Leveraging External Hazard Data to Safeguard Automated Driving System

Abstract

Automated driving systems (ADS) are constrained by the limits of onboard perception, especially when visual obstructions create hazards. Using external data, such as V2X hazard messages, can mitigate these shortcomings but may introduce additional hazards due to their unreliability. We propose an Early Hazard Processing System (EHPS) that informs an extended ADS about external hazards, enabling it to perform proactive risk mitigation. Extended ADS has a cautious state that proactively reduces speed and applies comfort-bounded deceleration when EHPS reports potential hazards. All other safety-critical actions depend on internal sensor confirmation and remain under the control of ADS fallback mechanisms. Our proposed solution is formalised with a three-state model that is shown to be as safe as baseline ADS, supported by safety argumentation. Finally, our simulation-based evaluation using the Automated Lane Keeping System shows how early awareness provides more reaction time and more comfortable braking, even when external information is unreliable. Our results show that even unreliable external hazard data improves ADS safety and comfort without compromising baseline safety guarantees.