A Surrogate Metric-based Framework for Placing Infrastructure Sensing Units to Enhance Cooperative Vehicle-Infrastructure Perception

It is anticipated that roadside infrastructure sensing units (ISU) can cooperatively work with intelligent and connected vehicles (ICVs) to perceive traffic scenes more accurately when ICVs increasingly penetrate the market. However, the dynamic occlusion issue may still impair the cooperative vehicle-infrastructure perception capability (CVIPC). This study addresses the lack of an effective method for placing ISUs to augment CVIPC in a partially connected traffic environment. This study introduces probabilistic occupancy grids (POGs) to model the uncertainty of dynamic occlusions. The ground truth POG is estimated with a co-simulation method, while the observed POG by ISUs and ICVs are estimated using the proposed occlusion-considered ray-tracing algorithm. The cross entropy (CE) is applied to measure the difference between the ground truth and observed POGs and is used as a surrogate metric for estimating CVIPC. Setting ISUs’ placement parameters and POG-based CE as decision variables and the objective, respectively, Bayesian optimization (BO) is integrated with the multi-agent deep reinforcement learning (DRL) to maximize CVIPC. The test results imply that combining BO and DRL can outperform BO in optimizing ISUs’ placement. Compared to the simulation-in-the-loop optimization, the surrogate metric -based framework can achieve faster optimization with a small compromise on the optimized CVIPC measured by intersection over union. Traffic volume, traffic composition and ICV penetration rate all substantially affect CVIPC. In the test cases, as the ICV penetration rate reaches 50%, the observed POG is very close to the ground truth POG, and a further increase in the number of ICVs does not substantially contribute to improving CVIPC.