Autonomous Navigation for Personal Mobility Vehicles Considering Passenger Tolerance to Approaching Pedestrians
Source abstract
Abstract
There are high expectations for autonomous personal mobility vehicles (PMVs) to support the mobility of older people. Autonomous navigation systems are being developed to assist mobility in public areas with mixed pedestrian traffic, such as airports and shopping malls. For autonomous navigation of PMVs, achieving both comfort and efficiency, even in crowded environments, is important. In this study, we focused on the characteristic of passenger tolerance, in which a passenger’s discomfort is relatively small concerning an approaching pedestrian. The objective was to propose an efficient autonomous navigation method without increasing passenger discomfort, considering the characteristics of passenger tolerance. First, the passenger tolerance characteristics were clarified through data analysis of a previous study’s dataset and a newly collected dataset. Next, a path-planning method considering the characteristics was proposed, and the proposed method was evaluated by numerical simulations. The evaluation results showed that the proposed method has a potential to achieve efficient autonomous navigation in crowded environments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Human-Automation Interaction and Safety · Autonomous Vehicle Technology and Safety · Evacuation and Crowd Dynamics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 113-8654, Japan
Department of Mechanical Engineering, School of Engineering, Institute of Science Tokyo, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
References (15)
Show source-provided references
1. Consumer Affairs Agency (2023, December 18). Investigation Report of Accident Causation Based on Article 23, Paragraph 1 of Consumer Safety Act, Available online: https://www.caa.go.jp/policies/council/csic/report/report_009/pdf/report_009_180129_0001.pdf.
2. Niijima, S., Sasaki, Y., and Mizoguchi, H. (2018, January 9–12). Autonomous navigation of electric wheelchairs in urban areas on the basis of self-generated 2D drivable maps. Proceedings of the 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Auckland, New Zealand.10.1109/AIM.2018.8452435 ↗
3. Mulky, R.S., Koganti, S., Shahi, S., and Liu, K. (2018, January 2–7). Autonomous scooter navigation for people with mobility challenges. Proceedings of the 2018 IEEE International Conference on Cognitive Computing (ICCC), San Francisco, CA, USA.10.1109/ICCC.2018.00020 ↗
4. Path planning algorithm for an autonomous electric wheelchair in hospitals10.1109/ACCESS.2020.3038452 ↗
5. Park, J.J., Johnson, C., and Kuipers, B. (2012, January 7–12). Robot navigation with model predictive equilibrium point control. Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, Portugal.10.1109/IROS.2012.6386195 ↗
6. Trautman, P., and Krause, A. (2010, January 18–22). Unfreezing the robot: Navigation in dense, interacting crowds. Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan.10.1109/IROS.2010.5654369 ↗
7. Schockenhoff, F., Nehse, H., and Lienkamp, M. (2020). Maneuver-based objectification of user comfort affecting aspects of driving style of autonomous vehicle concepts. Appl. Sci., 10.10.3390/app10113946 ↗
8. Hall, E.T. (1966). The Hidden Dimension, Doubleday.
9. Evaluation of the effects of a personal mobility vehicle on multiple pedestrians using personal space10.1109/TITS.2014.2388219 ↗
10. Toyoshima, A., Nishino, N., Chugo, D., Muramatsu, S., Yokota, S., and Hashimoto, H. (2018, January 13–15). Autonomous mobile robot navigation: Consideration of the pedestrian’s dynamic personal space. Proceedings of the 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE), Cairns, QLD, Australia.10.1109/ISIE.2018.8433693 ↗
11. Pedestrian’s avoidance behavior characteristics against autonomous personal mobility vehicles for smooth avoidance10.20965/jrm.2024.p0918 ↗
12. Isono, Y., Yoshitake, H., and Shino, M. (2023, January 6–8). Autonomous navigation method considering passenger comfort recognition for personal mobility vehicles in crowded pedestrian spaces. Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics, Virtual Event.10.5220/0010849700003124 ↗
13. Toward a theory of situation awareness in dynamic systems10.1518/001872095779049543 ↗
14. Pedestrian avoidance method considering passenger comfort for autonomous personal mobility vehicles10.20965/jrm.2023.p0231 ↗
15. Social force model with explicit collision prediction10.1209/0295-5075/93/68005 ↗