| 1. |
- Khound, Parthib, et al.
(författare)
-
Performance Index Modeling from Fault Injection Analysis for an Autonomous Lane-Keeping System
- 2023
-
Ingår i: Proceeding of the 33rd European Safety and Reliability Conference. - : Research Publishing Services.
-
Konferensbidrag (refereegranskat)abstract
- A faulty sensor data could not only undermine the stability but also drastically compromise the safety of autonomoussystems. The reliability of the functional operation can be significantly enhanced, if any monitoring modules canevaluate the risk on the system for a particular fault in a sensor. Based on the estimated risk, the system can thenexecute the necessary safety operation. To develop a risk evaluating algorithm, the relation between the faults and theeffects should be known. Therefore, to establish such cause-and-effect relationship, this paper presents a performanceindexing method that quantifies the effects caused by given fault types with different intensities. Here, the consideredsystem is a lane keeping robot and the only sensor used for the functional operation is a red, green, and blue (RGB)camera. The lane keeping algorithm is modeled using a supervised artificial intelligence (AI) learning method. Toquantify the effects with performance indices (PIs), different faults are injected to the RBG camera. For an injectedfault type, the system’s PI is evaluated from the AI algorithm’s (open-loop) outcome and the lane keeping (closedloop) outcome. The lane keeping/closed-loop outcome is quantified from the trajectory data computed using thestrapdown inertial navigation algorithm with the measurement data from a 6D inertial measurement unit (IMU).
|
|
| 2. |
- Mohammed, Omar, et al.
(författare)
-
Multilevel Artificial Intelligence Classification of Faulty Image Data for Enhancing Sensor Reliability
- 2023
-
Ingår i: Proceeding of the 33rd European Safety and Reliability Conference. - : Research Publishing Services.
-
Konferensbidrag (refereegranskat)abstract
- A multi-stage classification algorithm is proposed to predict the fault type and its associated intensity level of acamera input frame to enhance the reliability of a camera-based system. A fault injecting tool is used to generate thedataset required for the training. The model architecture mainly comprises three convolutions neural network (CNN)layers and three fully connected layers. The model achieves 93.8% accuracy for predicting a fault type. For the faultintensity prediction the accuracy significantly varies for each fault type but for some faults, the model achieves avery good prediction accuracy. However, for some other faults the accuracy can be remarkably low. The primaryreason for this gap is that the intensity levels of all considered faults can be described in a sufficiently quantitativeway, i.e., there is no sufficient metric available so far.
|
|
| 3. |
- Tahmasebi, Kaveh Nazem, et al.
(författare)
-
A Condition-Aware Stochastic Dynamic Control Strategy for Safe Automated Driving
- 2024
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858 .- 2379-8904. ; , s. 1-11
-
Tidskriftsartikel (refereegranskat)abstract
- Condition-awareness regarding electrical and electronic components is not only significant for predictive maintenance of automotive vehicles but also plays a crucial role in ensuring the operational safety by supporting the detection of anomalies, faults, and degradations over lifetime. In this paper, we present a novel control strategy that combines stochastic dynamic control method with condition-awareness for safe automated driving. In particular, the effectiveness of condition-awareness is supported by two distinct condition-monitoring functions. The first function involves the monitoring of a vehicle's internal health condition using model-based approaches. The second function involves the monitoring of a vehicle's external surrounding conditions, using machine learning and artificial intelligence approaches. For the quantification of current conditions, the results from these monitoring functions are used to create system health indices, which are then utilized by a safety control function for dynamic behavior regulation. The design of this safety control function is based on a chance-constrained model predictive control model, combined with a control barrier function for ensuring safe operation. The novelty of the proposed method lies in a systematic integration of monitored external and internal conditions, estimated component degradation, and remaining useful life, with the controller's dynamic responsiveness. The efficacy of the proposed strategy is evaluated with adaptive cruise control in the presence of various sensory uncertainties.
|
|
