This paper presents a novel radar based, singleframe, multi-class detection method for moving road users (pedestrian, cyclist, car), which utilizes low-level radar cube data. The method provides class information both on the radar targetand object-level. Radar targets are classified individually after extending the target features with a cropped block of the 3D radar cube around their positions, thereby capturing the motion of moving parts in the local velocity distribution. A Convolutional Neural Network (CNN) is proposed for this classification step. Afterwards, object proposals are generated with a clustering step, which not only considers the radar targets' positions and velocities, but their calculated class scores as well. In experiments on a real-life dataset we demonstrate that our method outperforms the state-of-the-art methods both target-and objectwise by reaching an average of 0.70 (baseline: 0.68) target-wise and 0.56 (baseline: 0.48) object-wise F1 score. Furthermore, we examine the importance of the used features in an ablation study.
This paper shows the characteristics of crossing collision accident by accident party. As Fukuoka Prefecture has the third most accidents in Japan, we selected Hakata and Chuo Ward in Fukuoka city as the target of the investigation. We investigated 1,810 crossing collision accidents and 2,662 intersections' environmental factors such as traffic lights and intersection shape. In addition, we classified the accidents into car to car accidents and car to bicycle accidents according to the type of accident parties involved. Characteristics of car to car accidents tending to occur at crossroads and car to bicycle accidents occurring regardless of the traffic regulations have been clarified.
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