BEVDrive-E2E: Imitation With Bird's Eye View Perception for Interpretable End-to-End Autonomous Driving

Engineering notes

Our proposed approach shows the state-of-the-art (SOTA) performance on both CARLA leaderboard 1.0 and leaderboard 2.0.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Imitation learning (IL) for end-to-end autonomous driving (E2E-AD) has made great progress recently in the closed-loop evaluation of the CARLA simulator. However, the causal confusion remains an open problem. To address this issue, we propose the BEVDrive-E2E to explore the interpretability of the end-to-end model by using visual abstractions in bird's eye view (BEV). We design a hybrid BEV fusion module (HBFM) that combines the feature aggregation capabilities of CNN and transformer in both local and global areas. To fully exploit the benefits of BEV representation, we perform BEV detection and segmentation to form a unified semantic BEV map and adopt a two-stage training schedule. We leverage the transformer decoder to predict the sequential path points in an autoregressive manner. Our proposed approach shows the state-of-the-art (SOTA) performance on both CARLA leaderboard 1.0 and leaderboard 2.0.

Links and sources

• Official / arXiv page

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