用于自动驾驶最优间隙选择与速度规划的多剖面二次规划（MPQP）
Multi-Profile Quadratic Programming (MPQP) for Optimal Gap Selection and Speed Planning of Autonomous Driving
AI总结：
本文提出多剖面二次规划（MPQP）方法，通过广度优先搜索探索时空交通间隙，并用二次规划生成最优速度剖面，实现自动驾驶平滑安全的速度规划，经 Carla 仿真与实车城市道路演示验证。
摘要：
平滑且安全的速度规划对于自动驾驶车辆的成功部署至关重要。本文提出了一种用于自动驾驶最优速度规划的数学公式，该公式已在高保真仿真和真实道路演示中结合实际约束得到验证。该算法利用广度优先搜索在时间和空间域中探索交通流间隙。对于每个间隙，二次规划求解出最优速度剖面，使时间-空间对与动态障碍物同步。文中报告了在 Carla 中的定性与定量分析，以讨论所提算法的平滑性和鲁棒性。最后，我们展示了城市道路驾驶的实车演示结果。
英文摘要：
Smooth and safe speed planning is imperative for the successful deployment of autonomous vehicles. This paper presents a mathematical formulation for the optimal speed planning of autonomous driving, which has been validated in high-fidelity simulations and real-road demonstrations with practical constraints. The algorithm explores the inter-traffic gaps in the time and space domain using a breadth-first search. For each gap, quadratic programming finds an optimal speed profile, synchronizing the time and space pair along with dynamic obstacles. Qualitative and quantitative analysis in Carla is reported to discuss the smoothness and robustness of the proposed algorithm. Finally, we present a road demonstration result for urban city driving.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
