Multi-Objective Optimization of Heterogeneous Sensor Placement for Autonomous Vehicles with Weighted ROI and Self-Occlusion Awareness

The perception abilities of autonomous vehicles are highly dependent on the configuration of various sensors installed in the vehicle. However, sensor placement often comes from a manual process. This research paper introduces an optimization model to address sensor placement as a multi-objective problem where the objectives consist of the maximization of weighted coverage of Regions of Interest (ROIs) and the minimization of sensor cost, subject to physical and perceptual constraints such as mounting bounds, directional balance, redundancy, and self-occlusion. The introduced method combines NSGA-II algorithm with visibility analysis by means of ray casting and weighted coverage of ROIs near the vehicle. The developed framework has been tested on three different vehicle types: a small car, a light commercial transporter, and a large bus. It has been found that the suggested optimization technique outperforms the baseline solution in case of the car and the bus, whereas the transporters pose much harder optimization problems characterized by high variance between runs. Thus, the results show that the sensor suite design heavily depends on the platform type and geometrical symmetry hinders the convergence of an evolutionary search method.