Exploration of the feasibility of steering wheelless cars based on Robotaxi operation data

Research Article
Open access

Exploration of the feasibility of steering wheelless cars based on Robotaxi operation data

Haolin Bian 1*
  • 1 Queen’s University Canada, Ontario, Kingston, EG3369995    
  • *corresponding author bianhaolin@gmail.com
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

With the development of autonomous driving technology, higher-level autonomous driving is hopeful to be applied in vehicles, and drivers’ control of the car will also be replaced by intelligent algorithms. At the same time, a question has been raised as to whether the traditional steering wheel can be replaced when advanced autonomous driving becomes commonplace. To this end, a survey on steering wheelless cars was conducted to explore the feasibility of autonomous vehicles without the steering wheel. As a result, the operational data of Waymo and Apollo, two Robotaxi (a self-driving car operated by a ridesharing company) companies in the United States and China, is analyzed. The results show that at this stage, autonomous driving cannot fully control the car, and the driver still needs to take over the vehicle in complex situations. Of course, according to the data, Miles per Intervention (MPI) is gradually rising and is expected to reach a reasonable expectation, so steering wheelless cars still have a certain feasibility in the future.

Keywords:

Robotaxi, Self Driving, Wheeless Car, Autonomous Driving, Machine Learning.

Bian,H. (2023). Exploration of the feasibility of steering wheelless cars based on Robotaxi operation data. Applied and Computational Engineering,5,219-223.
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References

[1]. Seppelt, B. D., & Victor, T. W. (2016). Potential solutions to human factors challenges in road vehicle automation. In Road vehicle automation 3(pp. 131-148). Springer, Cham.

[2]. Singh, S. (2015). Critical reasons for crashes investigated in the national motor vehicle crash causation survey(No. DOT HS 812 115).

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[14]. Gao, C., Wang, G., Shi, W., Wang, Z., & Chen, Y. (2021). Autonomous Driving Security: State of the Art and Challenges. IEEE Internet of Things Journal.

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[19]. Giust, F., Sciancalepore, V., Sabella, D., Filippou, M. C., Mangiante, S., Featherstone, W., & Munaretto, D. (2018). Multi-access edge computing: The driver behind the wheel of 5G-connected cars. IEEE Communications Standards Magazine, 2(3), 66-73.

[20]. Zhang, S., Shi, J., Guo, K., & Wang, Y. (2020). Virtual validation method for automated driving vehicles based on traffic accident. In CICTP 2020 (pp. 365-375).

[21]. Gupta, A., Anpalagan, A., Guan, L., & Khwaja, A. S. (2021). Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues. Array, 10, 100057.

[22]. Akintunde, M. E., Kevorchian, A., Lomuscio, A., & Pirovano, E. (2019, July). Verification of RNN-based neural agent-environment systems. In Proceedings of the AAAI Conference on Artificial Intelligence(Vol. 33, No. 01, pp. 6006-6013).

[23]. Pikovsky, A., Osipov, G., Rosenblum, M., Zaks, M., & Kurths, J. (1997). Attractor-repeller collision and eyelet intermittency at the transition to phase synchronization. Physical review letters, 79(1), 47.


Cite this article

Bian,H. (2023). Exploration of the feasibility of steering wheelless cars based on Robotaxi operation data. Applied and Computational Engineering,5,219-223.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN:978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor:Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN:2755-2721(Print) / 2755-273X(Online)

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References

[1]. Seppelt, B. D., & Victor, T. W. (2016). Potential solutions to human factors challenges in road vehicle automation. In Road vehicle automation 3(pp. 131-148). Springer, Cham.

[2]. Singh, S. (2015). Critical reasons for crashes investigated in the national motor vehicle crash causation survey(No. DOT HS 812 115).

[3]. Buttice, C. (2022). Hacking autonomous vehicles: Is this why we don’t have self-. Techopedia.com. Retrieved September 28, 2022, from https://www.techopedia.com/hacking-autonomous-vehicles-is-this-why-we-dont-have-self-driving-cars-yet/2/33650

[4]. Pipkorn, L., Victor, T. W., Dozza, M., & Tivesten, E. (2021). Driver conflict response during supervised automation: Do hands on wheel matter? Transportation research part F: traffic psychology and behaviour, 76, 14-25.

[5]. Abe, G., & Richardson, J. (2004). The effect of alarm timing on driver behaviour: an investigation of differences in driver trust and response to alarms according to alarm timing. Transportation Research Part F: Traffic Psychology and Behaviour, 7(4-5), 307-322.

[6]. Munoz-Castaner, J., Asorey-Cacheda, R., Gil-Castineira, F. J., Gonzalez-Castano, F. J., & Rodriguez-Hernandez, P. S. (2010). A review of aeronautical electronics and its parallelism with automotive electronics. IEEE Transactions on Industrial Electronics, 58(7), 3090-3100.

[7]. Godha, S. (2006). Performance evaluation of low cost MEMS-based IMU integrated with GPS for land vehicle navigation application. UCGE report, (20239).

[8]. Watzenig, D., & Horn, M. (2017). Introduction to automated driving. In Automated Driving(pp. 3-16). Springer, Cham.

[9]. Liu, Y., & Jiang, Q. (2020). Who is Doing Better? — Differences in Innovation Management Between Apollo and Waymo. Frontiers, 1(11).

[10]. Haupt, A. (2021). From Automotive Industry to Robotaxi Industry.

[11]. Korosec, K. (2019, September 16). Waymo’s robotaxi pilot surpassed 6, 200 riders in its first month in California. Tech Crunch. Retrieved September 29, 2022, from https://techcrunch.com/2019/09/16/waymos-robotaxi-pilot-surpassed-6200-riders-in-its-first-month-in-california/?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAM9uO2kW0UGGbDcGnCxrEblxM3FKVyaDltOBpvsA2u6SFdBbQnSGO5iPEFGri8l1-JJHRF2cisLu4d9u-MzyWr7yaHV19eMQJIN3W_byv_1kjRvviKV3LsNT_pFIP1RbKbvlWu7ZUoGURk6IAY_BTlVKse_t3QelUNtFrFp8lMTr

[12]. Schwall, M., Daniel, T., Victor, T., Favaro, F., & Hohnhold, H. (2020). Waymo public road safety performance data. arXiv preprint arXiv:2011.00038.

[13]. Morris, A. P., Haworth, N., Filtness, A., Nguatem, D. P. A., Brown, L., Rakotonirainy, A., & Glaser, S. (2021). Autonomous vehicles and vulnerable road-users — important considerations and requirements based on crash data from two countries. Behavioral Sciences, 11(7), 101.

[14]. Gao, C., Wang, G., Shi, W., Wang, Z., & Chen, Y. (2021). Autonomous Driving Security: State of the Art and Challenges. IEEE Internet of Things Journal.

[15]. Autonomous Vehicles and vulnerable road-users—important considerations ... (n.d.). Retrieved September 29, 2022, from https://www.researchgate.net/publication/353291642_Autonomous_Vehicles_and_Vulnerable_Road-Users-Important_Considerations_and_Requirements_Based_on_Crash_Data_from_Two_Countries

[16]. Media, N. H. T. S. A. (2022, May 17). Newly released estimates show traffic fatalities reached a 16-year high in 2021. NHTSA. Retrieved September 29, 2022, from https://www.nhtsa.gov/press-releases/early-estimate-2021-traffic-fatalities

[17]. Bradsher, K. (2021). China Bets on Electric Car Domination. The New York Times, B1-L.

[18]. Wang, X., Peng, Y., Xu, T., Xu, Q., Wu, X., Xiang, G., ... & Wang, H. (2022). Autonomous driving testing scenario generation based on in-depth vehicle-to-powered two-wheeler crash data in China. Accident Analysis & Prevention, 176, 106812.

[19]. Giust, F., Sciancalepore, V., Sabella, D., Filippou, M. C., Mangiante, S., Featherstone, W., & Munaretto, D. (2018). Multi-access edge computing: The driver behind the wheel of 5G-connected cars. IEEE Communications Standards Magazine, 2(3), 66-73.

[20]. Zhang, S., Shi, J., Guo, K., & Wang, Y. (2020). Virtual validation method for automated driving vehicles based on traffic accident. In CICTP 2020 (pp. 365-375).

[21]. Gupta, A., Anpalagan, A., Guan, L., & Khwaja, A. S. (2021). Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues. Array, 10, 100057.

[22]. Akintunde, M. E., Kevorchian, A., Lomuscio, A., & Pirovano, E. (2019, July). Verification of RNN-based neural agent-environment systems. In Proceedings of the AAAI Conference on Artificial Intelligence(Vol. 33, No. 01, pp. 6006-6013).

[23]. Pikovsky, A., Osipov, G., Rosenblum, M., Zaks, M., & Kurths, J. (1997). Attractor-repeller collision and eyelet intermittency at the transition to phase synchronization. Physical review letters, 79(1), 47.