PolarPoint-BEV: Bird-Eye-View Perception in Polar Points for Explainable End-to-End Autonomous Driving

Engineering notes

Key topics: end-to-end autonomous driving, autonomous driving, bev perception, bev, end-to-end, perception. See the paper for implementation details and experimental results.

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

End-to-end autonomous driving has attracted great attentions in recent years. Compared to traditional modular methods, end-to-end methods are more scalable in complex traffic environments, but they lack explainability. Many methods have been proposed to increase the explainability for end-to-end autonomous driving, such as using semantic bird-eye-view (BEV) maps. BEV maps can explain the outputs of end-to-end methods by showing how the networks perceive and understand surrounding traffic environments. However, there are some limitations in traditional semantic BEV maps. For instance, all regions of traffic scenes are treated equally, but the fact is that the regions near the ego vehicle are normally more critical to vehicle safety. Moreover, traditional BEV maps represent traffic scenes in the fine-grained pixel-level mode, which leads to much computational cost. To address these issues, we introduce a novel lightweight BEV perception method, PolarPoint-BEV, which prioritizes the regions according to object distances to the ego vehicle. Furthermore, we propose an explainable end-to-end autonomous driving network to investigate the influence of our PolarPoint-BEV in terms of driving performance. Experimental results demonstrate that our PolarPoint-BEV improves both the driving capability and explainability of the network.

Links and sources

• Official / arXiv page

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