Abstract
Vehicle automation is expected for future society. Automated driving systems will be implemented in mobility services including public transport. In this study, we focus on automation for mobility service and don’t deal with automation for privately owned vehicles. If level 4 automation is used, no drivers on board can be realized. Considering automated vehicle services, level 4 automated vehicles should be monitored by remote operators, and remote operators are expected for managing automated vehicles. One operator should manage more than two vehicles for the cost benefit. On managing or operating, there are interactions between a remote operator and an automated driving system. Human factor issues and adequate human machine interface between a remote operator and the system should be considered. Thus, we exposed the issues related to human factor between them by categorizing the number of vehicles. We are working on social implementation of automated vehicle services, which has been supported by the government in Japan. Also, we have a plan to do the experiments with real automated bus and a remote operation system, which were developed in order to clarify these human factors.
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References
US. DOT. http://www.its.dot.gov/automated_vehicle/index.htm. Accessed 23 May 2022
ITS Japan. http://www.its-jp.org/english/files/2015/04/SIP_Worlshop2015_leaflets_e_20150326.pdf. Accessed 23 May 2022
European Commission. IOT Large Scale Pilot 5 Autonomous Vehicles in a Connected Environment. https://www.ertrac.org/uploads/documents_publications/2015%20ART%20Info%20Day/IoTPilot-autonomous%20vehicle-nov5.pdf. Accessed 23 May 2022
Aeberhard, M., et al.: Experience, results and lessons learned from automated driving on Germany’s highways. IEEE Intell. Transp. Syst. Mag. 7, 42–57 (2015)
Shladover, S.: Challenges to evaluation of CO2 impacts of intelligent transportation systems. In: Presented at 2011 IEEE integrated and sustainable transportation system, Vienna (Austria) (2011)
Tsugawa, S., Jeschke, S., Shladover, S.: A review of truck platooning projects for energy savings. IEEE Trans. Intell. Veh. 1, 68–77 (2016)
Hashimoto, N., Takinami, Y., Yamamoto, M.: Experimental study on different types of curves for ride comfort in automated vehicles. J. Adv. Transp. (2021)
Hashimoto, N., Thompson, S., Kato, S., Boyali, A., Tsugawa, S.: Necessity of automated vehicle control customization: Experimental results during lane changing. Transportation Research Record 2672 (22), 1–9
NEDO. SIP Automated Driving for Universal Services (SIP-adus) R&D Plan, https://www.nedo.go.jp/content/100887563.pdf. Accessed 15 Apr 2022
SAE J3016TM levels of driving automation. https://www.sae.org/binaries/content/assets/cm/content/blog/sae-j3016-visual-chart_5.3.21.pdf. Accessed 15 Apr 2022
Hashimoto, N., et al.: Introduction of prototype of remote type automated vehicle system by using communication between operator and vehicles in real environment. In: Proceedings of 2018 16th International Conference on Intelligent Transportation Systems Telecommunications (ITST) (2018)
Dawson, J., Garikapati, D.: Extending ISO26262 to an operationally complex system. In: 2021 IEEE International Systems Conference (SysCon), pp. 1–7 (2021). https://doi.org/10.1109/SysCon48628.2021.9447146
Neumeier, S., Facchi, C.: Towards a driver support system for teleoperated driving. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 4190-4196 (2019). https://doi.org/10.1109/ITSC.2019.8917244
Xiong, G., Chen, H., Gong, J., Wu, S.: Development and implementation of remote control system for an unmanned heavy tracked vehicle. In: 2007 IEEE Intelligent Vehicles Symposium, pp. 663–667 (2007). https://doi.org/10.1109/IVS.2007.4290192
Juang, R.-T.: The implementation of remote monitoring for autonomous driving.. In: 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), pp. 53–56 (2019). https://doi.org/10.1109/ACIRS.2019.8935978
Rogers, R.D., Monsell, S.: Costs of a predictible switch between simple cognitive tasks. J. Exp. Psychol. Gen. 124(2), 207 (1995)
Suzuki, S.: Visualization of task switching strategy of machine operation. In: 2009 International Conference on Networking, Sensing and Control, pp. 513–518 (2009). https://doi.org/10.1109/ICNSC.2009.4919329
Zhao, G., Liu, Y.-J., Shi, Y.: Real-time assessment of the cross-task mental workload using physiological measures during anomaly detection. IEEE Trans. Hum.-Mach. Syst. 48(2), 149–160 (2018). https://doi.org/10.1109/THMS.2018.2803025
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This project has been supported by Ministry of Economy Trade and Industry, and Ministry of Land Infrastructure Transport and Tourism in Japan.
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Hashimoto, N., Wu, Y., Sato, T. (2022). Human Factor Issues in Remote Operator of Automated Driving System for Services - One Operator to N Units of Automated Vehicles in Automated Vehicle Services -. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2022 – Late Breaking Posters. HCII 2022. Communications in Computer and Information Science, vol 1655. Springer, Cham. https://doi.org/10.1007/978-3-031-19682-9_47
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DOI: https://doi.org/10.1007/978-3-031-19682-9_47
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