This paper introduces a local planner that synergizes the decision making and trajectory planning modules towards autonomous driving. The decision making and trajectory planning tasks are jointly formulated as a nonlinear programming problem with an integrated objective function. However, integrating the discrete decision variables into the continuous trajectory optimization leads to a mixed-integer programming (MIP) problem with inherent nonlinearity and nonconvexity. To address the challenge in solving the problem, the original problem is decomposed into two sub-stages, and a two-stage optimization (TSO) based approach is presented to ensure the coherence in outcomes for the two stages. The optimization problem in the first stage determines the optimal decision sequence that acts as an informed initialization. With the outputs from the first stage, the second stage necessitates the use of a high-fidelity vehicle model and strict enforcement of the collision avoidance constraints as part of the trajectory planning problem. We evaluate the effectiveness of our proposed planner across diverse multi-lane scenarios. The results demonstrate that the proposed planner simultaneously generates a sequence of optimal decisions and the corresponding trajectory that significantly improves driving performance in terms of driving safety and traveling efficiency as compared to alternative methods. Additionally, we implement the closed-loop simulation in CARLA, and the results showcase the effectiveness of the proposed planner to adapt to changing driving situations with high computational efficiency.

翻译：本文提出一种将决策模块与轨迹规划模块协同工作的局部规划器，用于实现自动驾驶。决策与轨迹规划任务被共同建模为一个具有集成目标函数的非线性规划问题。然而，将离散决策变量融入连续轨迹优化会形成一个兼具非线性与非凸性的混合整数规划问题。为应对求解挑战，原始问题被分解为两个子阶段，并提出一种基于两阶段优化的方法以确保两阶段结果的一致性。第一阶段的优化问题确定最优决策序列，作为有信息引导的初始化。基于第一阶段的输出，第二阶段需采用高保真车辆模型并严格遵循避障约束，作为轨迹规划问题的一部分。我们在多种多车道场景中评估所提出规划器的有效性。结果表明，相较于其他方法，该规划器能同时生成最优决策序列及对应轨迹，在行驶安全性与通行效率方面显著提升驾驶性能。此外，我们在CARLA中实现了闭环仿真，结果验证了所提出规划器能以较高计算效率适应动态驾驶场景的有效性。