Learning-Based Error Detection System for Advanced Vehicle Instrument Cluster Rendering

TL;DR

This paper introduces a learning-based system to detect rendering errors in vehicle instrument clusters, ensuring display correctness despite complex effects like overlays and scaling, thereby improving safety and reliability.

Contribution

It presents a novel, resilient monitoring approach that uses telltales to distinguish correct from corrupted displays, overcoming limitations of traditional CRC methods.

Findings

All corrupted test patterns were correctly classified.

No false alarms were triggered in experiments.

The system is resilient to pixel errors and display effects.

Abstract

The automotive industry is currently expanding digital display options with every new model that comes onto the market. This entails not just an expansion in dimensions, resolution, and customization choices, but also the capability to employ novel display effects like overlays while assembling the content of the display cluster. Unfortunately, this raises the need for appropriate monitoring systems that can detect rendering errors and apply appropriate countermeasures when required. Classical solutions such as Cyclic Redundancy Checks (CRC) will soon be no longer viable as any sort of alpha blending, warping of scaling of content can cause unwanted CRC violations. Therefore, we propose a novel monitoring approach to verify correctness of displayed content using telltales (e.g. warning signs) as example. It uses a learning-based approach to separate "good" telltales, i.e. those that a human driver will understand correctly, and "corrupted" telltales, i.e. those that will not be visible or perceived correctly. As a result, it possesses inherent resilience against individual pixel errors and implicitly supports changing backgrounds, overlay or scaling effects. This is underlined by our experimental study where all "corrupted" test patterns were correctly classified, while no false alarms were triggered.