Slasher: Stadium racer car for event camera end-to-end learning autonomous driving experiments

Abstract

Slasher is the first open 1/10 scale autonomous driving platform for exploring the use of neuromorphic event cameras for fast driving in unstructured indoor and outdoor environments. Slasher features a DAVIS event-based camera and ROS computer for perception and control. The DAVIS camera provides high dynamic range, sparse output, and sub-millisecond latency output for the quick visual control needed for fast driving. A race controller and Bluetooth remote joystick are used to coordinate different processing pipelines, and a low-cost ultra-wide-band (UWB) positioning system records trajectories. The modular design of Slasher can easily integrate additional features and sensors. In this paper, we show its application in a reflexive Convolutional Neural Network (CNN) steering controller trained by end-to-end learning. We present preliminary experiments in closed-loop indoor and outdoor trail driving.