Autonomous vehicles must be capable of handling the occlusion of the environment to ensure safe and efficient driving. In urban environment, occlusion often arises due to other vehicles obscuring the perception of the ego vehicle. Since the occlusion condition can impact the trajectories of vehicles, the behavior of other vehicles is helpful in making inferences about the occlusion as a remedy for perceptual deficiencies. This paper introduces a novel social occlusion inference approach that learns a mapping from agent trajectories and scene context to an occupancy grid map (OGM) representing the view of ego vehicle. Specially, vectorized features are encoded through the polyline encoder to aggregate features of vectors into features of polylines. A transformer module is then utilized to model the high-order interactions of polylines. Importantly, occlusion queries are proposed to fuse polyline features and generate the OGM without the input of visual modality. To verify the performance of vectorized representation, we design a baseline based on a fully transformer encoder-decoder architecture mapping the OGM with occlusion and historical trajectories information to the ground truth OGM. We evaluate our approach on an unsignalized intersection in the INTERACTION dataset, which outperforms the state-of-the-art results.

翻译：自动驾驶车辆必须能够处理环境遮挡以确保安全高效行驶。在城市环境中，遮挡通常由其他车辆遮挡自车视线所致。由于遮挡条件会影响车辆轨迹，其他车辆的行为可作为感知缺陷的补救措施，辅助推断遮挡情况。本文提出一种新颖的社会遮挡推断方法，学习从智能体轨迹和场景上下文到表征自车视角的占用网格地图（OGM）的映射。具体而言，通过折线编码器对向量化特征进行编码，将向量特征聚合为折线特征，随后利用Transformer模块建模折线间的高阶交互。重要的是，提出遮挡查询以融合折线特征，并在无需视觉模态输入的情况下生成OGM。为验证向量化表征性能，我们设计了一个基于全Transformer编码器-解码器架构的基线模型，将包含遮挡与历史轨迹信息的OGM映射至真实OGM。我们在INTERACTION数据集的无信号交叉口场景中评估了该方法，其性能超越了当前最优结果。