Real-Time Capable Decision Making for Autonomous Driving Using Reachable Sets

TL;DR

This paper introduces a real-time decision module for autonomous driving that uses reachability analysis to identify safe driving corridors, improving planning speed and safety in complex traffic scenarios.

Contribution

The work presents a novel set-based reachability approach for real-time decision making in autonomous driving, integrating it with existing motion planners for enhanced performance.

Findings

Decision module is real-time capable.

Significant speed-ups in planning performance.

Effective in complex traffic scenarios.

Abstract

Despite large advances in recent years, real-time capable motion planning for autonomous road vehicles remains a huge challenge. In this work, we present a decision module that is based on set-based reachability analysis: First, we identify all possible driving corridors by computing the reachable set for the longitudinal position of the vehicle along the lanelets of the road network, where lane changes are modeled as discrete events. Next, we select the best driving corridor based on a cost function that penalizes lane changes and deviations from a desired velocity profile. Finally, we generate a reference trajectory inside the selected driving corridor, which can be used to guide or warm start low-level trajectory planners. For the numerical evaluation we combine our decision module with a motion-primitive-based and an optimization-based planner and evaluate the performance on 2000 challenging CommonRoad traffic scenarios as well in the realistic CARLA simulator. The results demonstrate that our decision module is real-time capable and yields significant speed-ups compared to executing a motion planner standalone without a decision module.