Safety Blind Spot in Remote Driving: Considerations for Risk Assessment of Connection Loss Fallback Strategies

TL;DR

The paper addresses a safety blind spot in remote driving by evaluating the risks of immediate braking as a fallback after radio-link loss in urban settings. It uses a naturalistic uniD dataset and two longitudinal driver models to simulate rear-end collision probability and analyzes hazards under ISO 21448 and ISO 26262 frameworks, highlighting high collision likelihood and increased severity when large trucks are involved. The findings indicate that simple emergency braking fallbacks can be unsafe in practice, necessitating safer fallback strategies designed early in the development cycle. The work aims to stimulate discussion among developers, safety engineers, and regulators and lays groundwork for future research into onboard automated driving extensions and more comprehensive hazard analyses across diverse datasets.

Abstract

As part of the overall goal of driverless road vehicles, remote driving is a major emerging field of research of its own. Current remote driving concepts for public road traffic often establish a fallback strategy of immediate braking to a standstill in the event of a connection loss. This may seem like the most logical option when human control of the vehicle is lost. However, our simulation results from hundreds of scenarios based on naturalistic traffic scenes indicate high collision rates for any immediate substantial deceleration to a standstill in urban settings. We show that such a fallback strategy can result in a SOTIF relevant hazard, making it questionable whether such a design decision can be considered acceptable. Therefore, from a safety perspective, we would call this problem a safety blind spot, as safety analyses in this regard seem to be very rare. In this article, we first present a simulation on a naturalistic dataset that shows a high probability of collision in the described case. Second, we discuss the severity of the resulting potential rear-end collisions and provide an even more severe example by including a large commercial vehicle in the potential collision.