Safe, Deterministic Trajectory Planning for Unstructured and Partially Occluded Environments

TL;DR

This paper presents a deterministic, certifiable trajectory planning system for automated vehicles in unstructured, partially occluded environments, combining model predictive control with a pedestrian monitoring system based on cellular automata.

Contribution

It introduces a novel integrated planner-monitor system that ensures safety and certifiability in complex parking scenarios using conservative pedestrian prediction models.

Findings

System demonstrates safe navigation in narrow parking environments.

Ensures vehicle remains within safe drivable areas at all times.

Performs effectively in low-speed parking scenarios despite conservative predictions.

Abstract

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.