Bioinspired Membrane Learnable Spiking Neural Network for Autonomous Vehicle Sensors Fault Diagnosis under Open Environments

Autonomous vehicles have successfully driven autonomously on urban roads, relying on numerous sensors for environmental perception and vehicle control. However, the abnormality and degradation of sensors will make vehicles face serious safety risks. Therefore, autonomous vehicles must have complete sensor fault diagnosis systems to detect anomalies and avoid accidents. Therefore, this paper explores brain-inspired spiking neural networks (SNN) for sensor fault diagnosis. Specifically, this paper proposes a brain-inspired membrane learnable residual spiking neural network (MLR-SNN) for sensor fault and health index prediction. SNN accurately simulates the dynamic mechanism of biological neurons and exhibits excellent spatiotemporal information processing potential and low power consumption while being highly biologically credible. Based on the convolution topology, this study designs a spike-residual-based SNN framework that optimizes the gradient transfer efficiency to enable deep-level spiking information encoding. In addition, membrane-learnable mechanisms are introduced to simulate the differences of neuronal membrane-related parameters in brains, which can better characterize the dynamics of neurons. The proposed MLR-SNN is validated on actual autonomous vehicle sensor datasets. Experimental results show that MLR-SNN with neural dynamics mechanism has excellent performance, and it can accurately predict fault mode and health index from multivariate sensor data under open environments.

• Record URL:
  https://doi.org/10.1016/j.ress.2023.109102
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0951832023000170
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09518320
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wang, Huan
  • 0000-0002-1403-5314
  • Li, Yan-Fu
  • 0000-0001-5755-7115
• Publication Date: 2023-5

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 109102
• Serial:
  • Reliability Engineering & System Safety
  • Volume: 233
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0951-8320
  • Serial URL: https://www.sciencedirect.com/journal/reliability-engineering-and-system-safety

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Fault location; In vehicle sensors; Machine learning; Neural networks; Structural health monitoring
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Vehicles and Equipment;

Filing Info

• Accession Number: 01876745
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 23 2023 10:20AM