Autonomous driving controllers with neuromorphic spiking neural networks

Raz Halaly, Elishai Ezra Tsur

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء


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.

اللغة الأصليةالإنجليزيّة
رقم المقال1234962
الصفحات (من إلى)1234962
دوريةFrontiers in Neurorobotics
مستوى الصوت17
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 2023

ملاحظة ببليوغرافية

Publisher Copyright:
Copyright © 2023 Halaly and Ezra Tsur.

Copyright © 2023 Halaly and Ezra Tsur.


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