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.
翻译:多伦多大学是SAE AutoDrive 挑战8个赛队之一,在2020年前开发一辆自驾驶车。在第一年挑战中,我们首先选择了第一年挑战,然后于2019年6月前往MCity进行第二个挑战。在那里,我们将与行人、骑自行车者和汽车进行互动。为了安全操作,必须准确估计该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物体探测和跟踪数据集(UofTPed50),该数据集使用全球定位系统来确定行人的位置和速度。据我们所知,以前文献中没有显示这种行人的数据。第二,我们提出了一个轻量物体探测和跟踪系统(autoTrack),该系统使用视觉、LIDAR和GPS/IMU定位来实现KITTI物体跟踪基准的最新性能。我们显示,AUTTracrack精确地估计行人的位置和速度,实时使用CUPUPOLS进行实时的行人位置和速度。我们仅用CUPOLOS测试了我们的闭式自动实验。