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


Hu, Yuhuang; Chen, Hong Ming; Delbruck, Tobi (2019). Slasher: Stadium racer car for event camera end-to-end learning autonomous driving experiments. In: 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Hsinchu, Taiwan, 18 March 2019 - 20 March 2019.

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.

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.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:20 March 2019
Deposited On:11 Feb 2020 15:08
Last Modified:16 Feb 2020 07:08
Publisher:IEEE
ISBN:9781538678848
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/aicas.2019.8771520

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