Accurately predicting the future motion of surrounding vehicles requires reasoning about the inherent uncertainty in goals and driving behavior. This uncertainty can be loosely decoupled into lateral (e.g., keeping lane, turning) and longitudinal (e.g., accelerating, braking). We present a novel method that combines learned discrete policy rollouts with a focused decoder on subsets of the lane graph. The policy rollouts explore different goals given our current observations, ensuring that the model captures lateral variability. The longitudinal variability is captured by our novel latent variable model decoder that is conditioned on various subsets of the lane graph. Our model achieves state-of-the-art performance on the nuScenes motion prediction dataset, and qualitatively demonstrates excellent scene compliance. Detailed ablations highlight the importance of both the policy rollouts and the decoder architecture.

翻译：准确预测周围车辆未来运动需要推理目标和驾驶行为固有的不确定性。这种不确定性可以松散地分解成横向(如保持车道、转动)和纵向(如加速、制动),我们提出了一个新颖的方法,将所学的离散政策推出与对车道图子集的集中解码器结合起来。政策推出根据我们目前的观察,探索了不同的目标,确保模型能够捕捉横向变异性。我们的新颖的潜伏变异模型可以捕捉到纵向变异性,该模型以车道图的各个子块为条件。我们的模型在Nuscens运动预测数据集上取得了最先进的性能,在质量上展示了极佳的现场合规性。详细的推理突出了政策推出和解变结构的重要性。