Ensuring Safe Autonomy: Navigating the Future of Autonomous Vehicles

TL;DR

This paper discusses a modular, self-adaptive framework with dynamic risk management to improve safety assurance in autonomous vehicles, addressing current limitations of traditional safety approaches and perception system unreliability.

Contribution

It introduces a novel modular, self-adaptive autonomy framework that incorporates dynamic risk management to enhance safety and reliability in autonomous driving systems.

Findings

Demonstrates improved safety performance with the proposed framework.

Shows robustness against perception uncertainties.

Highlights potential for real-world deployment improvements.

Abstract

Autonomous driving vehicles provide a vast potential for realizing use cases in the on-road and off-road domains. Consequently, remarkable solutions exist to autonomous systems' environmental perception and control. Nevertheless, proof of safety remains an open challenge preventing such machinery from being introduced to markets and deployed in real world. Traditional approaches for safety assurance of autonomously driving vehicles often lead to underperformance due to conservative safety assumptions that cannot handle the overall complexity. Besides, the more sophisticated safety systems rely on the vehicle's perception systems. However, perception is often unreliable due to uncertainties resulting from disturbances or the lack of context incorporation for data interpretation. Accordingly, this paper illustrates the potential of a modular, self-adaptive autonomy framework with integrated dynamic risk management to overcome the abovementioned drawbacks.