適応進化型リスク楕円フレームワークによる信頼性と安全性の高い自動運転
Adaptive Evolution Factor Risk Ellipse Framework for Reliable and Safe Autonomous Driving
衝突までの時間と危険時間窓をシグモイド正規化した適応進化係数でリスク楕円を動的に更新し、MPCに統合して周囲車両の不確実な挙動に対応する自動運転手法を提案した。
詳しい要約
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著者: Fujiang Yuan, Zhen Tian, Yangfan He, Guojian Zou, Chunhong Yuan, Yanhong Peng, Zhihao Lin
分類: cs.RO
原文アブストラクト
In recent years, ensuring safety, efficiency, and comfort in interactive autonomous driving has become a critical challenge. Traditional model-based techniques, such as game-theoretic methods and robust control, are often overly conservative or computationally intensive. Conversely, learning-based approaches typically require extensive training data and frequently exhibit limited interpretability and generalizability. Simpler strategies, such as Risk Potential Fields (RPF), provide lightweight alternatives with minimal data demands but are inherently static and struggle to adapt effectively to dynamic traffic conditions. To overcome these limitations, we propose the Evolutionary Risk Potential Field (ERPF), a novel approach that dynamically updates risk assessments in dynamical scenarios based on historical obstacle proximity data. We introduce a Risk-Ellipse construct that combines longitudinal reach and lateral uncertainty into a unified spatial temporal collision envelope. Additionally, we define an adaptive Evolution Factor metric, computed through sigmoid normalization of Time to Collision (TTC) and Time-Window-of-Hazard (TWH), which dynamically adjusts the dimensions of the ellipse axes in real time. This adaptive risk metric is integrated seamlessly into a Model Predictive Control (MPC) framework, enabling autonomous vehicles to proactively address complex interactive driving scenarios in terms of uncertain driving of surrounding vehicles. Comprehensive comparative experiments demonstrate that our ERPF-MPC approach consistently achieves smoother trajectories, higher average speeds, and collision-free navigation, offering a robust and adaptive solution suitable for complex interactive driving environments.
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