AI 中文总结
本文提出由径向基函数网络指导的运动规划框架，结合RBFN候选轨迹生成、碰撞概率评估与轨迹优化，在城市驾驶场景中提升风险感知并减少车辆限制违规，确保安全与可解释性。
AI 中文摘要
本文提出了一种由径向基函数网络（RBFN）指导的安全高效城市自动驾驶运动规划框架。该方法将基于RBFN的候选轨迹生成、解析碰撞概率评估与基于优化的轨迹优化相结合。该网络学习最小加加速度的轨迹，使模型预测控制（MPC）能在缩减且动态一致的搜索空间内运行。候选运动基元根据精确的概率风险度量进行选择，该设计降低了求解器复杂度，同时保持安全性和约束满足度。该框架在多种城市驾驶场景中进行评估，结果表明与基准方法相比，其风险感知能力提升，车辆限制违规次数减少。该方法将基于学习的轨迹集成到基于优化的运动规划中，从而确保安全性和可解释性。
英文摘要
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.
Comments8 pages, submitted to IEEE ITSC 2026, Naples, Italy
