Integrating End-to-End and Modular Driving Approaches for Online Corner Case Detection in Autonomous Driving

TL;DR

This paper presents a novel method combining end-to-end and modular approaches for online corner case detection in autonomous driving, improving safety by leveraging the strengths of both systems.

Contribution

It introduces an integrated system where end-to-end networks assist modular systems in detecting corner cases, a novel approach for real-time safety enhancement.

Findings

End-to-end networks improve corner case detection accuracy.

The integrated system effectively identifies rare driving scenarios.

Real vehicle implementation demonstrates practical viability.

Abstract

Online corner case detection is crucial for ensuring safety in autonomous driving vehicles. Current autonomous driving approaches can be categorized into modular approaches and end-to-end approaches. To leverage the advantages of both, we propose a method for online corner case detection that integrates an end-to-end approach into a modular system. The modular system takes over the primary driving task and the end-to-end network runs in parallel as a secondary one, the disagreement between the systems is then used for corner case detection. We implement this method on a real vehicle and evaluate it qualitatively. Our results demonstrate that end-to-end networks, known for their superior situational awareness, as secondary driving systems, can effectively contribute to corner case detection. These findings suggest that such an approach holds potential for enhancing the safety of autonomous vehicles.