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<p>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, by thoroughly analyzing the dynamic interaction characteristics exhibited by typical biological collectives when exposed to risk, the study reveals the underlying patterns of trajectory changes influenced by external danger. Then, an attention-based spatiotemporal risk cognition network is designed to establish a mapping between driving behavior features and potential 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 datasets demonstrate that the proposed method achieves a risk prediction accuracy of 90.76%, outperforming other baseline models in performance.</p>

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Semi-supervised Risk Assessment Research for Intelligent Vehicles Inspired by Collective Biological Risk-avoidance Behaviors

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Hongyu Hu,
  • Zhonghua Xiong,
  • Zhengyi Li,
  • Tianjun Sun,
  • Rui Ran

摘要

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, by thoroughly analyzing the dynamic interaction characteristics exhibited by typical biological collectives when exposed to risk, the study reveals the underlying patterns of trajectory changes influenced by external danger. Then, an attention-based spatiotemporal risk cognition network is designed to establish a mapping between driving behavior features and potential 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 datasets demonstrate that the proposed method achieves a risk prediction accuracy of 90.76%, outperforming other baseline models in performance.