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The University of Toronto is one of eight teams competing in the SAEAutoDrive 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 June2019 for the second challenge. There, we will interact with pedestrians,cyclists, and cars. For safe operation, it is critical to have an accurateestimate of the position of all objects surrounding the vehicle. Thecontributions of this work are twofold: First, we present a new objectdetection and tracking dataset (UofTPed50), which uses GPS to ground truth theposition and velocity of a pedestrian. To our knowledge, a dataset of this typefor pedestrians has not been shown in the literature before. Second, we presenta lightweight object detection and tracking system (aUToTrack) that usesvision, LIDAR, and GPS/IMU positioning to achieve state-of-the-art performanceon the KITTI Object Tracking benchmark. We show that aUToTrack accuratelyestimates the position and velocity of pedestrians, in real-time, using CPUsonly. aUToTrack has been tested in closed-loop experiments on a realself-driving car, and we demonstrate its performance on our dataset.

aUToTrack: A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge