Autonomous driving controllers with neuromorphic spiking neural networks

Raz Halaly, Elishai Ezra Tsur

פרסום מחקרי: פרסום בכתב עתמאמרביקורת עמיתים


Autonomous driving is one of the hallmarks of artificial intelligence. Neuromorphic (brain-inspired) control is posed to significantly contribute to autonomous behavior by leveraging spiking neural networks-based energy-efficient computational frameworks. In this work, we have explored neuromorphic implementations of four prominent controllers for autonomous driving: pure-pursuit, Stanley, PID, and MPC, using a physics-aware simulation framework. We extensively evaluated these models with various intrinsic parameters and compared their performance with conventional CPU-based implementations. While being neural approximations, we show that neuromorphic models can perform competitively with their conventional counterparts. We provide guidelines for building neuromorphic architectures for control and describe the importance of their underlying tuning parameters and neuronal resources. Our results show that most models would converge to their optimal performances with merely 100–1,000 neurons. They also highlight the importance of hybrid conventional and neuromorphic designs, as was suggested here with the MPC controller. This study also highlights the limitations of neuromorphic implementations, particularly at higher (> 15 m/s) speeds where they tend to degrade faster than in conventional designs.

שפה מקוריתאנגלית
מספר המאמר1234962
עמודים (מ-עד)1234962
כתב עתFrontiers in Neurorobotics
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 2023

הערה ביבליוגרפית

Publisher Copyright:
Copyright © 2023 Halaly and Ezra Tsur.

Copyright © 2023 Halaly and Ezra Tsur.

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