Autonomous vehicles (AVs) must share the driving space with other drivers and often employ conservative motion planning strategies to ensure safety. These conservative strategies can negatively impact AV's performance and significantly slow traffic throughput. Therefore, to avoid conservatism, we design an interaction-aware motion planner for the ego vehicle (AV) that interacts with surrounding vehicles to perform complex maneuvers in a locally optimal manner. Our planner uses a neural network-based interactive trajectory predictor and analytically integrates it with model predictive control (MPC). We solve the MPC optimization using the alternating direction method of multipliers (ADMM) and prove the algorithm's convergence. We provide an empirical study and compare our method with a baseline heuristic method.

翻译：自动驾驶车辆（AV）必须与其他驾驶员共享行驶空间，并常采用保守的运动规划策略以确保安全。这些保守策略可能对AV的性能产生负面影响，并显著降低交通流量。因此，为避免保守性，我们为本车（AV）设计了一种面向交互的运动规划器，使其能与周围车辆以局部最优的方式进行复杂机动操作。该规划器采用基于神经网络的交互式轨迹预测器，并将其与模型预测控制（MPC）进行解析集成。我们利用交替方向乘子法（ADMM）求解MPC优化问题，并证明了算法的收敛性。我们开展了实证研究，并将所提方法与基线启发式方法进行了对比。