Generating Evidential BEV Maps in Continuous Driving Space

TL;DR

GevBEV is a probabilistic model that generates evidential Bird's Eye View maps with uncertainty quantification, improving perception reliability and reducing communication overhead in autonomous driving scenarios.

Contribution

The paper introduces GevBEV, a novel probabilistic BEV map model that captures uncertainties and enhances cooperative perception efficiency.

Findings

Outperforms previous BEV interpretation methods on OPV2V and V2V4Real benchmarks.

Provides more reliable uncertainty quantification in perception.

Reduces communication overhead by 87% with minimal performance loss.

Abstract

Safety is critical for autonomous driving, and one aspect of improving safety is to accurately capture the uncertainties of the perception system, especially knowing the unknown. Different from only providing deterministic or probabilistic results, e.g., probabilistic object detection, that only provide partial information for the perception scenario, we propose a complete probabilistic model named GevBEV. It interprets the 2D driving space as a probabilistic Bird's Eye View (BEV) map with point-based spatial Gaussian distributions, from which one can draw evidence as the parameters for the categorical Dirichlet distribution of any new sample point in the continuous driving space. The experimental results show that GevBEV not only provides more reliable uncertainty quantification but also outperforms the previous works on the benchmarks OPV2V and V2V4Real of BEV map interpretation for cooperative perception in simulated and real-world driving scenarios, respectively. A critical factor in cooperative perception is the data transmission size through the communication channels. GevBEV helps reduce communication overhead by selecting only the most important information to share from the learned uncertainty, reducing the average information communicated by 87% with only a slight performance drop. Our code is published at https://github.com/YuanYunshuang/GevBEV.