This paper offers a technique for estimating collision risk for automated ground vehicles engaged in cooperative sensing. The technique allows quantification of (i) risk reduced due to cooperation, and (ii) the increased accuracy of risk assessment due to cooperation. If either is significant, cooperation can be viewed as a desirable practice for meeting the stringent risk budget of increasingly automated vehicles; if not, then cooperation—with its various drawbacks—need not be pursued. Collision risk is evaluated over an ego vehicle’s trajectory based on a dynamic probabilistic occupancy map and a loss function that maps collision-relevant state information to a cost metric. The risk evaluation framework is demonstrated using real data captured from two cooperating vehicles traversing an urban intersection.

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LaChapelle, Daniel, Todd Humphreys, Lakshay Narula, Peter Iannucci, and Ehsan Moradi-Pari. "Automotive Collision Risk Estimation Under Cooperative Sensing," In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 9200-9204. IEEE, 2020.