提出统一框架，让自动驾驶车在复杂城市环境中高效规划路径并实时控制。
aUToPath: Unified Planning and Control for Autonomous Vehicles in Urban Environments Using Hybrid Lattice and Free-Space Search
- 结合预计算格网与动态空域采样生成最优行驶通道
- 用单个优化问题同时生成轨迹与控制指令，提升安全性与可行性
- 实测在密集障碍物中成功率100%，适合高安全要求的自动驾驶系统
本文提出aUToPath，一种统一的在线框架，用于解决复杂城市环境中自动驾驶车辆的全局路径规划与控制问题。核心是新型混合规划器，将预计算的格网地图与动态空域采样结合，高效生成复杂场景下的最优可行驶通道。系统采用基于序列凸规划（SCP）的模型预测控制（MPC），将通道细化为平滑且动力学一致的轨迹。通过单一优化问题同时生成轨迹及其对应的控制命令，克服了传统解耦方法的局限性，确保路径的安全性与可行性。在随机生成的高障碍物场景下，基于自适应启发式树*（AIT*）的空域规划器表现出高成功率，运行时间与格网规划器相当。在雪佛兰Bolt EUV上的真实世界实验进一步验证了系统在密集障碍物中的性能，八次测试均未违反交通、运动学或车辆约束，成功率达到100%。
原文摘要 · Abstract (English)
This paper presents aUToPath, a unified online framework for global path-planning and control to address the challenge of autonomous navigation in cluttered urban environments. A key component of our framework is a novel hybrid planner that combines pre-computed lattice maps with dynamic free-space sampling to efficiently generate optimal driveable corridors in cluttered scenarios. Our system also features sequential convex programming (SCP)-based model predictive control (MPC) to refine the corridors into smooth, dynamically consistent trajectories. A single optimization problem is used to both generate a trajectory and its corresponding control commands; this addresses limitations of decoupled approaches by guaranteeing a safe and feasible path. Simulation results of the novel planner on randomly generated obstacle-rich scenarios demonstrate the success rate of a free-space Adaptively Informed Trees* (AIT*)-based planner, and runtimes comparable to a lattice-based planner. Real-world experiments of the full system on a Chevrolet Bolt EUV further validate performance in dense obstacle fields, demonstrating no violations of traffic, kinematic, or vehicle constraints, and a 100% success rate across eight trials.
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