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Journal articles on the topic 'Highly automated driving'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Bartels, Arne, Thanh-Binh To, Simon Karrenberg, and Andreas Weiser. "Highly Automated Driving on Motorways." ATZ worldwide eMagazine 113, no. 9 (February 9, 2011): 28–33. http://dx.doi.org/10.1365/s38311-011-0086-4.

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2

Mazzega, Jens, Frank Köster, Karsten Lemmer, and Thomas Form. "Testing of Highly Automated Driving Functions." ATZ worldwide 118, no. 10 (September 27, 2016): 44–48. http://dx.doi.org/10.1007/s38311-016-0101-x.

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3

Navarro, Jordan, and Catherine Gabaude. "Human factors perspectives on highly automated driving." Le travail humain 83, no. 4 (2020): 285. http://dx.doi.org/10.3917/th.834.0285.

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4

Junietz, Philipp, Udo Steininger, and Hermann Winner. "Macroscopic Safety Requirements for Highly Automated Driving." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 3 (February 21, 2019): 1–10. http://dx.doi.org/10.1177/0361198119827910.

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The common expectation for highly automated vehicles (HAVs) is that an introduction will lead to increased road safety and a reduction in traffic fatalities—at least in relation to the mileage. However, quantizing the safety requirements is still in discussion. This paper analyzes the risk acceptance in other fields and applies the safety level on today’s traffic to derive references for acceptable risks. The focus is on macroscopic safety requirements, meaning accident rates per mileage, and not the behavior in individual driving situations. It was concluded that the acceptable risk varies according to the group involved and with the field share of automated vehicles. Increased safety of conventional driving in the future could lead to higher requirements as well. We also point out that it is not guaranteed that the given acceptable risk levels will also accepted by the user, because factors other than the accident statistics are relevant. However, as none of these risk levels can be proven before introduction, the monitoring of vehicles in the field is suggested. Despite increased research efforts in safety validation, uncertainty surrounding the safety of HAVs will remain at the time of introduction. Different introduction and risk management strategies are briefly introduced.

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5

Kämpchen, Nico, Michael Aeberhard, Michael Ardelt, and Sebastian Rauch. "Technologies for highly automated driving on highways." ATZ worldwide 114, no. 6 (June 2012): 34–38. http://dx.doi.org/10.1007/s38311-012-0176-y.

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6

Kerschbaum, Philipp, Lutz Lorenz, and Klaus Bengler. "Highly automated driving with a decoupled steering wheel." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 58, no. 1 (September 2014): 1686–90. http://dx.doi.org/10.1177/1541931214581352.

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7

Navarro, Jordan. "A state of science on highly automated driving." Theoretical Issues in Ergonomics Science 20, no. 3 (February 22, 2018): 366–96. http://dx.doi.org/10.1080/1463922x.2018.1439544.

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8

Heitz, Thomas, Arne Schacht, Tim Bayer, and Daniel Kreutz. "Steering Concepts for Highly Automated and Autonomous Driving." ATZ worldwide 120, no. 11 (October 26, 2018): 18–23. http://dx.doi.org/10.1007/s38311-018-0154-0.

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9

Henriques, Bernardo, Thomas Mauthner, Gernot Hasenbichler, and Indula Amarasinghe. "Garbage Collection Vehicles with Highly Automated Driving Features." ATZheavy duty worldwide 14, no. 2 (June 2021): 10–15. http://dx.doi.org/10.1007/s41321-021-0419-1.

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10

Skottke, Eva-Maria, Günter Debus, Lei Wang, and Lynn Huestegge. "Carryover Effects of Highly Automated Convoy Driving on Subsequent Manual Driving Performance." Human Factors: The Journal of the Human Factors and Ergonomics Society 56, no. 7 (March 4, 2014): 1272–83. http://dx.doi.org/10.1177/0018720814524594.

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11

Gold, Christian, Ilirjan Berisha, and Klaus Bengler. "Utilization of Drivetime – Performing Non-Driving Related Tasks While Driving Highly Automated." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 59, no. 1 (September 2015): 1666–70. http://dx.doi.org/10.1177/1541931215591360.

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12

Vogelpohl, Tobias, Matthias Kühn, Thomas Hummel, and Mark Vollrath. "Asleep at the automated wheel—Sleepiness and fatigue during highly automated driving." Accident Analysis & Prevention 126 (May 2019): 70–84. http://dx.doi.org/10.1016/j.aap.2018.03.013.

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13

Feierle, Alexander, Simon Danner, Sarah Steininger, and Klaus Bengler. "Information Needs and Visual Attention during Urban, Highly Automated Driving—An Investigation of Potential Influencing Factors." Information 11, no. 2 (January 25, 2020): 62. http://dx.doi.org/10.3390/info11020062.

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During highly automated driving, the passenger is allowed to conduct non-driving related activities (NDRA) and no longer has to act as a fallback at the functional limits of the driving automation system. Previous research has shown that at lower levels of automation, passengers still wish to be informed about automated vehicle behavior to a certain extent. Due to the aim of the introduction of urban automated driving, which is characterized by high complexity, we investigated the information needs and visual attention of the passenger during urban, highly automated driving. Additionally, there was an investigation into the influence of the experience of automated driving and of NDRAs on these results. Forty participants took part in a driving simulator study. As well as the information presented on the human–machine interface (system status, navigation information, speed and speed limit), participants requested information about maneuvers, reasons for maneuvers, environmental settings and additional navigation data. Visual attention was significantly affected by the NDRA, while the experience of automated driving had no effect. Experience and NDRA showed no significant effect on the need for information. Differences in information needs seem to be due to the requirements of the individual passenger, rather than the investigated factors.

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14

Scharfe-Scherf, Marlene Susanne Lisa, and Nele Russwinkel. "Familiarity and Complexity during a Takeover in Highly Automated Driving." International Journal of Intelligent Transportation Systems Research 19, no. 3 (July 21, 2021): 525–38. http://dx.doi.org/10.1007/s13177-021-00259-0.

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AbstractThis paper shows, how objective complexity and familiarity impact the subjective complexity and the time to make an action decision during the takeover task in a highly automated driving scenario. In the next generation of highly automated driving the driver remains as fallback and has to take over the driving task whenever the system reaches a limit. It is thus highly important to develop an assistance system that supports the individual driver based on information about the drivers’ current cognitive state. The impact of familiarity and complexity (objective and subjective) on the time to make an action decision during a takeover is investigated. To produce replicable driving scenarios and manipulate the independent variables situation familiarity and objective complexity, a driving simulator is used. Results show that the familiarity with a traffic situation as well as the objective complexity of the environment significantly influence the subjective complexity and the time to make an action decision. Furthermore, it is shown that the subjective complexity is a mediator variable between objective complexity/familiarity and the time to make an action decision. Complexity and familiarity are thus important parameters that have to be considered in the development of highly automated driving systems. Based on the presented mediation effect, the opportunity of gathering the drivers’ subjective complexity and adapting cognitive assistance systems accordingly is opened up. The results of this study provide a solid basis that enables an individualization of the takeover by implementing useful cognitive modeling to individualize cognitive assistance systems for highly automated driving.

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15

Wandtner, Bernhard, Nadja Schömig, and Gerald Schmidt. "Effects of Non-Driving Related Task Modalities on Takeover Performance in Highly Automated Driving." Human Factors: The Journal of the Human Factors and Ergonomics Society 60, no. 6 (April 4, 2018): 870–81. http://dx.doi.org/10.1177/0018720818768199.

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Objective: Aim of the study was to evaluate the impact of different non-driving related tasks (NDR tasks) on takeover performance in highly automated driving. Background: During highly automated driving, it is allowed to engage in NDR tasks temporarily. However, drivers must be able to take over control when reaching a system limit. There is evidence that the type of NDR task has an impact on takeover performance, but little is known about the specific task characteristics that account for performance decrements. Method: Thirty participants drove in a simulator using a highly automated driving system. Each participant faced five critical takeover situations. Based on assumptions of Wickens’s multiple resource theory, stimulus and response modalities of a prototypical NDR task were systematically manipulated. Additionally, in one experimental group, the task was locked out simultaneously with the takeover request. Results: Task modalities had significant effects on several measures of takeover performance. A visual-manual texting task degraded performance the most, particularly when performed handheld. In contrast, takeover performance with an auditory-vocal task was comparable to a baseline without any task. Task lockout was associated with faster hands-on-wheel times but not altered brake response times. Conclusion: Results showed that NDR task modalities are relevant factors for takeover performance. An NDR task lockout was highly accepted by the drivers and showed moderate benefits for the first takeover reaction. Application: Knowledge about the impact of NDR task characteristics is an enabler for adaptive takeover concepts. In addition, it might help regulators to make decisions on allowed NDR tasks during automated driving.

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16

Fickenscher, Jörg, Sandra Schmidt, Frank Hannig, Mohamed Bouzouraa, and Jürgen Teich. "Path Planning for Highly Automated Driving on Embedded GPUs." Journal of Low Power Electronics and Applications 8, no. 4 (October 2, 2018): 35. http://dx.doi.org/10.3390/jlpea8040035.

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The sector of autonomous driving gains more and more importance for the car makers. A key enabler of such systems is the planning of the path the vehicle should take, but it can be very computationally burdensome finding a good one. Here, new architectures in ECU are required, such as GPU, because standard processors struggle to provide enough computing power. In this work, we present a novel parallelization of a path planning algorithm. We show how many paths can be reasonably planned under real-time requirements and how they can be rated. As an evaluation platform, an Nvidia Jetson board equipped with a Tegra K1 SoC was used, whose GPU is also employed in the zFAS ECU of the AUDI AG.

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17

Kühn, Wolfgang, Michael Müller, and Tom Höppner. "Road Data as Prior Knowledge for Highly Automated Driving." Transportation Research Procedia 27 (2017): 222–29. http://dx.doi.org/10.1016/j.trpro.2017.12.011.

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18

Merat, Natasha, A. Hamish Jamson, Frank C. H. Lai, and Oliver Carsten. "Highly Automated Driving, Secondary Task Performance, and Driver State." Human Factors: The Journal of the Human Factors and Ergonomics Society 54, no. 5 (April 5, 2012): 762–71. http://dx.doi.org/10.1177/0018720812442087.

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19

Vanholme, Benoit, Dominique Gruyer, Benoit Lusetti, Sébastien Glaser, and Saïd Mammar. "Highly Automated Driving on Highways Based on Legal Safety." IEEE Transactions on Intelligent Transportation Systems 14, no. 1 (March 2013): 333–47. http://dx.doi.org/10.1109/tits.2012.2225104.

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20

Takacs, Arpad, Imre Rudas, Dominik Bosl, and Tamas Haidegger. "Highly Automated Vehicles and Self-Driving Cars [Industry Tutorial]." IEEE Robotics & Automation Magazine 25, no. 4 (December 2018): 106–12. http://dx.doi.org/10.1109/mra.2018.2874301.

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21

Perner, Marcus, Martin Gebhardt, and Simon Heine. "Control Concepts as Fallback Solution for Highly Automated Driving." ATZ worldwide 122, no. 5 (April 24, 2020): 26–29. http://dx.doi.org/10.1007/s38311-020-0229-6.

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22

Heerwagen, Mathias. "Legal Limitations Highly Automated Driving Functions Put on Standby." ATZelektronik worldwide 13, no. 6 (December 2018): 8–13. http://dx.doi.org/10.1007/s38314-018-0073-2.

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23

Heerwagen, Mathias. "Legal Limitations Highly Automated Driving Functions Put on Standby." ATZ worldwide 120, no. 11 (October 26, 2018): 10–15. http://dx.doi.org/10.1007/s38311-018-0149-x.

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24

Bock, Julian, Lennart Vater, Robert Krajewski, and Tobias Moers. "Highly Accurate Scenario and Reference Data for Automated Driving." ATZ worldwide 123, no. 5-6 (May 2021): 50–55. http://dx.doi.org/10.1007/s38311-021-0668-8.

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25

Li, Shihuan, and Lei Wang. "Independent wheel control system design for highly automated driving." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 12 (March 29, 2021): 3101–18. http://dx.doi.org/10.1177/09544070211006525.

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For L4 and above autonomous driving levels, the automatic control system has been redundantly designed, and a new steering control method based on brake has been proposed; a new dual-track model has been established through multiple driving tests. The axle part of the model was improved, the accuracy of the transfer function of the model was verified again through acceleration-slide tests; a controller based on interference measurement was designed on the basis of the model, and the relationships between the controller parameters was discussed. Through the linearization of the controller, the robustness of uncertain automobile parameters is discussed; the control scheme is tested and verified through group driving test, and the results prove that the accuracy and precision of the controller meet the requirements, the robustness stability is good. Moreover, the predicted value of the model fits well with the actual observation value, the proposal of this method provides a new idea for avoiding car out of control.

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26

Yoon, Sol Hee, and Yong Gu Ji. "Non-driving-related tasks, workload, and takeover performance in highly automated driving contexts." Transportation Research Part F: Traffic Psychology and Behaviour 60 (January 2019): 620–31. http://dx.doi.org/10.1016/j.trf.2018.11.015.

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27

Strand, Niklas, Josef Nilsson, I. C. MariAnne Karlsson, and Lena Nilsson. "Semi-automated versus highly automated driving in critical situations caused by automation failures." Transportation Research Part F: Traffic Psychology and Behaviour 27 (November 2014): 218–28. http://dx.doi.org/10.1016/j.trf.2014.04.005.

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28

Emirler, Mümin Tolga, İsmail Meriç Can Uygan, Bilin Aksun Güvenç, and Levent Güvenç. "Robust PID Steering Control in Parameter Space for Highly Automated Driving." International Journal of Vehicular Technology 2014 (February 4, 2014): 1–8. http://dx.doi.org/10.1155/2014/259465.

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This paper is on the design of a parameter space based robust PID steering controller. This controller is used for automated steering in automated path following of a midsized sedan. Linear and nonlinear models of this midsized sedan are presented in the paper. Experimental results are used to validate the longitudinal and lateral dynamic models of this vehicle. This paper is on automated steering control and concentrates on the lateral direction of motion. The linear model is used to design a PID steering controller in parameter space that satisfies D-stability. The PID steering controller that is designed is used in a simulation study to illustrate the effectiveness of the proposed method. Simulation results for a circular trajectory and for a curved trajectory are presented and discussed in detail. This study is part of a larger research effort aimed at implementing highly automated driving in a midsized sedan.

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29

Chu, Liang, Yanwu Xu, Di Zhao, and Cheng Chang. "Research on Pressure Control Algorithm of Regenerative Braking System for Highly Automated Driving Vehicles." World Electric Vehicle Journal 12, no. 3 (August 10, 2021): 112. http://dx.doi.org/10.3390/wevj12030112.

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Conclusive evidence has demonstrated the critical importance of highly automated driving systems and regenerative braking systems in improving driving safety and economy. However, the traditional regenerative braking system cannot be applied to highly automated driving vehicles. Therefore, this paper proposes a fully decoupled regenerative braking system for highly automated driving vehicles, which has two working modes: conventional braking and redundant braking. Aimed at the above two working modes, this paper respectively proposes the pressure control algorithm, based on P-V characteristics, and the pressure control algorithm, based on the overflow characteristics of the solenoid valve. AMESim is utilized as the simulation platform, and then is co-simulated with MATLAB/Simulink, which is embedded with the control algorithm. The simulation results show the feasibility and effectiveness of the regenerative braking system and the pressure control algorithm.

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30

Ritschel, Robert, Frank Schrödel, Juliane Hädrich, and Jens Jäkel. "Nonlinear Model Predictive Path-Following Control for Highly Automated Driving." IFAC-PapersOnLine 52, no. 8 (2019): 350–55. http://dx.doi.org/10.1016/j.ifacol.2019.08.112.

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31

Wörle, Johanna, Barbara Metz, Ina Othersen, and Martin Baumann. "Sleep in highly automated driving: Takeover performance after waking up." Accident Analysis & Prevention 144 (September 2020): 105617. http://dx.doi.org/10.1016/j.aap.2020.105617.

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Kondo, Ryo, Takahiro Wada, and Kohei Sonoda. "Use of Haptic Shared Control in Highly Automated Driving Systems." IFAC-PapersOnLine 52, no. 19 (2019): 43–48. http://dx.doi.org/10.1016/j.ifacol.2019.12.084.

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33

Wagner, Johannes, and Jürgen Häring. "Validation of Highly Automated Driving Functions with Cloud-based Simulation." ATZ worldwide 122, no. 1 (December 27, 2019): 50–54. http://dx.doi.org/10.1007/s38311-019-0168-2.

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34

Kremer, Markus, Sébastien Christiaens, Christian Granrath, and Max-Arno Meyer. "Scenario- and Model-based Systems Engineering for Highly Automated Driving." ATZ worldwide 122, no. 12 (November 27, 2020): 16–21. http://dx.doi.org/10.1007/s38311-020-0330-x.

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35

Weiß, Gereon, Philipp Schleiß, and Christian Drabek. "Fail-operational E/E Architecture for Highly-automated Driving Functions." ATZelektronik worldwide 11, no. 3 (June 2016): 16–21. http://dx.doi.org/10.1007/s38314-016-0032-8.

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36

Schrödel, Frank, Jonas Gutsche, Rico Baumgart, and Tim Alscher. "Optimized Driving Strategies for Energy Efficiency in Highly Automated Vehicles." ATZelectronics worldwide 15, no. 5 (May 2020): 44–47. http://dx.doi.org/10.1007/s38314-020-0184-4.

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37

Yoon, Sol Hee, Seul Chan Lee, and Yong Gu Ji. "Modeling takeover time based on non-driving-related task attributes in highly automated driving." Applied Ergonomics 92 (April 2021): 103343. http://dx.doi.org/10.1016/j.apergo.2020.103343.

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38

Riegler, Andreas, Philipp Wintersberger, Andreas Riener, and Clemens Holzmann. "Augmented Reality Windshield Displays and Their Potential to Enhance User Experience in Automated Driving." i-com 18, no. 2 (August 27, 2019): 127–49. http://dx.doi.org/10.1515/icom-2018-0033.

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Abstract Increasing vehicle automation presents challenges as drivers of highly automated vehicles become more disengaged from the primary driving task. However, even with fully automated driving, there will still be activities that require interfaces for vehicle-passenger interactions. Windshield displays are a technology with a promising potential for automated driving, as they are able to provide large content areas supporting drivers in non-driving related activities. However, it is still unknown how potential drivers or passengers would use these displays. This work addresses user preferences for windshield displays in automated driving. Participants of a user study (N=63) were presented two levels of automation (conditional and full), and could freely choose preferred positions, content types, as well as size, transparency levels and importance levels of content windows using a simulated “ideal” windshield display. We visualized the results in form of heatmap data which show that user preferences differ with respect to the level of automation, age, gender, or environment aspects. These insights can help designers of interiors and in-vehicle applications to provide a rich user experience in highly automated vehicles.

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39

Strle, Gregor, Yilun Xing, Erika E. Miller, Linda Ng Boyle, and Jaka Sodnik. "Take-Over Time: A Cross-Cultural Study of Take-Over Responses in Highly Automated Driving." Applied Sciences 11, no. 17 (August 28, 2021): 7959. http://dx.doi.org/10.3390/app11177959.

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The article presents a cross-cultural study of take-over performance in highly automated driving. As take-over performance is an important measure of safe driving, potential cultural differences could have important implications for the future development of automated vehicles. The study was conducted in two culturally different locations, Seattle, WA (n = 20) and Ljubljana, Slovenia (n = 18), using a driving simulator. While driving, participants voluntarily engaged in secondary tasks. The take-over request (TOR) was triggered at a specific time during the drive, and take-over time and type of response (none, brake, steer) were measured for each participant. Results show significant differences in take-over performance between the two locations. In Seattle 30% of participants in Seattle did not respond to TOR; the remaining 70% responded by braking only, compared to Slovenian participants who all responded by either braking or steering. Participants from Seattle responded significantly more slowly to TOR (M = +1285 ms) than Slovenian participants. Secondary task engagement at TOR also had an effect, with distracted US participants’ response taking significantly longer (M = +1596 ms) than Slovenian participants. Reported differences in take-over performance may indicate cultural differences in driving behavior and trust in automated driving.

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40

Brandenburg, Stefan, and Sandra Epple. "Drivers’ Individual Design Preferences of Takeover Requests in Highly Automated Driving." i-com 18, no. 2 (August 27, 2019): 167–78. http://dx.doi.org/10.1515/icom-2018-0028.

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Abstract Highly automated cars will be on the worlds’ roads within the next decade. In highly automated driving the vehicle’s lateral and longitudinal controls can be passed on from the driver to the vehicle and back again. The design of a vehicle’s take-over requests will largely determine the driver’s performance after taking back vehicle control. In the scope of this paper, potential drivers of highly automated cars were asked about their preferences regarding the human-machine interface design of take-over requests. Participants were asked to evaluate eight different take-over requests that differed with respect to (a) take-over request procedure (one-step or two-step procedure), (b) visual take-over request modality (text or text and pictogram), and (c) auditory take-over request modality (tone or speech). Results showed that participants preferred a two-step procedure using text and speech to communicate take-over requests. A subsequent conjoint analysis revealed that take-over requests ideally use speech output in a two-step procedure. Finally, a detailed evaluation showed that the best take-over request interface received significantly higher user experience ratings regarding product characteristics as well as users’ emotions and consequences of product use than the worst take-over request interface. Results are related to the background literature and practical implications are discussed.

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41

Zhou, Huiping, Makoto Itoh, and Satoshi Kitazaki. "Does Adaptive Mode Transition Contribute to Better Driver Intervention in Highly Automated Driving?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 287–91. http://dx.doi.org/10.1177/1071181319631205.

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This paper presents an adaptive mode (level) transition in highly combined driving automation in which the mode of a system could adaptively shift to any level including SAE level 3 (conditional automation, CA) to level 2 (partial automation) based on the driving environment. We show the effects of the adaptive transition on the take over of car control by a human driver and driving behavior after intervention when the system issues a response to intervene. A driving simulator experiment is conducted to collect data during the transition from automated control to manual driving in three scenes: obstacle on a driving lane, blurred lane mark, and stopped car ahead. Results indicate that the interventions of drivers who experience the adaptive transition are delayed in comparison to those who experience only the fixed transition. The adaptive transition is conducive for drivers to stop the car for preventing a potential collision with a stopped car ahead. Owing to the adaptive transition, drivers perceive a critical hazard after taking over car control and provide a rapid response. In addition, during the adaptive transition, drivers prefer verbal messages to the simple “beeping” message.

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42

Dubovsky, V. A., and V. V. Savchenko. "An approach to organizing the transition of vehicle control from an automated driving system to a person." Doklady BGUIR 18, no. 7 (November 25, 2020): 40–46. http://dx.doi.org/10.35596/1729-7648-2020-18-7-40-46.

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The analysis of trends in the development of the automotive industry and well-known methods of automating vehicle control served the basis for us to propose an approach to organizing the transfer of vehicle control from an automated driving system to a person. The approach involves monitoring the vehicle performance and the systems that provide automated driving, the state of the environment and the driver's psychophysiological state, as well as road conditions on the upcoming path, predicting the place and time of transition of control to the driver, determining and regulating his/her readiness to take control if necessary. This approach is peculiar for in time of automated driving, the minimum level of the driver's readiness to operate the vehicle is constantly maintained, which is brought to optimal within a certain time before the scheduled transition to manual control. This two-level monitoring of the condition of drivers of highly automated vehicles will improve road safety both in cases of predicted and unexpected need for an emergency transition from automated to manual driving. The aim of the work is to develop a methodology for improving road safety with highly automated vehicles involved.

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43

Metz, Barbara, Johanna Wörle, Michael Hanig, Marcus Schmitt, and Aaron Lutz. "Repeated Usage of an L3 Motorway Chauffeur: Change of Evaluation and Usage." Information 11, no. 2 (February 18, 2020): 114. http://dx.doi.org/10.3390/info11020114.

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Most studies on users’ perception of highly automated driving functions are focused on first contact/single usage. Nevertheless, it is expected that with repeated usage, acceptance and usage of automated driving functions might change this perception (behavioural adaptation). Changes can occur in drivers’ evaluation, in function usage and in drivers’ reactions to take-over situations. In a driving simulator study, N = 30 drivers used a level 3 (L3) automated driving function for motorways during six experimental sessions. They were free to activate/deactivate that system as they liked and to spend driving time on self-chosen side tasks. Results already show an increase of experienced trust and safety, together with an increase of time spent on side tasks between the first and fourth sessions. Furthermore, attention directed to the road decreases with growing experience with the system. The results are discussed with regard to the theory of behavioural adaptation. Results indicate that the adaptation of acceptance and usage of the highly automated driving function occurs rather quickly. At the same time, no behavioural adaptation for the reaction to take-over situations could be found.

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44

Monkhouse, Helen E., Ibrahim Habli, and John McDermid. "An enhanced vehicle control model for assessing highly automated driving safety." Reliability Engineering & System Safety 202 (October 2020): 107061. http://dx.doi.org/10.1016/j.ress.2020.107061.

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45

Cohen-Lazry, Guy, Nuphar Katzman, Avinoam Borowsky, and Tal Oron-Gilad. "Directional tactile alerts for take-over requests in highly-automated driving." Transportation Research Part F: Traffic Psychology and Behaviour 65 (August 2019): 217–26. http://dx.doi.org/10.1016/j.trf.2019.07.025.

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46

Schömig, Nadja, Volker Hargutt, Alexandra Neukum, Ina Petermann-Stock, and Ina Othersen. "The Interaction Between Highly Automated Driving and the Development of Drowsiness." Procedia Manufacturing 3 (2015): 6652–59. http://dx.doi.org/10.1016/j.promfg.2015.11.005.

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Eriksson, Alexander, and Neville A. Stanton. "Driving Performance After Self-Regulated Control Transitions in Highly Automated Vehicles." Human Factors: The Journal of the Human Factors and Ergonomics Society 59, no. 8 (September 13, 2017): 1233–48. http://dx.doi.org/10.1177/0018720817728774.

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Petermeijer, Sebastiaan M., Joost C. F. de Winter, and Klaus J. Bengler. "Vibrotactile Displays: A Survey With a View on Highly Automated Driving." IEEE Transactions on Intelligent Transportation Systems 17, no. 4 (April 2016): 897–907. http://dx.doi.org/10.1109/tits.2015.2494873.

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Zhou, Feng, Areen Alsaid, Mike Blommer, Reates Curry, Radhakrishnan Swaminathan, Dev Kochhar, Walter Talamonti, Louis Tijerina, and Baiying Lei. "Driver fatigue transition prediction in highly automated driving using physiological features." Expert Systems with Applications 147 (June 2020): 113204. http://dx.doi.org/10.1016/j.eswa.2020.113204.

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Eriksson, Alexander, and Neville A. Stanton. "Takeover Time in Highly Automated Vehicles: Noncritical Transitions to and From Manual Control." Human Factors: The Journal of the Human Factors and Ergonomics Society 59, no. 4 (January 26, 2017): 689–705. http://dx.doi.org/10.1177/0018720816685832.

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Abstract:

Objective: The aim of this study was to review existing research into driver control transitions and to determine the time it takes drivers to resume control from a highly automated vehicle in noncritical scenarios. Background: Contemporary research has moved from an inclusive design approach to adhering only to mean/median values when designing control transitions in automated driving. Research into control transitions in highly automated driving has focused on urgent scenarios where drivers are given a relatively short time span to respond to a request to resume manual control. We found a paucity in research into more frequent scenarios for control transitions, such as planned exits from highway systems. Method: Twenty-six drivers drove two scenarios with an automated driving feature activated. Drivers were asked to read a newspaper, or to monitor the system, and to relinquish, or resume, control from the automation when prompted by vehicle systems. Results: Significantly longer control transition times were found between driving with and without secondary tasks. Control transition times were substantially longer than those reported in the peer-reviewed literature. Conclusion: We found that drivers take longer to resume control when under no time pressure compared with that reported in the literature. Moreover, we found that drivers occupied by a secondary task exhibit larger variance and slower responses to requests to resume control. Workload scores implied optimal workload. Application: Intra- and interindividual differences need to be accommodated by vehicle manufacturers and policy makers alike to ensure inclusive design of contemporary systems and safety during control transitions.

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