Towards Online Environment Model Verification

Ensuring safety for highly automated vehicles (AVs) using complex algorithms including artificial intelligence is still an open research question. A first step towards the goal of achieving safe operation is the Responsibility Sensitive Safety (RSS) model proposed by Intel/Mobileye, which addresses the decision making of an AV system. However, RSS requires a correct environment model. Hence, to have a comprehensive overall AV safety case, additional solutions and argumentation are required, to verify correctness of the environment model. For this purpose we propose in this paper a novel solution, that uses a Monitor-Recovery approach based on a dynamic occupancy grid. The grid is used to verify the object information (Monitor) provided to RSS, and if required to correct wrong information (Recovery). Our results show that with this approach we can detect common failures of a perception system and successfully recover from those errors.