复杂环境下自动驾驶车辆的运动规划研究

作为工业与信息社会的代表性产物，汽车自诞生以来便极大地提高了交通的运转效率，而自动驾驶技术的出现，则为汽车工业带来了新的技术变革，并为现代智慧城市与智慧交通注入了新的动力。自动驾驶车辆是一个复杂的信息物理系统，其涵盖了多个技术模块与研究领域，而运动规划作为其核心功能之一，直接影响着车辆的行驶质量和效率。
对于不同的复杂道路环境，运动规划需要面对不同的问题形式和任务要求。在结构化的道路环境中，不同车辆存在着驾驶需求的竞争和冲突，这使得运动规划需有效地化解矛盾并提高驾驶的社会效益；在非结构化的道路环境中，复杂约束的耦合以及高度非线性使运动规划问题呈现出高维非凸非线性的特征，这使得如何提高问题的可解性与计算效率成为难点。基于此，本文在结构化与非结构化的不同道路环境中，分别针对单车道匝道汇车、多车道匝道汇车、单车辆避障、多车辆协同这四类典型场景下的运动规划问题展开研究，主要的研究工作如下：

针对结构化道路环境下的单车道匝道汇车问题，通过引入时间价值，构建了车辆包含行驶时间、能源消耗、驾驶舒适度等多项性能指标的经济效益函数。将可转移效用的双人合作博弈论与最优控制结合，通过威胁策略和旁支付平衡不同车辆间的收益并化解冲突，确定了兼顾总体与个体收益的汇车序列，并基于最优控制得到车辆的协同汇车轨迹，最终实现了总体收益最大且个体收益共赢的单车道匝道汇车运动规划算法。不同情形下的仿真实验与对比结果验证了所提出算法能够有效地实现无冲突的协同汇车，并能更好地提高整个交通系统的经济收益。

针对结构化道路环境下的多车道匝道汇车问题，通过控制区域划分将其解耦为主路变道问题与匝道汇车问题。结合多人合作博弈论与数值最优控制，基于焦点均衡与夏普利值提出了收益共赢的车道分配机制，构建了考虑完整运动学约束的协同变道模型，并引入先离线求解后在线查询的策略实时求解该问题。同时建立了优化时间的单车道汇车模型，并与提出的匝道汇车算法融合，进而实现了有效化解主路变道与匝道汇车冲突的多车道匝道汇车运动规划算法。不同情形下的仿真实验与对比结果表明，所提出算法能够在满足实时性的前提下，实现安全高效且社会收益较高的协同变道与汇车。
针对非结构化道路环境下的单车辆避障问题，基于交替方向乘子法将原始复杂问题拆分为两个分别仅包含运动学约束和避障约束的子问题，并基于可行域的几何性质，引入凸可行集算法将仅含避障约束的子问题进一步凸化为一系列的二次规划次子问题。将交替方向乘子法与凸可行集算法结合以弥补各自的短板，进而提出了约束解耦与凸化的单车辆避障运动规划算法，并证明了其收敛性。不同情形的下的仿真实验与对比结果表明，所提出算法仅依赖简单初始解便可求解更为复杂的问题，且在问题可解性与计算效率上具有显著的优势。
针对非结构化道路环境下的多车辆协同问题，通过引入辅助变量和基于一致性问题的交替方向乘子法将原始复杂问题分解为相互解耦且可并行计算的运动学子问题与避障子问题。对于运动学子问题，提出了先求解后整定时间的方法以消除时间耦合，进而将该子问题拆分为不同车辆间可并行求解的次子问题。对于避障子问题，基于副本变量将其也拆分为可并行求解的次子问题，并提出了改进的凸可行集算法与凸化引导策略，实现了对该次子问题的凸化热启动求解。不同问题间的并行迭代求解构建了高效的多车辆协同运动规划算法。不同情形下的仿真实验与对比结果表明，所提出的算法能在兼顾最优性的同时显著提高计算效率。
上述研究成果分别针对结构化环境下的单车道与多车道匝道汇车问题，以及非结构化环境下的单车辆避障与多车辆协同问题，基于博弈论、最优控制、分布式优化、非线性规划等方法，从不同角度提出了可行有效的运动规划算法，为自动驾驶车辆在复杂环境下的智能驾驶提供参考。本文最后对全文研究进行了总结并展望了未来的研究方向。

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