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Guocong Zhai

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#reinforcement learning Open access Sep 2026

Generating realistic safety–critical scenarios for vehicle–pedestrian interactions

Automated driving system (ADS) deployment requires rigorous validation across safety–critical vehicle–pedestrian interactions, yet real-world datasets rarely capture high-risk scenarios while simulation platforms lack realistic behavior. In response, this study proposes a three-stage framework that combines real-world...

Qian Pu, Kun Xie, Yuan Zhu et al. · 0 citations

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