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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2502.16027 |
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| _version_ | 1866929726350688256 |
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| author | Wang, Haidong Xiao, Pengfei Liu, Ao Shan, Qia Zhang, Jianhua |
| author_facet | Wang, Haidong Xiao, Pengfei Liu, Ao Shan, Qia Zhang, Jianhua |
| contents | In the realm of autonomous driving, conventional approaches for vehicle perception and decision-making primarily rely on sensor input and rule-based algorithms. However, these methodologies often suffer from lack of interpretability and robustness, particularly in intricate traffic scenarios. To tackle this challenge, we propose a novel brain-inspired driving (BID) framework. Diverging from traditional methods, our approach harnesses brain-inspired perception technology to achieve more efficient and robust environmental perception. Additionally, it employs brain-inspired decision-making techniques to facilitate intelligent decision-making. The experimental results show that the performance has been significantly improved across various autonomous driving tasks and achieved the end-to-end autopilot successfully. This contribution not only advances interpretability and robustness but also offers fancy insights and methodologies for further advancing autonomous driving technology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16027 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Brain-Inspired Perception-Decision Driving Model Based on Neural Pathway Anatomical Alignment Wang, Haidong Xiao, Pengfei Liu, Ao Shan, Qia Zhang, Jianhua Robotics Neural and Evolutionary Computing In the realm of autonomous driving, conventional approaches for vehicle perception and decision-making primarily rely on sensor input and rule-based algorithms. However, these methodologies often suffer from lack of interpretability and robustness, particularly in intricate traffic scenarios. To tackle this challenge, we propose a novel brain-inspired driving (BID) framework. Diverging from traditional methods, our approach harnesses brain-inspired perception technology to achieve more efficient and robust environmental perception. Additionally, it employs brain-inspired decision-making techniques to facilitate intelligent decision-making. The experimental results show that the performance has been significantly improved across various autonomous driving tasks and achieved the end-to-end autopilot successfully. This contribution not only advances interpretability and robustness but also offers fancy insights and methodologies for further advancing autonomous driving technology. |
| title | A Brain-Inspired Perception-Decision Driving Model Based on Neural Pathway Anatomical Alignment |
| topic | Robotics Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2502.16027 |