A Deep Learning Method for Fault Detection of Autonomous Vehicles
- Resource Type
- Conference
- Authors
- Ren, Jing; Ren, Rui; Green, Mark; Huang, Xishi
- Source
- 2019 14th International Conference on Computer Science & Education (ICCSE) Computer Science & Education (ICCSE), 2019 14th International Conference on. :749-754 Aug, 2019
- Subject
- Computing and Processing
Engineering Profession
Robotics and Control Systems
Deep learning
Fault detection
Feature extraction
Fault diagnosis
Continuous wavelet transforms
deep learning
convolutional neural network
fault detection
dynamic system
autonomous vehicle
- Language
- ISSN
- 2473-9464
Fault detection is a crucial step for the safe operation of autonomous vehicles. Failure to detect faults can result in component failure leading to the breakdown of the car or even catastrophic accidents. In this paper, we propose a general fault detection method using deep learning techniques to learn patterns of faults reflected in the dynamic model of an autonomous vehicle. We have applied the proposed method to a remotely operated scaled multi-wheeled combat vehicle and evaluated the algorithm using normal and defective signals. The results show that the proposed deep learning method can accurately identify faults that are caused by mechanical problems or changes in system parameter which are reflected in the dynamic models. This general deep learning technique can be tailored to detect many defects or faults in the manufacturing and/or operation of autonomous vehicles.