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Journal articles on the topic 'Autonomus driving'

Author: Grafiati

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 (2017): 26–29. http://dx.doi.org/10.1007/s41491-017-0006-z.

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2

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 (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 (2019): 1573–75. http://dx.doi.org/10.31142/ijtsrd23442.

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4

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

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5

Walch, Marcel, Kristin Mühl, Martin Baumann, and Michael Weber. "Autonomous Driving." International Journal of Mobile Human Computer Interaction 9, no. 2 (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 additi

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6

Fuchs, Andreas. "Autonomous Driving." ATZoffhighway worldwide 11, no. 1 (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 (2019): 47. http://dx.doi.org/10.1007/s41491-019-0029-8.

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8

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 (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. Me

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9

Li, W., C. W. Pan, R. Zhang, et al. "AADS: Augmented autonomous driving simulation using data-driven algorithms." Science Robotics 4, no. 28 (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 drivi

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10

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 (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 prefer

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11

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

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12

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 (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 me

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13

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 elekt

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14

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 (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 (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 (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 d

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18

M R, Prajwal. "Self-Driving Autonomous Car." International Journal for Research in Applied Science and Engineering Technology 8, no. 8 (2020): 260–63. http://dx.doi.org/10.22214/ijraset.2020.30866.

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19

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

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20

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

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21

Tian, Jilei, Alvin Chin, and Halim Yanikomeroglu. "Connected and Autonomous Driving." IT Professional 20, no. 6 (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 (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 (2018): 16–17. http://dx.doi.org/10.1007/s38311-017-0175-0.

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24

V S, Amar. "Autonomous Driving using CNN." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (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 netwo

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25

Lee, Joey, Benedikt Groß, and Raphael Reimann. "Who wants to be a self-driving car?" Information Design Journal 25, no. 1 (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

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26

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 (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

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27

Rößger, Peter. "Autonomous Driving How Much Autonomy Driving Does Stand." ATZelektronik worldwide 10, no. 2 (2015): 26–29. http://dx.doi.org/10.1007/s38314-015-0514-0.

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28

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

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29

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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30

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 (2015): 22–27. http://dx.doi.org/10.1365/s40112-015-0978-6.

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32

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

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33

Cho, Eunae, and Yoonhyuk Jung. "Consumers’ understanding of autonomous driving." Information Technology & People 31, no. 5 (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 driv

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34

Cobus, Andreas, Matthias Rick, Laura Sommer, et al. "Optimal Control in Autonomous Driving." PAMM 17, no. 1 (2017): 783–84. http://dx.doi.org/10.1002/pamm.201710359.

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35

Devi, T. Kirthiga, Akshat Srivatsava, Kritesh Kumar Mudgal, Ranjnish Raj Jayanti, and T. Karthick. "Behaviour Cloning for Autonomous Driving." Webology 17, no. 2 (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

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36

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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37

Yang, Diange, Xinyu Jiao, Kun Jiang, and Zhong Cao. "Driving Space for Autonomous Vehicles." Automotive Innovation 2, no. 4 (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 o

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38

Solomon, Andreea, and Ronald Kaempf. "Testing Solutions for Autonomous Driving." ATZ worldwide 119, no. 9 (2017): 56–59. http://dx.doi.org/10.1007/s38311-017-0085-1.

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39

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

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40

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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41

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

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42

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 (2018): 3872–82. http://dx.doi.org/10.1109/tits.2018.2797308.

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43

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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44

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 (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-follo

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45

Goadsby, Peter J. "Cluster headache and the trigeminal-autonomic reflex: Driving or being driven?" Cephalalgia 38, no. 8 (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 (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

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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

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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 (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 (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

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Sini, Jacopo, Antonio Costantino Marceddu, and Massimo Violante. "Automatic Emotion Recognition for the Calibration of Autonomous Driving Functions." Electronics 9, no. 3 (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: differen

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