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