Uncertainty Quantification of Collaborative Detection for Self-Driving. (arXiv:2209.08162v1 [cs.CV])

Sharing information between connected and autonomous vehicles (CAVs)
fundamentally improves the performance of collaborative object detection for
self-driving. However, CAVs still have uncertainties on object detection due to
practical challenges, which will affect the later modules in self-driving such
as planning and control. Hence, uncertainty quantification is crucial for
safety-critical systems such as CAVs. Our work is the first to estimate the
uncertainty of collaborative object detection. We propose a novel uncertainty
quantification method, called Double-M Quantification, which tailors a …

arxiv collaborative detection driving quantification self-driving uncertainty