Sensor Fault Detection and Diagnosis for autonomous vehicles

Realpe, Miguel; Vintimilla, Boris X.; Vlacic, Ljubo

doi:10.1051/matecconf/20153004003

Cited by 26 publications

(7 citation statements)

References 18 publications

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“…Anomalous sensor behavior in CAVs could result from both sensor failures or malicious cyber attacks. Sensor readings may be influenced by a variety of factors, leading to collection of faulty information [23,24,25]. For example, environmental perturbations and sensor age may result in higher probabilities of failure.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors

Wang

Masoud

Khojandi

2021

IEEE Trans. Intell. Transport. Syst.

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In this paper we propose a novel observer-based method to improve the safety and security of connected and automated vehicle (CAV) transportation. The proposed method combines model-based signal filtering and anomaly detection methods. Specifically, we use adaptive extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following model. Using the car-following model the subject vehicle (i.e., the following vehicle) utilizes the leading vehicle's information to detect sensor anomalies by employing previouslytrained One Class Support Vector Machine (OCSVM) models. This approach allows the AEKF to estimate the state of a vehicle not only based on the vehicle's location and speed, but also by taking into account the state of the surrounding traffic. A communication time delay factor is considered in the carfollowing model to make it more suitable for real-world applications. Our experiments show that compared with the AEKF with a traditional χ 2 -detector, our proposed method achieves a better anomaly detection performance. We also demonstrate that a larger time delay factor has a negative impact on the overall detection performance.

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Section: Related Workmentioning

confidence: 99%

Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors

Wang

Masoud

Khojandi

2021

IEEE Trans. Intell. Transport. Syst.

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“…The next drawback of AV is dysfunctional sensors [31]. Sensor failures often happened in AV [31], as an example the locking system.…”

Section: B Challenges Of Avmentioning

confidence: 99%

“…The next drawback of AV is dysfunctional sensors [31]. Sensor failures often happened in AV [31], as an example the locking system. The locking system is a very crucial part in automated vehicles safety [32].…”

Section: B Challenges Of Avmentioning

confidence: 99%

Intelligent Locking System using Deep Learning for Autonomous Vehicle in Internet of Things

H¹,

Ithnin²,

Wahab³

et al. 2021

IJACSA

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“…Thus, fault detection is indispensable for diagnosing system malfunctions. Fault detection in sensors [44] is especially crucial to an autonomous system because the modeling environment constitutes the fundamental element of every operation. This kind of mechanism have been the focus of studies of fault detection and diagnosis (FDD).…”

Section: Autonomy In Engineeringmentioning

confidence: 99%

Integrations between Autonomous Systems and Modern Computing Techniques: A Mini Review

Chen

Abbod

Shieh

2019

Sensors

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The emulation of human behavior for autonomous problem solving has been an interdisciplinary field of research. Generally, classical control systems are used for static environments, where external disturbances and changes in internal parameters can be fully modulated before or neglected during operation. However, classical control systems are inadequate at addressing environmental uncertainty. By contrast, autonomous systems, which were first studied in the field of control systems, can be applied in an unknown environment. This paper summarizes the state of the art autonomous systems by first discussing the definition, modeling, and system structure of autonomous systems and then providing a perspective on how autonomous systems can be integrated with advanced resources (e.g., the Internet of Things, big data, Over-the-Air, and federated learning). Finally, what comes after reaching full autonomy is briefly discussed.

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Sensor Fault Detection and Diagnosis for autonomous vehicles

Cited by 26 publications

References 18 publications

Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors

Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors

Intelligent Locking System using Deep Learning for Autonomous Vehicle in Internet of Things

Integrations between Autonomous Systems and Modern Computing Techniques: A Mini Review

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