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Real-Time Environment Condition Classification for Autonomous Vehicles by Introvigne, Marco; Ramazzina, Andrea; Walz, Stefanie; Scheuble, Dominik; Bijelic, Mario is a scholarly article available to read on EtoBox.

What is Real-Time Environment Condition Classification for Autonomous Vehicles about?

Current autonomous driving technologies are being rolled out in geo-fenced areas with well-defined operation conditions such as time of operation, area, weather conditions and road conditions. In this way, challenging conditions as adverse weather, slippery road or densely-populated city centers can be excluded. In order to lift the geo-fenced restriction and allow a more dynamic availability of autonomous driving functions, it is necessary for the vehicle to autonomously perform an environment condition assessment in real time to identify when the system cannot operate safely and either stop operation or require the resting passenger to take control. In particular, adverse-weather challenges are a fundamental limitation as sensor performance degenerates quickly, prohibiting the use of sensors such as cameras to locate and monitor road signs, pedestrians or other vehicles. To address this issue, we train a deep learning model to identify outdoor weather and dangerous road conditions, enabling a quick reaction to new situations and environments. We achieve this by introducing an improved taxonomy and label hierarchy for a state-of-the-art adverse-weather dataset, relabelling it with

Author

Introvigne, Marco; Ramazzina, Andrea; Walz, Stefanie; Scheuble, Dominik; Bijelic, Mario

Published

2024

Language

EN