Realtime Vehicle and Pedestrian Tracking for Didi Udacity Self-Driving Car Challenge

The efficiency and execution time of a road scene evaluation subsystem directly influences a self-driving car's control effectiveness. This article presents a novel approach to fuse data from various sensors (camera, LIDAR, radar, IMU) for pedestrian and vehicle tracking. Our approach, thanks to modern methods of image processing and power of GPU for LIDAR processing, achieves 25Hz update frequency for tracking. The system has been tested on the DiDi-Udacity Self-Driving Challenge datasets.