Outdoor Traffic Scene Risk Estimation in the Context of Autonomous Driving

An outdoor pedestrian-related risk estimation framework for traffic scenes using low cost visual sensors is proposed. The framework includes a Jetson Nano device with a monocular color camera for scene perception and processing. The risk estimation algorithm combines deep learning based approaches for road segmentation and object detection with conventional machine learning algorithms such as Random Forest, Support Vector Machines, Decision Trees, AdaBoost trained on visual and motion features computed for each frame. For evaluating the proposed algorithm a benchmark dataset for joint attention in autonomous driving and a custom dataset were annotated with the risk level each frame. The results show an accuracy of 90% for the benchmark data and 80% for the custom data series.