Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems

TL;DR

This paper presents a co-design methodology for out-of-distribution detectors and learning-enabled components in autonomous emergency braking systems, optimizing safety and resource use through risk modeling.

Contribution

It introduces a risk-based co-design approach that jointly optimizes OOD detectors and LECs for autonomous systems, demonstrating significant risk reduction.

Findings

Achieved 42.3% risk reduction in AEBS

Maintained resource utilization while improving safety

Demonstrated effectiveness on vision-based AEBS

Abstract

Learning enabled components (LECs), while critical for decision making in autonomous vehicles (AVs), are likely to make incorrect decisions when presented with samples outside of their training distributions. Out-of-distribution (OOD) detectors have been proposed to detect such samples, thereby acting as a safety monitor, however, both OOD detectors and LECs require heavy utilization of embedded hardware typically found in AVs. For both components, there is a tradeoff between non-functional and functional performance, and both impact a vehicle's safety. For instance, giving an OOD detector a longer response time can increase its accuracy at the expense of the LEC. We consider an LEC with binary output like an autonomous emergency braking system (AEBS) and use risk, the combination of severity and occurrence of a failure, to model the effect of both components' design parameters on each other's functional and non-functional performance, as well as their impact on system safety. We formulate a co-design methodology that uses this risk model to find the design parameters for an OOD detector and LEC that decrease risk below that of the baseline system and demonstrate it on a vision based AEBS. Using our methodology, we achieve a 42.3% risk reduction while maintaining equivalent resource utilization.