  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.
