融合学习与优化的高速路径规划框架，兼顾安全与实时性。
Reliable and Real-Time Highway Trajectory Planning via Hybrid Learning-Optimization Frameworks
- 用学习模块生成适应交通的速度曲线，优化模块确保安全决策
- 在HighD数据集上成功率超97%，单次规划仅需约54毫秒
- 适合自动驾驶系统开发人员关注实时安全路径规划
自动驾驶高速行驶面临反应时间短、罕见但危险事件频发的问题，对路径规划的可靠性与计算效率提出极高要求。本文提出一种混合高速路径规划（H-HTP）框架，结合基于学习的自适应能力与基于优化的形式化安全保证。核心设计是分工明确：学习模块生成交通适应性速度曲线，所有安全关键决策（如避撞、运动学可行性）由混合整数二次规划（MIQP）负责，确保无论多车交互多复杂，形式化安全约束始终被强制执行。通过车辆几何的线性化策略，显著减少整数变量数量，实现无需牺牲安全性的实时优化。在HighD数据集上的实验表明，H-HTP在安全关键场景下成功率达97%以上，平均规划周期约为54毫秒，能稳定生成平滑、运动学可行且无碰撞的轨迹。
原文摘要 · Abstract (English)
Autonomous highway driving involves high-speed safety risks due to limited reaction time, where rare but dangerous events may lead to severe consequences. This places stringent requirements on trajectory planning in terms of both reliability and computational efficiency. This paper proposes a hybrid highway trajectory planning (H-HTP) framework that integrates learning-based adaptability with optimization-based formal safety guarantees. The key design principle is a deliberate division of labor: a learning module generates a traffic-adaptive velocity profile, while all safety-critical decisions including collision avoidance and kinematic feasibility are delegated to a Mixed-Integer Quadratic Program (MIQP). This design ensures that formal safety constraints are always enforced, regardless of the complexity of multi-vehicle interactions. A linearization strategy for the vehicle geometry substantially reduces the number of integer variables, enabling real-time optimization without sacrificing formal safety guarantees. Experiments on the HighD dataset demonstrate that H-HTP achieves a scenario success rate above 97% with an average planning-cycle time of approximately 54 ms, reliably producing smooth, kinematically feasible, and collision-free trajectories in safety-critical highway scenarios.
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