混在交通環境における異種動的協調を考慮した意図駆動型車線変更フレームワーク
An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment
人間ドライバーの運転スタイルをリアルタイム認識し、協調スコアとBC・IRL・MPCを組み合わせて、混在交通下での安全で社会的に調和した車線変更を実現する枠組みを提案した。
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著者: Xiaoyun Qiu, Haichao Liu, Yue Pan, Jun Ma, Xinhu Zheng
分類: cs.RO
原文アブストラクト
In mixed-traffic environments, autonomous vehicles (AVs) must interact with heterogeneous human-driven vehicles (HVs) whose intentions and driving styles vary across individuals and scenarios. Such variability introduces uncertainty into lane change interactions, where safety and efficiency critically depend on accurately anticipating surrounding drivers' cooperative responses. Existing methods often oversimplify these interactions by assuming uniform or fixed behavioral patterns. To address this limitation, we propose an intention-driven lane change framework that integrates driving-style recognition with cooperation-aware decision-making and motion-planning. A deep learning-based classifier identifies distinct human driving styles in real time. We then introduce a dual-perspective cooperation score composed of intrinsic style-dependent tendencies and interactive dynamic components, enabling interpretable and adaptive intention prediction and quantitative inference. A decision-making module combines behavior cloning (BC) and inverse reinforcement learning (IRL) to determine lane change feasibility. Later, a coordinated motion-planning architecture integrating IRL-based intention inference with model predictive control (MPC) is established to generate collision-free and socially compliant trajectories. Experiments on the NGSIM dataset show that the proposed decision-making model outperforms representative rule-based and learning-based baselines, achieving 96.98% accuracy in lane change classification. Motion-planning evaluations further demonstrate improved maneuver success and execution stability in mixed-traffic environments. These results validate the effectiveness of structured cooperation modeling for intention-driven autonomous lane changes.
