Autonomous driving controllers with neuromorphic spiking neural networks
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1234962/full |
| _version_ | 1797744793813516288 |
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| author | Raz Halaly Elishai Ezra Tsur |
| author_facet | Raz Halaly Elishai Ezra Tsur |
| author_sort | Raz Halaly |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T15:14:20Z |
| format | Article |
| id | doaj.art-af744130cfcc4627af80be8742acdcb0 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-03-12T15:14:20Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-af744130cfcc4627af80be8742acdcb02023-08-11T16:53:42ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182023-08-011710.3389/fnbot.2023.12349621234962Autonomous driving controllers with neuromorphic spiking neural networksRaz HalalyElishai Ezra TsurAutonomous 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.https://www.frontiersin.org/articles/10.3389/fnbot.2023.1234962/fullautonomous drivingneuromorphic controlspiking neural networkspath-tracking controllersneural engineering framework (NEF)energy efficiency |
| spellingShingle | Raz Halaly Elishai Ezra Tsur Autonomous driving controllers with neuromorphic spiking neural networks Frontiers in Neurorobotics autonomous driving neuromorphic control spiking neural networks path-tracking controllers neural engineering framework (NEF) energy efficiency |
| title | Autonomous driving controllers with neuromorphic spiking neural networks |
| title_full | Autonomous driving controllers with neuromorphic spiking neural networks |
| title_fullStr | Autonomous driving controllers with neuromorphic spiking neural networks |
| title_full_unstemmed | Autonomous driving controllers with neuromorphic spiking neural networks |
| title_short | Autonomous driving controllers with neuromorphic spiking neural networks |
| title_sort | autonomous driving controllers with neuromorphic spiking neural networks |
| topic | autonomous driving neuromorphic control spiking neural networks path-tracking controllers neural engineering framework (NEF) energy efficiency |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1234962/full |
| work_keys_str_mv | AT razhalaly autonomousdrivingcontrollerswithneuromorphicspikingneuralnetworks AT elishaiezratsur autonomousdrivingcontrollerswithneuromorphicspikingneuralnetworks |