The content discusses the challenges faced by autonomous vehicles in navigating complex environments with occluded regions. It introduces the Scene Informer, a novel approach that predicts both observed agent trajectories and infers occlusions in partially observable environments. The framework utilizes a transformer to aggregate input modalities and facilitate selective queries on occlusions intersecting with the AV's planned path. By estimating occupancy probabilities and likely trajectories for occlusions, as well as forecasting motion for observed agents, the Scene Informer outperforms existing methods in occupancy prediction and trajectory prediction on the Waymo Open Motion Dataset.
The paper highlights the importance of reasoning about both visible and occluded parts of the environment for safe navigation through dynamic scenarios. It emphasizes the need to consider interactions between observed and occluded agents while processing vectorized inputs from perception frameworks. The proposed Scene Informer addresses limitations in prior work by providing an end-to-end solution that integrates occlusion inference with trajectory prediction.
Furthermore, experiments conducted on the Waymo Open Motion Dataset demonstrate the superior performance of Scene Informer compared to existing methods. The framework shows increased robustness to partial observability, showcasing its ability to predict future trajectories accurately even when dealing with occluded objects. Overall, Scene Informer offers a comprehensive solution for environment prediction in partially observable settings.
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by Bernard Lang... at arxiv.org 03-12-2024
https://arxiv.org/pdf/2309.13893.pdfDeeper Inquiries