Safe, Deterministic Trajectory Planning for Unstructured and Partially Occluded Environments
This addresses the problem of ensuring safe and certifiable autonomous driving in unregulated traffic areas, though it is incremental as it builds on existing methods like model predictive control and monitoring systems.
The paper tackles safe trajectory planning for automated vehicles in unstructured, partially occluded environments like parking garages by combining a model predictive control planner with a pedestrian safety monitor using cellular automata, resulting in deterministic behavior and full certifiability for low-speed navigation.
Ensuring safe behavior for automated vehicles in unregulated traffic areas poses a complex challenge for the industry. It is an open problem to provide scalable and certifiable solutions to this challenge. We derive a trajectory planner based on model predictive control which interoperates with a monitoring system for pedestrian safety based on cellular automata. The combined planner-monitor system is demonstrated on the example of a narrow indoor parking environment. The system features deterministic behavior, mitigating the immanent risk of black boxes and offering full certifiability. By using fundamental and conservative prediction models of pedestrians the monitor is able to determine a safe drivable area in the partially occluded and unstructured parking environment. The information is fed to the trajectory planner which ensures the vehicle remains in the safe drivable area at any time through constrained optimization. We show how the approach enables solving plenty of situations in tight parking garage scenarios. Even though conservative prediction models are applied, evaluations indicate a performant system for the tested low-speed navigation.