Abstract
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.
Abstract (translated)
多伦多大学是参加SAE自动驾驶挑战赛的八支车队之一，该挑战赛旨在在2020年前开发一款自动驾驶汽车。在第一年挑战赛中获得第一名后，我们将于2019年6月前往McCity参加第二次挑战赛。在那里，我们将与行人、骑车人和汽车互动。为了安全操作，必须准确估计车辆周围所有物体的位置。这项工作的贡献是双重的：首先，我们提出了一个新的目标检测和跟踪数据集（UOFTPED50），它使用GPS来真实地测量行人的位置和速度。据我们所知，这种类型的行人数据集以前没有在文献中显示过。其次，我们提出了一种轻量物体检测和跟踪系统（autotrack），它使用视觉、激光雷达和GPS/IMU定位来实现基蒂物体跟踪基准的最先进性能。我们表明，自动跟踪准确估计行人的位置和速度，在实时，只用CPU。在实际的自动驾驶汽车上进行了闭环试验，并在数据集上对其性能进行了验证。
URL
https://arxiv.org/abs/1905.08758
