Vehicle Lane-Changing scenario generation using time-series generative adversarial networks with an Adaptative parameter optimization strategy

Author:

Li YeORCID,Zeng Fanming,Han Chunyang,Feng ShuoORCID

Publisher

Elsevier BV

Reference63 articles.

1. Administration, N.H.T.S., 2018. Traffic safety facts annual report tables. National Highway Traffic Safety Administration.

2. Akagi, Y., Kato, R., Kitajima, S., Antona-Makoshi, J., Uchida, N., Year. A risk-index based sampling method to generate scenarios for the evaluation of automated driving vehicle safety *. In: Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

3. Brophy, E., Wang, Z., Ward, T.E., 2019. Quick and easy time series generation with established image-based gans.

4. Adversarial evaluation of autonomous vehicles in lane-change scenarios;Chen;IEEE Trans. Intell. Transp. Syst.,2021

5. Using vehicular trajectory data to explore risky factors and unobserved heterogeneity during lane-changing;Chen;Accid. Anal. Prev.,2020

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