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:自主车辆必须与其他驾驶员共用驾驶空间,并经常采用保守的机动规划战略来确保安全。这些保守战略可能对AV的性能产生消极影响,并大大减缓交通流量。因此,为了避免保守主义,我们为与周围车辆互动的自我汽车设计了一个互动机动规划器,以当地最佳的方式进行复杂的操作。我们的规划员使用一个基于神经网络的交互式轨道预测器,在分析上将其与模型预测控制(MPC)相结合。我们用乘数交替方向法(ADMM)解决MPC优化问题,并证明算法的趋同。我们提供了经验研究,并将我们的方法与基线超常方法进行了比较。</s>