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.
翻译:自行驾驶的车辆需要预测一系列不同的未来交通情况,以便安全地与其他交通参与者分享道路,这些参与者可能表现出罕见但危险的驾驶方式。 在本文中,我们介绍“LookOut”这一方法,共同看待环境,从传感器数据中预测各种不同的未来,估计其概率,并根据这些不同的未来实现情况优化应急计划。特别是,我们了解到在交通场多试剂未来轨迹上的不同联合分布,这使我们能够覆盖广泛的未来模式,具有高采样效率,同时利用基因模型的显眼力。与以往的不同运动预测工作不同,我们的多样性目标明确奖励未来情景抽样,这些情景需要由自行驾驶的车辆做出不同反应,以提高安全性。我们的应急计划设计者随后发现舒适的轨迹,以确保对广泛的未来情景做出安全反应。我们通过广泛的评估,显示我们的模型显示,在大规模自我驾驶数据集中,在长期闭路模拟中,在比目前状态模型更安全、更舒适和更舒适的移动计划中,其样本效率要大得多。