Self-driving vehicles need to anticipate a diverse set of future traffic scenarios in order to safely share the road with other traffic participants that may exhibit rare but dangerous driving. In this paper, we present LookOut, an approach to jointly perceive the environment and predict a diverse set of futures from sensor data, estimate their probability, and optimize a contingency plan over these diverse future realizations. In particular, we learn a diverse joint distribution over multi-agent future trajectories in a traffic scene that allows us to cover a wide range of future modes with high sample efficiency while leveraging the expressive power of generative models. Unlike previous work in diverse motion forecasting, our diversity objective explicitly rewards sampling future scenarios that require distinct reactions from the self-driving vehicle for improved safety. Our contingency planner then finds comfortable trajectories that ensure safe reactions to a wide range of future scenarios. Through extensive evaluations, we show that our model demonstrates significantly more diverse and sample-efficient motion forecasting in a large-scale self-driving dataset as well as safer and more comfortable motion plans in long-term closed-loop simulations than current state-of-the-art models.

本文提出了一种新的自主系统——LookOut，它能够感知环境，预测场景可能出现的不同未来情景，并通过针对这些情景的应变计划来评估自动驾驶车辆的轨迹。通过使用生成模型学习交通场景中多智能体未来轨迹的多样化联合分布，本文的多样性目标显式地奖励采样需要从车辆中得到不同反应的未来情景，然后通过应变规划器找到舒适且非保守的轨迹，确保在广泛的未来情景下进行安全反应。通过广泛的评估，我们发现我们的模型能够在大规模自动驾驶数据集中展示出更加多样且样本高效的运动预测，并且与当前最先进的模型相比，在长期闭环模拟中展现出更加安全和非保守的运动规划。

LookOut：自动驾驶的多样性多未来预测和规划