In this paper we show an effective means of integrating data driven frameworks to sampling based optimal control to vastly reduce the compute time for easy adoption and adaptation to real time applications such as on-road autonomous driving in the presence of dynamic actors. Presented with training examples, a spatio-temporal CNN learns to predict the optimal mean control over a finite horizon that precludes further resampling, an iterative process that makes sampling based optimal control formulations difficult to adopt in real time settings. Generating control samples around the network-predicted optimal mean retains the advantage of sample diversity while enabling real time rollout of trajectories that avoids multiple dynamic obstacles in an on-road navigation setting. Further the 3D CNN architecture implicitly learns the future trajectories of the dynamic agents in the scene resulting in successful collision free navigation despite no explicit future trajectory prediction. We show performance gain over multiple baselines in a number of on-road scenes through closed loop simulations in CARLA. We also showcase the real world applicability of our system by running it on our custom Autonomous Driving Platform (AutoDP).

翻译：本文展示了一种将数据驱动框架有效融入采样型最优控制的方法，可大幅降低计算时间，便于在动态参与者存在的道路自动驾驶等实时应用场景中快速采用与适配。通过训练样本，时空卷积神经网络学习预测有限时域内的最优平均控制量，从而避免了重新采样这一迭代过程——正是该过程使得采样型最优控制方法难以应用于实时场景。在网络预测的最优均值周围生成控制样本，既能保留样本多样性的优势，又可在道路导航中实现实时轨迹规划，规避多个动态障碍物。此外，3D卷积神经网络隐式学习了场景中动态代理的未来轨迹，无需显式轨迹预测即可实现无碰撞导航。通过在CARLA仿真器中进行闭环实验，我们展示了系统在多个道路场景中相较于多个基线方法的性能提升。同时，我们在自研的自动驾驶平台（AutoDP）上运行该系统，验证了其实用性。