A fault diagnosis framework for autonomous vehicles with sensor self-diagnosis

Outstanding steps towards intelligent transportation systems with autonomous vehicles have been taken in the past few years. Nevertheless, the safety issue in autonomous vehicles is critical and remains to be fully solved. Sensor data provide information about the internal status of the system and the impact of its external environment, where the occurrence of sensor data anomalies indicates the existence of potential safety risks. Therefore, in this work, a taxonomy for potential sensor data anomaly sources from the perspective of road vehicle safety is proposed, motivated by the lack of a unified comprehensive taxonomy of sensor data anomaly identification for autonomous vehicles. In this context, sources are divided into; 1) fault or failure of the components or subsystems; 2) failure of the adaptability to the external environment; 3) cyber-attacks; and 4) faults or design deficiencies of sensors. Based on the taxonomy proposed, related works, and in particular, countermeasures for the four potential sources of sensor data anomalies in autonomous vehicles are then reviewed. In the context of providing a comprehensive discussion, other taxonomies of potential sources causing sensor data anomalies for autonomous vehicles and the issue of interpretability of sensor data anomalies are also discussed, providing insight into the strengths of the proposed taxonomy. 

Potential Sources of Sensor Data Anomalies for Autonomous Vehicles: An Overview from Road Vehicle Safety Perspective

Generative Artificial Intelligence (GAI) has sparked a transformative wave across various domains, including machine learning, healthcare, business, and entertainment, owing to its remarkable ability to generate lifelike data. This comprehensive survey offers a meticulous examination of the privacy and security challenges inherent to GAI. It provides five pivotal perspectives essential for a comprehensive understanding of these intricacies. The paper encompasses discussions on GAI architectures, diverse generative model types, practical applications, and recent advancements within the field. In addition, it highlights current security strategies and proposes sustainable solutions, emphasizing user, developer, institutional, and policymaker involvement.

Privacy and Security Concerns in Generative AI: A Comprehensive Survey

Efficient anomaly detection is crucial to ensure safe operation of Autonomous vehicles (AVs). This study proposes an interpretable method for detecting anomalies in AV data based on denoising variational Transformer (DVT). Considering the limitation of simple structure, poor learning ability of typical autoencoder network, and inability of learning data distribution of traditional self-attention mechanism, current work develops an innovative variational attention mechanism by incorporating Gaussian prior distribution into attention weight. Further, based on proposed variational attention mechanism, an unsupervised anomaly detection network model for DVT is constructed, which can learn distribution of input and reconstruct original input as output to construct residuals for anomaly detection. Additionally, a residual interpreter is designed to calculate contribution of each input feature to anomaly score to explain anomaly detection results. By employing proposed model, an AV data anomaly detection framework is constructed. The proposed model is experimentally verified using simulation platform and real AV platform, and experimental results demonstrate that proposed DVT model outperforms other unsupervised anomaly methods, improving F1-score value by 8%–50% and area under curve index by 1%–20%. Moreover, the average interpretation accuracy of proposed residual interpreter surpasses that of kernel shapley additive explanations by more than 50%, while significantly reducing computational time by over 99%. 

Toward interpretable anomaly detection for autonomous vehicles with denoising variational transformer

A survey on misbehavior detection for connected and autonomous vehicles

An accurate fault detection and diagnosis system is of great importance for autonomous vehicles to prevent the potential hazardous situations. In this paper, we propose a fault detection and diagnosis system based on hybrid approaches. First, to detect the state faults of the autonomous vehicle, One-Class Support Vector Machine (SVM) method is adopted to train the boundary curve which separates the safe domain and unsafe domain. Meanwhile, a Kalman filter observer is designed based on the linear kinematic vehicle bicycle model to predict the current position of the vehicle, and after obtaining the residuals between prediction and measurement, Jarque-Bera test is applied to check the normality of the residuals probability distribution to monitor whether the trajectory deviates. Furthermore, we design a fuzzy system to distinguish the types of the detected faults based on a modified neutral network, in which a membership function layer is added after the input layer. With the strong self-learning ability of neutral network, the initial membership function of the fuzzy system is updated through black box test and can indicate the probability of each fault type. Experiments on the real autonomous vehicle platform ‘Xinda’ and performance comparison with other fault detectors validate the effectiveness of these methods and the usability of the fault detection and diagnosis system.

A Fault Detection and Diagnosis System for Autonomous Vehicles Based on Hybrid Approaches

At an airport, the information of the number and positions of airplanes is very important for the applications of air navigation. Especially, the information from airplane extraction and identification is significant in both civil and military remote sensing. In this paper, according to the characteristics of airplanes and airport in satellite remote sensing images, a new airplane image segmentation algorithm is proposed based on improved pulse-coupled neural network (PCNN) with wavelet transform, and airplane identification algorithm is carried out by using modified Zernike moments. Firstly, for an original image, a PCNN model is improved and then used to do image segmentation by combining the wavelet transform. Then, in order to reduce the number of irrespective targets in the image and increase the processing speed, the airplanes in the original image are roughly detected on the characteristics of the segmented object contour geometries. Finally, the Zernike moments are modified and then applied to ide...

Airplane extraction and identification by improved PCNN with wavelet transform and modified Zernike moments

The invention discloses an automobile fault diagnosis method and system. The automobile fault diagnosis method comprises the steps of carrying out training optimization on a vehicle system model through utilizing system operation data information of a vehicle in a normal driving state, collecting system operation data information of the vehicle in a driving state in real time, performing sensor data noise reduction and fatal sensor fault feature extraction and screening on the system operation data information in the vehicle driving state, performing reconstruction on the same, inputting the reconstructed system operation data information into the trained and optimized vehicle system model, outputting system state data, comparing the system state data with a set threshold value, and if thesystem state data exceeds a set threshold value, indicating that a fault occurs in a system executer corresponding to the system state data. Through carrying out sensor data noise reduction and fatalsensor fault feature extraction and screening on the collected system operation data, fatal abnormity of the sensor data and abnormity of the vehicle operation state can be effectively detected; andautomobile fault positioning is realized by utilizing the position corresponding to the system operation data, so that the reliability of automobile fault diagnosis is greatly improved.

Automobile fault diagnosis method and system

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A Fault Detection and Diagnosis System for Autonomous Vehicles Based on Hybrid Approaches

Airplane extraction and identification by improved PCNN with wavelet transform and modified Zernike moments

Automobile fault diagnosis method and system