Intelligent Assessment and Training of Hazard Perception for Human Drivers and Autonomous Vehicles Using Deep Reinforcement Learning and Hierarchical Clustering

• February 25th, 2025

Hazard perception (HP) is one of the few driving skills proven to reduce traffic accidents through training and testing. The driver licensing process in some countries includes assessing individuals through videos demonstrating hazards and measuring their reactions. Testing HP using a driving simulator has been shown to better approximate real-world driving and, therefore, be more effective. However, current methods need improvement for large-scale assessment and evaluation of autonomous vehicle HP abilities. This paper proposes a novel four-step procedure for training and assessing HP using deep reinforcement learning (DRL) and hierarchical clustering (HC).

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