AutoSCOOP: Automated Road-Side Sensor Coverage Optimization for Robotic Vehicles on Proving Grounds

摘要

The use of automated vehicle testing on proving grounds is increasing to enable time and cost-effective testing and reduce risks to test drivers. Robot test vehicles are used to perform various functions and load tests, even under severe conditions. Therefore, to ensure safety in proving grounds, perception and monitoring of surrounding vehicles are necessary. This requires a target-oriented, robust and foresighted perception based on road-side systems, due to the fact that test vehicles' on-board sensors are generally insufficient and short-sighted. Such a challenging sensor system has to take into account area-wide coverage, high detection probability, and low cost, for complex areas. To address this problem, we introduce AutoSCOOP, a novel method to automatically optimize sensor coverage on proving grounds. AutoSCOOP uses ray-cast sensor models and a detailed 3D environment model in a game engine to determine accurate and realistic sensor coverage. In combination with an evolutionary strategy-based method, an optimization is performed to find the optimal placement and number of road-side sensors. The methodology is successfully applied to an environmental model based on a real proving ground, and experimental evaluations are presented to show that full coverage is achieved with a minimal number of sensors.