aUToTrack: A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge
This addresses safety-critical object tracking for autonomous vehicles in competitions like the SAE AutoDrive Challenge, though it is incremental as it builds on existing benchmarks and methods.
The paper tackles the problem of accurately estimating pedestrian positions and velocities for self-driving cars by introducing a new dataset (UofTPed50) with GPS ground truth and a lightweight detection and tracking system (aUToTrack) that achieves state-of-the-art performance on the KITTI benchmark using CPUs only.
The University of Toronto is one of eight teams competing in the SAE AutoDrive Challenge -- a competition to develop a self-driving car by 2020. After placing first at the Year 1 challenge, we are headed to MCity in June 2019 for the second challenge. There, we will interact with pedestrians, cyclists, and cars. For safe operation, it is critical to have an accurate estimate of the position of all objects surrounding the vehicle. The contributions of this work are twofold: First, we present a new object detection and tracking dataset (UofTPed50), which uses GPS to ground truth the position and velocity of a pedestrian. To our knowledge, a dataset of this type for pedestrians has not been shown in the literature before. Second, we present a lightweight object detection and tracking system (aUToTrack) that uses vision, LIDAR, and GPS/IMU positioning to achieve state-of-the-art performance on the KITTI Object Tracking benchmark. We show that aUToTrack accurately estimates the position and velocity of pedestrians, in real-time, using CPUs only. aUToTrack has been tested in closed-loop experiments on a real self-driving car, and we demonstrate its performance on our dataset.