Vulnerable Road Users Overtaking Path Planning on Urban Road considering Individual Driving Styles
Main Article Content
Abstract
In order to reduce the number of traffic accidents related to vulnerable road users (cyclists, pedestrians), recent studies show great interests in developing intelligent technologies for Advanced Driver Assistance Systems. One of the latest functions is navigating the vehicle in a reference trajectory to avoid the possible collision. However, the assistance system may be deactivated due to disagreement of ideal reference path between individuals. In this paper, we proposed the path planning method considering driving styles of drivers for overtaking a moving vulnerable road user on urban road where there is no clear risk of collision. The optimal paths which were generated based on widely studied artificial potential field method are expected to be human-like and generally accepted by drivers. An improved repulsive function was proposed for representing a vulnerable road user walking on the shoulder of the road. The parameters of potential functions for each driving style were identified using avoidance driving data. Then the potential map was created and unique reference paths were calculated with gradient descent method. The proposed method can be a useful integration to Advanced Driver Assistance Systems for gaining driver’s mutual acceptance in approaching and avoiding a moving vulnerable road user.
Keywords
Advanced Driver Assistance Systems (ADAS), driving style, path planning, potential field, vulnerable road user
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
[1] National Public Safety Commission and National Police Agency, Statistics about Road Traffic, Statistics of Japan, Japan, An. Rep. 00130002, Feb. 2021.
[2] S. Nagiri, H. Aoki, M-D. Vu, Q.H. Nguyen Van, S. Itou, A. Hattori, Experimental study on driving behavior of general driver when passing the pedestrian’s side, Trans. Soc. Automot. Eng. Japan, vol. 52, no. 2, pp. 480-485, Mar. 2021.
[3] M-D. Vu, H. Aoki, T. Suzuki et al., An analysis of vulnerable road users overtaking maneuver along the urban road, Appl. Sci., vol. 11, no. 14, pp. 6361, Jul. 2021. https://doi.org/10.3390/app11146361
[4] A. Rasch, G. Panero, C-N. Boda, M. Dozza, How do drivers overtake pedestrians? Evidence from field test and naturalistic driving data, Accid. Anal. Prev., vol. 139, pp. 105494, May 2020. https://doi.org/10.1016/j.aap.2020.105494
[5] N. Noto, H. Okuda, Y. Tazaki, T. Suzuki, Steering assisting system for obstacle avoidance based on personalized potential field, in Proc. IEEE Intell. Trans. Syst. Conf., Alaska, USA, Sep. 16-19, 2012, pp. 1702-1707. https://doi.org/10.1109/ITSC.2012.6338628
[6] K. Ezawa, P. Raksincharoensak, Y. Akagi, K. Maeda, T. Kojima, Study on autonomous braking control system based on motion prediction considering overtaking motion of cyclists, Trans. of the JSME (in Japanese), vol. 84, no. 865, pp. 17-00557, Sep. 2018. https://doi.org/10.1299/transjsme.17-00557
[7] A. Bolovinou, F. Bellotti, A. Amditis, M. Tarkiani, Driving style recognition for co-operative driving: a survey, in Proc. ADAPTIVE 2014, Venice, Italy, May 25-29, 2014, pp. 73-78.
[8] M. Hasenjäger, M. Heckmann and H. Wersing, A survey of personalization for advanced driver assistance systems, IEEE Trans. Intell. Veh., vol. 5, no. 2, pp. 335-344, Jun. 2020. https://doi.org/10.1109/TIV.2019.2955910
[9] H. Aoki, Perceptual risk estimate for collision avoidance by braking and steering, in Proc. FASTzero’11, Tokyo, Japan, Sep. 5-9, 2011.
[10] J.H. Holland, Adaption in Natural and Artificial Systems, Ann Arbor, MI, USA: University of Michigan Press, 1975.
[11] X. Wang, F.J. Hickernell, Randomized Halton sequences, Math. Comput. Model., vol. 32, no. 7-8, pp. 887-899, Oct. 2000. https://doi.org/10.1016/S0895-7177(00)00178-3
[12] A. Lipowski, D. Lipowska, Roulette-wheel selection via stochastic acceptance, Phys. A: Stat. Mech. Appl., vol. 391, no. 6, pp. 22193-2196, Mar. 2012. https://doi.org/10.1016/j.physa.2011.12.004
[2] S. Nagiri, H. Aoki, M-D. Vu, Q.H. Nguyen Van, S. Itou, A. Hattori, Experimental study on driving behavior of general driver when passing the pedestrian’s side, Trans. Soc. Automot. Eng. Japan, vol. 52, no. 2, pp. 480-485, Mar. 2021.
[3] M-D. Vu, H. Aoki, T. Suzuki et al., An analysis of vulnerable road users overtaking maneuver along the urban road, Appl. Sci., vol. 11, no. 14, pp. 6361, Jul. 2021. https://doi.org/10.3390/app11146361
[4] A. Rasch, G. Panero, C-N. Boda, M. Dozza, How do drivers overtake pedestrians? Evidence from field test and naturalistic driving data, Accid. Anal. Prev., vol. 139, pp. 105494, May 2020. https://doi.org/10.1016/j.aap.2020.105494
[5] N. Noto, H. Okuda, Y. Tazaki, T. Suzuki, Steering assisting system for obstacle avoidance based on personalized potential field, in Proc. IEEE Intell. Trans. Syst. Conf., Alaska, USA, Sep. 16-19, 2012, pp. 1702-1707. https://doi.org/10.1109/ITSC.2012.6338628
[6] K. Ezawa, P. Raksincharoensak, Y. Akagi, K. Maeda, T. Kojima, Study on autonomous braking control system based on motion prediction considering overtaking motion of cyclists, Trans. of the JSME (in Japanese), vol. 84, no. 865, pp. 17-00557, Sep. 2018. https://doi.org/10.1299/transjsme.17-00557
[7] A. Bolovinou, F. Bellotti, A. Amditis, M. Tarkiani, Driving style recognition for co-operative driving: a survey, in Proc. ADAPTIVE 2014, Venice, Italy, May 25-29, 2014, pp. 73-78.
[8] M. Hasenjäger, M. Heckmann and H. Wersing, A survey of personalization for advanced driver assistance systems, IEEE Trans. Intell. Veh., vol. 5, no. 2, pp. 335-344, Jun. 2020. https://doi.org/10.1109/TIV.2019.2955910
[9] H. Aoki, Perceptual risk estimate for collision avoidance by braking and steering, in Proc. FASTzero’11, Tokyo, Japan, Sep. 5-9, 2011.
[10] J.H. Holland, Adaption in Natural and Artificial Systems, Ann Arbor, MI, USA: University of Michigan Press, 1975.
[11] X. Wang, F.J. Hickernell, Randomized Halton sequences, Math. Comput. Model., vol. 32, no. 7-8, pp. 887-899, Oct. 2000. https://doi.org/10.1016/S0895-7177(00)00178-3
[12] A. Lipowski, D. Lipowska, Roulette-wheel selection via stochastic acceptance, Phys. A: Stat. Mech. Appl., vol. 391, no. 6, pp. 22193-2196, Mar. 2012. https://doi.org/10.1016/j.physa.2011.12.004