Author

Siyuan Liu

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Preprint Oct 2026

Controllable and Photorealistic Pedestrian Risky Motion Generation for End-to-End Driving Safety Evaluation

Evaluating end-to-end autonomous driving under rare, safety-critical vehicle-pedestrian interactions requires photorealistic, sensor-level scenarios. However, trajectory-based scenario generators cannot synthesize raw visual observations, whereas video-based approaches lack controllability. To bridge this gap, we prese...

Si-Yuan Liu, Miao Li, Hai-Bao Yu et al. · 0 citations

Sep 2026

Modeling pedestrian-vehicle interaction risk based on risk perception of experienced drivers in autonomous vehicles.

An expert-guided framework to quantify pedestrian-vehicle interaction risk by learning the takeover behaviors of safety drivers in AVs is developed and validates and models interaction risk based on experienced drivers' risk perception and highlights key factors influencing human risk assessment.

Yiran Luo, Quan Li, Si-Yuan Liu et al. · Traffic Injury Prevention · 0 citations

Jul 2026

Learning social norms enhances compatibility in dynamic human-AI coordination

It is indicated that formalizing tacit social norms into explicit, quantifiable principles can enable AI agents to achieve mutually beneficial coordination in dynamic interactions, supporting their more natural integration into human society.

Yi Yang, Siyuan Liu, Xin Gao et al. · arXiv.org · 0 citations