Autonomous racing provides the opportunity to test safety-critical perception
pipelines at their limit. This paper describes the practical challenges and
solutions to applying state-of-the-art computer vision algorithms to build a
low-latency, high-accuracy perception system for DUT18 Driverless (DUT18D), a
4WD electric race car with podium finishes at all Formula Driverless
competitions for which it raced. The key components of DUT18D include
YOLOv3-based object detection, pose estimation, and time synchronization on its
dual stereovision/monovision camera setup. We highlight modifications required
to adapt perception CNNs to racing domains, improvements to loss functions used
for pose estimation, and methodologies for sub-microsecond camera
synchronization among other improvements. We perform a thorough experimental
evaluation of the system, demonstrating its accuracy and low-latency in
real-world racing scenarios.

本论文介绍了将最先进的计算机视觉算法应用于自动驾驶赛车的实际挑战和解决方案，包括基于 YOLOv3 的物体检测，姿态估计和双目 / 单目视觉摄像头的时间同步，强调了适用于赛车领域的感知卷积神经网络的修改，姿态估计所使用的损失函数的改进以及亚微秒级相机同步的方法等。我们对该系统进行了彻底实验评估，证明其在实际赛车场景中具有准确性和低延迟性。