Bioinspired membrane learnable spiking neural network for autonomous vehicle sensors fault diagnosis under open environments

Huan Wang and Yan-Fu Li

Reliability Engineering and System Safety, 2023, vol. 233, issue C

Abstract: 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.

Keywords: Fault diagnosis; Health status prediction; Spiking neural network; Autonomous vehicle sensors (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832023000170
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:233:y:2023:i:c:s0951832023000170

DOI: 10.1016/j.ress.2023.109102

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().