Fault-Tolerant Perception for Automated Driving A Lightweight Monitoring Approach
This addresses safety concerns in automated driving by providing a feasible solution for perception monitoring, though it is incremental as it builds on existing sensor and plausibility checks.
The paper tackles the problem of ensuring fault-tolerance in perception systems for automated driving by proposing a lightweight monitoring approach that detects errors in object distance and velocity using LiDAR and motion history checks, with minimal compute overhead.
While the most visible part of the safety verification process of automated vehicles concerns the planning and control system, it is often overlooked that safety of the latter crucially depends on the fault-tolerance of the preceding environment perception. Modern perception systems feature complex and often machine-learning-based components with various failure modes that can jeopardize the overall safety. At the same time, a verification by for example redundant execution is not always feasible due to resource constraints. In this paper, we address the need for feasible and efficient perception monitors and propose a lightweight approach that helps to protect the integrity of the perception system while keeping the additional compute overhead minimal. In contrast to existing solutions, the monitor is realized by a well-balanced combination of sensor checks -- here using LiDAR information -- and plausibility checks on the object motion history. It is designed to detect relevant errors in the distance and velocity of objects in the environment of the automated vehicle. In conjunction with an appropriate planning system, such a monitor can help to make safe automated driving feasible.