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

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.


Published in:
29-33
Presented at:
2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Hsinchu, Taiwan, March 18-20, 2019
Year:
Jul 25 2019
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 Record created 2019-10-31, last modified 2019-11-05


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