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Autonomous racing provides the opportunity to test safety-critical perceptionpipelines at their limit. This paper describes the practical challenges andsolutions to applying state-of-the-art computer vision algorithms to build alow-latency, high-accuracy perception system for DUT18 Driverless (DUT18D), a4WD electric race car with podium finishes at all Formula Driverlesscompetitions for which it raced. The key components of DUT18D includeYOLOv3-based object detection, pose estimation, and time synchronization on itsdual stereovision/monovision camera setup. We highlight modifications requiredto adapt perception CNNs to racing domains, improvements to loss functions usedfor pose estimation, and methodologies for sub-microsecond camerasynchronization among other improvements. We perform a thorough experimentalevaluation of the system, demonstrating its accuracy and low-latency inreal-world racing scenarios.

Accurate, Low-Latency Visual Perception for Autonomous Racing:Challenges, Mechanisms, and Practical Solutions