Correctly understanding surrounding environments is a fundamental capability for autonomous driving. Semantic forecasting of bird-eye-view (BEV) maps can provide semantic perception information in advance, which is important for environment understanding. Currently, the research works on combining semantic forecasting and semantic BEV map generation is limited. Most existing work focuses on individual tasks only. In this work, we attempt to forecast semantic BEV maps in an end-to-end framework for future front-view (FV) images. To this end, we predict depth distributions and context features for FV input images and then forecast depth-context features for the future. The depth-context features are finally converted to the future semantic BEV maps. We conduct ablation studies and create baselines for evaluation and comparison. The results demonstrate that our network achieves superior performance.
Forecasting Semantic Bird-Eye-View Maps for Autonomous Driving
02.06.2024
1292193 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Semantic 3D Grid Maps for Autonomous Driving
IEEE | 2022
|Autonomous Vehicles: A Detailed Bird Eye View
Springer Verlag | 2022
|Semantic road maps for autonomous vehicles
Tema Archiv | 2007
|