Vehicles need to operate safely in all conditions. While current perception approaches have enabled ADAS and Autonomous Vehicles to make progress in that regard, it is crucial for “all conditions” to include the most difficult scenarios such as darkness and poor weather, i.e. rain, snow, and fog. Unfortunately, current testing guidelines from NHTSA and EuroNCAP evaluate vehicle safety in nominal good conditions only. Numerous reports show a lack of robustness in these harsh scenarios. For example, a recent AAA report testing late-model vehicles concluded that Automated Emergency Braking (AEB) consistently fails in darkness. Algolux will review the challenges of robust computer vision, describe advanced machine learning approaches to improve computer vision accuracy under all scenarios, and show direct comparisons and benchmarks against open and commercial perception solutions.
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