. The module commands actions by sending messages to other modules and evolves its internal state according to the events (messages) received. A programming environment named RoboGraph (RG) is introduced with this architecture. RG includes a graphical interface that allows the edition, execution, tracing, and maintenance of the PNs. For the execution, a dispatcher loads these PNs and executes the different behaviors. The RG monitor that shows the state of all the running nets has proven to be very useful for debugging and tracing purposes. The whole system has been applied to an autonomous car designed for elderly or disabled people.
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Rößger, Peter. "Autonomous Driving How Much Autonomy Driving Does Stand." ATZelektronik worldwide 10, no. 2 (April 2015): 26–29. http://dx.doi.org/10.1007/s38314-015-0514-0.

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ENDO, Kaoru. "Autonomous Driving and Social Ethics." TRENDS IN THE SCIENCES 25, no. 5 (May 1, 2020): 5_48–5_51. http://dx.doi.org/10.5363/tits.25.5_48.

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Liu, Shaoshan, Jie Tang, Zhe Zhang, and Jean-Luc Gaudiot. "Computer Architectures for Autonomous Driving." Computer 50, no. 8 (2017): 18–25. http://dx.doi.org/10.1109/mc.2017.3001256.

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RAKSINCHAROENSAK, Pongsathorn. "Evolution of Autonomous Driving Technology." Proceedings of Mechanical Engineering Congress, Japan 2018 (2018): K18100. http://dx.doi.org/10.1299/jsmemecj.2018.k18100.

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31

Sadasivam, Srikumar. "Autonomous driving — what drives it?" Auto Tech Review 4, no. 9 (September 2015): 22–27. http://dx.doi.org/10.1365/s40112-015-0978-6.

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Kim, Junsung, Ragunathan (Raj) Rajkumar, and Markus Jochim. "Towards dependable autonomous driving vehicles." ACM SIGBED Review 10, no. 1 (February 2013): 29–32. http://dx.doi.org/10.1145/2492385.2492390.

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Cho, Eunae, and Yoonhyuk Jung. "Consumers’ understanding of autonomous driving." Information Technology & People 31, no. 5 (October 1, 2018): 1035–46. http://dx.doi.org/10.1108/itp-10-2017-0338.

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Purpose The purpose of this paper is to explore consumers’ understanding of autonomous driving by comparing perceptions of occasional drivers (ODs) and frequent drivers (FDs). Design/methodology/approach Data were gathered through semi-structured interviews with 41 drivers. Their responses were categorized into thematic categories or topics on the basis of content analysis, and the topics were structured based on the core-periphery model. Finally, the authors visualized the structure on a perceptual map by adopting a maximum tree approach. Findings Respondents’ understanding of autonomous driving were categorized into 10 topics. There were significant differences in topics and their relationships between ODs and FDs. Findings also show that FD can better detect hazardousness from autonomous driving environments than ODs. Research limitations/implications Differently from prior studies’ focus on its technological aspect and some derived benefits, the study examines it from the viewpoint of consumers, who are critical participants in the dissemination of autonomous driving. Practical implications The findings suggest that rather than focusing on developing the highest level of autonomous cars, developing in an evolutionary way by adding automated functions to existing cars can be the better strategy to dominate the autonomous vehicle market. Originality/value This study is a pioneering work in that it can be an initial empirical work on autonomous driving from the customer standpoint.
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Cobus, Andreas, Matthias Rick, Laura Sommer, Niels Backfisch, Alexander Probst, Mitja Echim, and Christof Büskens. "Optimal Control in Autonomous Driving." PAMM 17, no. 1 (December 2017): 783–84. http://dx.doi.org/10.1002/pamm.201710359.

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Devi, T. Kirthiga, Akshat Srivatsava, Kritesh Kumar Mudgal, Ranjnish Raj Jayanti, and T. Karthick. "Behaviour Cloning for Autonomous Driving." Webology 17, no. 2 (December 21, 2020): 694–705. http://dx.doi.org/10.14704/web/v17i2/web17061.

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The objective of this project is to automate the process of driving a car. The result of this project will surely reduce the number of hazards happening everyday. Our world is in progress and self driving car is on its way to reach consumer‟s door-step but the big question still lies that will people accept such a car which is fully automated and driverless. The idea is to create an autonomous Vehicle that uses only some sensors (collision detectors, temperature detectors etc.) and camera module to travel between destinations with minimal/no human intervention. The car will be using a trained Convolutional Neural Network (CNN) which would control the parameters that are required for smoothly driving a car. They are directly connected to the main steering mechanism and the output of the deep learning model will control the steering angle of the vehicle. Many algorithms like Lane Detection, Object Detection are used in tandem to provide the necessary functionalities in the car.
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Barabás, I., A. Todoruţ, N. Cordoş, and A. Molea. "Current challenges in autonomous driving." IOP Conference Series: Materials Science and Engineering 252 (October 2017): 012096. http://dx.doi.org/10.1088/1757-899x/252/1/012096.

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Yang, Diange, Xinyu Jiao, Kun Jiang, and Zhong Cao. "Driving Space for Autonomous Vehicles." Automotive Innovation 2, no. 4 (December 2019): 241–53. http://dx.doi.org/10.1007/s42154-019-00081-1.

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AbstractDriving space for autonomous vehicles (AVs) is a simplified representation of real driving environments that helps facilitate driving decision processes. Existing literatures present numerous methods for constructing driving spaces, which is a fundamental step in AV development. This study reviews the existing researches to gain a more systematic understanding of driving space and focuses on two questions: how to reconstruct the driving environment, and how to make driving decisions within the constructed driving space. Furthermore, the advantages and disadvantages of different types of driving space are analyzed. The study provides further understanding of the relationship between perception and decision-making and gives insight into direction of future research on driving space of AVs.
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Solomon, Andreea, and Ronald Kaempf. "Testing Solutions for Autonomous Driving." ATZ worldwide 119, no. 9 (August 25, 2017): 56–59. http://dx.doi.org/10.1007/s38311-017-0085-1.

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Loh, Wulf, and Catrin Misselhorn. "Autonomous Driving and Perverse Incentives." Philosophy & Technology 32, no. 4 (July 16, 2018): 575–90. http://dx.doi.org/10.1007/s13347-018-0322-6.

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Khan, Manzoor Ahmed. "Intelligent Environment Enabling Autonomous Driving." IEEE Access 9 (2021): 32997–3017. http://dx.doi.org/10.1109/access.2021.3059652.

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Ebert, Christof, Michael Weyrich, Benjamin Lindemann, and Sarada Preethi Chandrasekar. "Systematic Testing for Autonomous Driving." ATZelectronics worldwide 16, no. 3 (March 2021): 18–23. http://dx.doi.org/10.1007/s38314-020-0575-6.

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Ma, Chao, Jianru Xue, Yuehu Liu, Jing Yang, Yongqiang Li, and Nanning Zheng. "Data-Driven State-Increment Statistical Model and Its Application in Autonomous Driving." IEEE Transactions on Intelligent Transportation Systems 19, no. 12 (December 2018): 3872–82. http://dx.doi.org/10.1109/tits.2018.2797308.

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Iqbal, Hafsa, Damian Campo, Lucio Marcenaro, David Martin Gomez, and Carlo Regazzoni. "Data-driven transition matrix estimation in probabilistic learning models for autonomous driving." Signal Processing 188 (November 2021): 108170. http://dx.doi.org/10.1016/j.sigpro.2021.108170.

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Rahmati, Yalda, Mohammadreza Khajeh Hosseini, Alireza Talebpour, Benjamin Swain, and Christopher Nelson. "Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 12 (July 16, 2019): 367–79. http://dx.doi.org/10.1177/0361198119862628.

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Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.
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Goadsby, Peter J. "Cluster headache and the trigeminal-autonomic reflex: Driving or being driven?" Cephalalgia 38, no. 8 (October 30, 2017): 1415–17. http://dx.doi.org/10.1177/0333102417738252.

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46

Grigorescu, Sorin, Tiberiu Cocias, Bogdan Trasnea, Andrea Margheri, Federico Lombardi, and Leonardo Aniello. "Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles." Sensors 20, no. 19 (September 23, 2020): 5450. http://dx.doi.org/10.3390/s20195450.

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Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction.
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47

Yu, Chun Yan, Ming Hui Wu, and Xiao Sheng He. "Vehicle Swarm Motion Coordination through Independent Local-Reactive Agents." Advanced Materials Research 108-111 (May 2010): 619–24. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.619.

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Vehicle swarm refers to a group of autonomous vehicles. Vehicle swarm motion coordination is a difficult problem in Intelligent Transport System. Due to similar characteristics of reactive agents and autonomous vehicles relying on self-organization principles, this paper presents reactive agent driven motion coordination for vehicle swarm that adopts large-scale independent local-reactive agents to perform a self-organized motion coordination control mechanism, which is composed of a network of swarm collaborative agents, a set of dynamic hybrid local networks of individual swarm collaborative agent and vehicle autonomic agents, and a homogenous self-organized motion coordination control protocol for individual vehicle autonomic agent’s self-adapting motion.
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48

Martí-Belda, Ana, Patricia Bosó, and Ignacio Lijarcio. "Beliefs and expectations of driving learners about autonomous driving." Transactions on Transport Sciences 10, no. 2 (January 17, 2020): 33–41. http://dx.doi.org/10.5507/tots.2019.005.

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49

Alqudah, Yazan, Belal Sababha, Esam Qaralleh, and Tarek Yousseff. "Machine Learning to Classify Driving Events Using Mobile Phone Sensors Data." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 02 (January 26, 2021): 124. http://dx.doi.org/10.3991/ijim.v15i02.18303.

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With the ever-increasing vehicle population and introduction of autonomous and self-driving cars, innovative research is needed to ensure safety and reliability on the road. This work introduces an innovative solution that aims at understanding vehicle behavior based on sensors data. The behavior is classified according to driving events. Understanding driving events can play a significant role in road safety and estimating the expense and risks of driving and consuming a vehicle. Rather than relying on the distance and time driven, driving events can provide a more accurate measure of vehicle driving consumption. This measure will become more valuable as more autonomous vehicles and more ride sharing applications are introduced to roads around the world. Estimating driving events can also help better design the road infrastructure to reduce energy consumption. By sharing data from official vehicles and volunteers, crowd sensing can be used to better understand congestion and road safety. This work studies driving events and proposes using machine learning to classify these events into different categories. The acquired data is collected using embedded mobile device motion sensors and are used to train machine learning algorithms to classify the events.
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50

Sini, Jacopo, Antonio Costantino Marceddu, and Massimo Violante. "Automatic Emotion Recognition for the Calibration of Autonomous Driving Functions." Electronics 9, no. 3 (March 21, 2020): 518. http://dx.doi.org/10.3390/electronics9030518.

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The development of autonomous driving cars is a complex activity, which poses challenges about ethics, safety, cybersecurity, and social acceptance. The latter, in particular, poses new problems since passengers are used to manually driven vehicles; hence, they need to move their trust from a person to a computer. To smooth the transition towards autonomous vehicles, a delicate calibration of the driving functions should be performed, making the automation decision closest to the passengers’ expectations. The complexity of this calibration lies in the presence of a person in the loop: different settings of a given algorithm should be evaluated by assessing the human reaction to the vehicle decisions. With this work, we for an objective method to classify the people’s reaction to vehicle decisions. By adopting machine learning techniques, it is possible to analyze the passengers’ emotions while driving with alternative vehicle calibrations. Through the analysis of these emotions, it is possible to obtain an objective metric about the comfort feeling of the passengers. As a result, we developed a proof-of-concept implementation of a simple, yet effective, emotions recognition system. It can be deployed either into real vehicles or simulators, during the driving functions calibration.
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