A Search-based Motion Planner utilizing a Monitoring Functionality for Initiating Minimal Risk Maneuvers
Kailin Tong; Selim Solmaz; Martin Horn
Abstract: A reliable automated driving system (ADS) needs to perform a minimal risk maneuver (MRM) in disrupting normal driving tasks, e.g., when its perception system fails or is unreliable. One way to achieve this is by utilizing a run-time monitoring device/functionality to supervise the automated driving system status to initiate an MRM. Unlike previous research on MRM planning or safe-stop planning, where a redundant planner is running, we solve this problem in a different direction. We propose a motion planning framework for MRM by extending the directed-graph map for normal driving conditions. In our implementation, the Monitoring device supervises sensors' health and data quality and decides whether an MRM should be initiated. If an MRM is triggered, no additional planner is required, but only one additional backup search graph for MRM is utilized. Hence, the planner redundancy is no longer necessary, and the computation resources can be potentially relieved. We evaluated our approach in normal driving and conditions with perception fault injections leading to MRM. Simulations utilizing the Autoware (architecture proposal) software stack  indicate that the proposed framework fulfills the deadline of 30 ms and provides increased reliability in ADS.