Abstract
This work considers the problem of optimal lane changing in a structured multi-agent road environment. A novel motion planning algorithm that can capture long-horizon dependencies as well as short-horizon dynamics is presented. Pivotal to our approach is a geometric approximation of the long-horizon combinatorial transition problem which we formulate in the continuous time-space domain. Moreover, a discrete-time formulation of a short-horizon optimal motion planning problem is formulated and combined with the long-horizon planner. Both individual problems, as well as their combination, are formulated as MIQP and solved in real-time by using state-of-the-art solvers. We show how the presented algorithm outperforms two other state-of-the-art motion planning algorithms in closed-loop performance and computation time in lane changing problems. Evaluations are performed using the traffic simulator SUMO, a custom low-level tracking model predictive controller, and high-fidelity vehicle models and scenarios, provided by the CommonRoad environment.
Abstract (translated)
本工作考虑了在结构多代理道路环境中进行最优车道切换的问题。我们提出了一个新颖的运动规划算法，可以捕捉长时依赖关系和短时动态。我们还在连续时间域中形式化了一个几何近似的长时间依赖关系组合问题，这是我们方法的关键。此外，我们还提出了一个离散时间的短期最优运动规划问题，并将其与长期规划器相结合。我们将其组合问题和解决方案都表示为MIQP，并使用最先进的求解器在实时状态下求解。我们证明了所提出的算法在关闭环路性能和计算时间方面优于另外两个最先进的运动规划算法。评估实验使用了交通仿真器SUMO、由CommonRoad环境提供的低级跟踪预测控制器和高度逼真的车辆模型和场景。
URL
https://arxiv.org/abs/2405.02979
