A popular way to plan trajectories in dynamic urban scenarios for Autonomous Vehicles is to rely on explicitly specified and hand crafted cost functions, coupled with random sampling in the trajectory space to find the minimum cost trajectory. Such methods require a high number of samples to find a low-cost trajectory and might end up with a highly suboptimal trajectory given the planning time budget. We explore the application of normalizing flows for improving the performance of trajectory planning for autonomous vehicles (AVs). Our key insight is to learn a sampling policy in a low-dimensional latent space of expert-like trajectories, out of which the best sample is selected for execution. By modeling the trajectory planner's cost manifold as an energy function, we learn a scene conditioned mapping from the prior to a Boltzmann distribution over the AV control space. Finally, we demonstrate the effectiveness of our approach on real-world datasets over IL and hand-constructed trajectory sampling techniques.

翻译：规划机动车辆动态城市情景轨迹的流行方式是依靠明确指定和手工设计的成本功能,同时在轨迹空间进行随机抽样,以找到最低成本轨迹。这种方法需要大量样本,以寻找低成本轨迹,最后可能出现高度不理想的轨迹,因为计划时间预算有规划。我们探索如何应用正常流来改进自主车辆轨迹规划的性能。我们的关键洞察力是在专家式轨迹的低维潜层中学习抽样政策,其中选用最佳样本进行操作。通过模拟轨迹规划器的多重成本作为能源功能,我们从Boltzmann对AV控制空间的先前分布中学习了场景条件绘图。最后,我们展示了我们对IL和手制轨迹取样技术的实际世界数据集的有效性。