Neuromorphic Computing for Autonomous Racing
Robert M. Patton, Catherine D. Schuman, Shruti Kulkarni, Maryam Parsa, J. Parker Mitchell, Nicholas Quentin Haas, Christopher Stahl, Spencer Paulissen, Prasanna Date, Thomas E. Potok, Shay Snyder
Abstract
Neuromorphic computing has many opportunities in future autonomous systems, especially those that will operate at the edge. However, there are relatively few demonstrations of neuromorphic implementations on real-world applications, partly because of the lack of availability of neuromorphic hardware and software, but also because of the lack of availability of an accessible demonstration platform. In this work, we propose utilizing the F1Tenth platform as an evaluation task for neuromorphic computing. F1Tenth is a competition wherein one tenth scale cars compete in an autonomous racing task; there are significant open source resources in both software and hardware for realizing this task. We present a workflow with neuromorphic hardware, software, and training that can be used to develop a spiking neural network for neuromorphic hardware deployment to perform autonomous racing. We present initial results on utilizing this approach for this small-scale, real-world autonomous vehicle task.