Trajectory optimization for autonomous modular vehicle or platooned autonomous vehicle split operations
Trajectory optimization for autonomous modular vehicle or platooned autonomous vehicle split operations
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DOI：
10.1016/j.tre.2023.103115
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Qianwen Li;Xiaopeng Li
中科院分区：
文献类型：
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作者：
Qianwen Li;Xiaopeng Li
自动模块化车辆（AMV）技术允许根据应用需求在途中灵活调整车辆长度，例如，将多个短车辆停靠到一辆长车辆中，或者相反，将一辆长车辆分成多个较短的车辆。 AMV 对接是自动驾驶车辆 (AV) 编队的极端情况，因为 AMV 之间的物理连接为零间隙。本文研究了排分割作战的轨迹规划。提出了一个两阶段优化问题来设计 AMV 或排式 AV 分割操作轨迹。第一阶段的目标是最大限度地减少分割操作时间，以提高操作效率。第二阶段的目标是最小化加速度平方和，以确定乘坐舒适性和燃油效率的最平滑轨迹。通过扩展时间地理提出了一种可行的锥体方法，以揭示解决方案可行性的理论特性，并分析解决第一阶段问题。提出了基于二次规划的精确求解方法和基于庞特里亚金极大值原理的启发式求解方法来求解第二阶段问题。通过可行域分析，大大减小了第二阶段问题的原始可行域。数值实验表明，启发式求解方法总是能够在不损失或轻微损失操作最优性的情况下瞬时求解第二阶段问题，以满足实时应用的需求，而采用最先进求解器的精确求解方法可能需要更长的求解时间，这可能会给某些实时应用带来挑战。还值得注意的是，在适应减少的可行区域后，精确求解时间显着减少。当启发式解决方案无法达到精确的最佳值时，这对于需要绝对最优的应用程序至关重要。启发式求解方法与定制基准方法之间的比较揭示了启发式求解方法在优化车辆分割轨迹方面的优越性。对关键参数的敏感性分析结果为工程实施提供了管理见解。大量数值实验证明了启发式求解方法在求解考虑加速度和加加速度的另一个轨迹平滑目标函数时的通用性。还讨论了启发式解决方案在大规模应用中以及考虑周围交通时的通用性。
Autonomous modular vehicle (AMV) technology allows for the flexible adjustment of vehicle length en-route per application needs, e.g., docking multiple short vehicles into one long vehicle or, conversely, splitting a long vehicle into multiple shorter ones. AMV docking is an extreme case of autonomous vehicle (AV) platooning in that AMVs are physically connected with zero gaps. This paper studies the trajectory planning for platoon split operations. A two-stage optimization problem is proposed to design AMV or platooned AV split operations trajectories. The first-stage objective minimizes the split operation time duration for operation efficiency. The second-stage objective minimizes the sum of squared acceleration to identify the smoothest trajectories for riding comfort and fuel efficiency.A feasible cone method is proposed by extending the time geography to reveal theoretical properties on the solution feasibility and analytically solve the first-stage problem. An exact solution approach based on quadratic programming and a heuristic solution approach based on Pontryagin's maximum principle are proposed to solve the second-stage problem. The original feasible region of the second-stage problem is greatly reduced through the feasible region analyses.Numerical experiments show that the heuristic solution approach can always solve the second-stage problem instantaneously without any or with a slight loss of the operation optimality to satisfy real-time applications needs, whereas the exact solution approach with a state-of-the-art solver may take a much longer solution time that may impose challenges to certain real-time applications. It is also noted that the exact solution time is significantly reduced after accommodating the reduced feasible region. This is critical for applications requiring absolute optimality when the heuristic solution approach fails to reach the exact optimum. The comparison between the heuristic solution approach and a customized benchmark approach reveals the superiority of the heuristic solution approach in optimizing vehicle split trajectories. Results from sensitivity analyses on key parameters provide managerial insights into engineering implementations. The generalizability of the heuristic solution approach in solving another trajectory smoothing objective function considering both acceleration and jerk is proven by extensive numerical experiments. The generalizability of the heuristic solution approach in large-scale applications and when considering surrounding traffic is also discussed.