用学习生成的轨迹优化自动驾驶决策，提升城市驾驶安全性。
Risk-Aware Motion Planning with Learned Trajectory Primitives and Probabilistic Safety Assessment
- 用径向基函数网络生成平滑候选轨迹，减少搜索空间
- 通过概率评估碰撞风险，降低车辆越界次数
- 结合学习与优化，兼顾安全与可解释性，适合城市驾驶
本文提出一种基于径向基函数网络（RBFN）的运动规划框架，用于安全高效的城市场景自动驾驶。该方法将RBFN生成的候选轨迹与解析式碰撞概率评估及基于优化的轨迹精修相结合。网络学习加速度变化最小的轨迹，使模型预测控制（MPC）在更小且动态一致的搜索空间中运行。基于精确的概率风险度量选择运动原型，降低求解复杂度的同时保证安全性和约束满足。在多种城市场景中进行评估，结果表明该方法显著提升了风险感知能力，并减少了车辆越界行为，优于基准方法。该框架将学习生成的轨迹融入优化式运动规划，确保了安全性与可解释性。
原文摘要 · Abstract (English)
This paper presents a radial basis function network (RBFN)-informed motion planning framework for safe and efficient urban autonomous driving. The proposed approach combines RBFN-based candidate trajectory generation with an analytic collision probability assessment and optimization-based trajectory refinement. The network learns jerk-minimal trajectories, enabling the MPC to operate within a reduced and dynamically consistent search space. Candidate motion primitives are selected based on an accurate probabilistic risk measure. This design decreases solver complexity while preserving safety and constraint satisfaction. The framework is evaluated in numerous urban driving scenarios. Results demonstrate improved risk awareness and fewer vehicle-limit violations compared to benchmark methods. The proposed approach integrates learning-based trajectories into optimization-based motion planning, thereby ensuring safety and interpretability.
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