Learning to Predict Collision Risk from Simulated Video Data

Abstract

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Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles

“…Lastly, certain researchers have delved into alternative perspectives of ML-based crash risk analysis by utilizing diverse techniques and data sources. To solve the data scarcity problem of collecting and labeling real (near) collisions, Schoonbeek et al [ 82 ] trained a perception module to predict optical flow and object detection from a sequence of RGB camera images, and proposed RiskNet to classify individual frames of a front-facing camera as safe or unsafe. The RiskNet was trained on a simulated collision dataset (58,904 safe and 7788 unsafe frames) and tested on real-world collision dataset (3604 safe and 1008 unsafe frames) with an accuracy of 91.8% and F1-score of 0.92.…”