Abstract: To address the critical challenge of risk perception and assessment for autonomous vehicles in dynamic interactive environments, this study proposes a semi-supervised spatiotemporal interaction risk cognition network with attention mechanism (SS-SIRCN), inspired by the behavioral adaptation patterns of biological groups under external threats. First, bythoroughly analyzing the dynamic interaction characteristics exhibited by typical biological collectives when exposed torisk, the study reveals the underlying patterns of trajectory changes influenced by external danger. Then, an attentionbased spatiotemporal risk cognition network is designed to establish a mapping between driving behavior features andpotential driving risks.Finally, a semi-supervised learning framework is employed to enable risk assessment for autonomous vehicles using only a small amount of labeled data.Experimental results on real-world vehicle trajectory datasetsdemonstrate that the proposed method achieves a risk prediction accuracy of 90.76%, outperforming other baseline modelsin performance.

Key words: Escape behaviour, Predator avoidance, Brain-like intelligent decision-making, Attention mechanism, Driving risk, Automated driving')">Escape behaviour, Predator avoidance, Brain-like intelligent decision-making, Attention mechanism, Driving risk, Automated driving