In autonomous driving, addressing occlusion scenarios is crucial yet challenging. Robust surrounding perception is essential for handling occlusions and aiding motion planning. State-of-the-art models fuse Lidar and Camera data to produce impressive perception results, but detecting occluded objects remains challenging. In this paper, we emphasize the crucial role of temporal cues by integrating them alongside these modalities to address this challenge. We propose a novel approach for bird's eye view semantic grid segmentation, that leverages sequential sensor data to achieve robustness against occlusions. Our model extracts information from the sensor readings using attention operations and aggregates this information into a lower-dimensional latent representation, enabling thus the processing of multi-step inputs at each prediction step. Moreover, we show how it can also be directly applied to forecast the development of traffic scenes and be seamlessly integrated into a motion planner for trajectory planning. On the semantic segmentation tasks, we evaluate our model on the nuScenes dataset and show that it outperforms other baselines, with particularly large differences when evaluating on occluded and partially-occluded vehicles. Additionally, on motion planning task we are among the early teams to train and evaluate on nuPlan, a cutting-edge large-scale dataset for motion planning.

翻译：在自动驾驶中，处理遮挡场景至关重要且极具挑战性。鲁棒的周围环境感知对于应对遮挡并辅助运动规划十分关键。现有最先进的模型通过融合激光雷达与摄像头数据取得了令人印象深刻的感知结果，但检测被遮挡目标仍具难度。本文强调时序线索的关键作用，通过将其与这些模态相结合来应对该挑战。我们提出一种创新的鸟瞰语义网格分割方法，利用时序传感器数据实现对遮挡的鲁棒性。该模型通过注意力操作从传感器读数中提取信息，并将这些信息汇聚为低维潜在表征，从而在每个预测步骤中处理多步输入。此外，我们展示了该方法可直接用于预测交通场景演变，并无缝集成至运动规划器中进行轨迹规划。在语义分割任务上，我们基于nuScenes数据集评估模型，结果表明其优于其他基线方法，尤其在评估被遮挡和部分遮挡车辆时差异显著。在运动规划任务上，我们是早期在nuPlan（一项前沿的大规模运动规划数据集）上训练和评测的团队之一。