Gunwant Dhadyalla

Probability based vehicle fault diagnosis: Bayesian network method

Fault diagnostics are increasingly important for ensuring vehicle safety and reliability. One of the issues in vehicle fault diagnosis is the difficulty of successful interpretation of failure symptoms to correctly diagnose the real root cause. This paper presents an innovative Bayesian Network based method for guiding off-line vehicle fault diagnosis. By using a vehicle infotainment system as a case study, a number of Bayesian diagnostic models have been established for fault cases with single and multiple symptoms. Particular considerations are given to the design of the Bayesian model structure, determination of prior probabilities of root causes, and diagnostic procedure. In order to unburden the computation, an object oriented model structure has been adopted to prevent the model from overly large. It is shown that the proposed method is capable of guiding vehicle diagnostics in a probabilistic manner. Furthermore, the method features a multiple-symptoms-orientated troubleshooting strategy, and is capable of diagnosing multiple symptoms optimally and simultaneously.

Design validation testing of vehicle instrument cluster using machine vision and hardware-in-the-loop

This paper presents an advanced testing system, combining hardware-in-the-loop (HIL) and machine vision technologies, for automated design validation testing of a vehicle instrument cluster. In the system, a HIL set-up supported by model-based approaches simulates vehicle network in real-time, and provides all essential signals to the instrument cluster under test. The machine vision system with novel image processing algorithms is designed to perform function tests by detecting gauges, warning lights/tell-tales, patterns and text displays. The system developed greatly eases the task of tedious validation testing, and makes onerous repeated tests possible.

Combinatorial Testing for an Automotive Hybrid Electric Vehicle Control System: A Case Study

Embedded electrical systems for passenger vehicles are highly complex distributed systems with varying system boundaries. The surge towards further electrification of vehicles demands the deployment of high voltage systems that provide propulsion through an electric motor as part of a hybrid electric or pure electric drive train. This demands additional care and robust deployment to ensure the safety of the end user and the environment around them. Exhaustive testing is not feasible for large systems and the use of formal approaches can be restrictive. In the presented work a combinatorial test approach is applied to a real Hybrid Electric Vehicle control system as part of a hardware-in-the-loop test system. 2-way, 3-way and mixed strength up to 4-way testing is carried out. The concept of CAN main and local is devised to intercept CAN messages and replace them with the generated combinatorial tests using the HIL simulators own processor to assure real-time testing. Early results indicate that the approach is effective in exposing incidents in system behavior not normally found during traditional functional testing.

Model-based testing of a vehicle instrument cluster for design validation using machine vision

This paper presents an advanced testing system, combining model-based testing and machine vision technologies, for automated design validation of a vehicle instrument cluster. In the system, a hardware-in-the-loop (HIL) tester, supported by model-based approaches, simulates vehicle operations in real time and dynamically provides all essential signals to the instrument cluster under test. A machine vision system with advanced image processing algorithms is designed to inspect the visual displays. Experiments demonstrate that the system developed is accurate for measuring the pointer position, bar graph position, pointer angular velocity and indicator flash rate, and is highly robust for validating various functionalities including warning lights status, symbol and text displays. Moreover, the system developed greatly eases the task of tedious validation testing and makes onerous repeated tests possible.

Identifying a gap in existing validation methodologies for intelligent automotive systems: Introducing the 3xD simulator

Recently there has been a growth in the incorporation of autonomous features within vehicles. From being perceived as a comfort feature, autonomous features in vehicles have now become a safety feature which are foreseen to reduce accidents. This has led to a new trend within the automotive industry of focussing on autonomous features for driver safety, which might ultimately lead to fully autonomous vehicles. Considering the fact that most of the accidents on UK roads occur due to driver error, driver-less vehicles would prove to be a benefit. However with automation, an even greater challenge of system validation in all scenarios needs to be addressed. For this, various methods of validation have been developed by different research organizations and manufacturers, but a standardized process still evades the industry. Some of the existing methods have been discussed in this paper to critically compare their quality of results and ease of execution. Subsequently, a new test platform has been proposed using the 3xD driving simulator which encompasses most requirements of a general testing method. A standardized process which would benefit the industry both in terms of reducing costs of having varied processes, and by increasing customer confidence can be developed using a non-invasive platform like the 3xD driving simulator. The novelty of the 3xD simulator is the ability to drive-in any vehicle (production/prototype) and develop testing methodologies in an immersive wireless environment.

Calibrating trust to increase the use of automated systems in a vehicle

While accident data show that human error is the cause of most of the on-road accidents, the move towards assisting or replacing the human driver with an automated system is not a straightforward one. Industries like aerospace, manufacturing, process etc. have a high penetration of automated systems; however, the automotive industry provides new challenges due to different and more dynamic interactions between the actors (driver, vehicle and environment). To reap benefits from the automated systems, drivers have to use the automated systems. Drivers’ trust in automated systems is one of the most important factors influencing drivers’ use of automated systems. Trust on automated systems is a dynamic construct, which can change with experience. While discussing various factors which influence drivers’ trust on automated systems, this paper discusses the changing nature of trust, i.e., calibration of trust and the possible interventions to calibrate trust on automated systems to an appropriate level.

Development of a Drive-in Driver-in-the-Loop Fully Immersive Driving Simulator for Virtual Validation of Automotive Systems

This paper gives an overview of the new Drive-in Driver-in-Loop simulator at WMG, University of Warwick, UK, which has been conceptualized to serve as a standard platform for virtual verification and validation of autonomous features. This front loading approach is key to the development of some of the upcoming technologies which have been lagging behind in terms of mass acceptance due to lack of proper simulation environment for testing. One of the key areas for the simulator is the study of driver acceptance of autonomous features in cars. Additionally, the simulator would help in the development of a validation methodology for autonomous systems keeping in mind the industry safety regulations and standards. The drive-in component on this scale adds to the novelty of the simulator, as its a first of its kind. This enhances the challenge of making the communication interfaces of the simulator general, in order to communicate with any vehicle driving into the simulator. In order to achieve this, emphasis was laid on software architecture to enable modularity and re-configuration. A brief about various applications of the simulator has been provided in this paper.

Robustness Testing against Low Voltage Transients - A Novel Approach

The increasing use of distributed functions in vehicles can introduce unexpected and undesirable emergent behavior. This can be as a result of transient events such as sudden drops in the supply voltage. In this situation system behavior is often not adequately specified or controlled. This paper presents a novel approach to automotive electronic systems testing addressing robustness against low voltage transient conditions. The paper will discuss the technical output as well as performance in real-world test usage. The proposed approach uses a combination of pseudo-random number generator algorithms to generate parameterized supply voltage waveforms simulating low voltage transient conditions, used to drive the system-under-test (SUT). Two measures are used to judge whether the SUT has passed or failed the test; the detection of unintended recorded Diagnostic Trouble Codes (DTCs) from the error memory of each Electronic Control Unit (ECU) and the detection of unexpected system functionality by a human observer or automated vision system.

Systems modelling of a driver information system - automotive industry case study

This paper describes work done at the IARC, in collaboration with an automotive original equipment manufacturer (OEM) and suppliers, in the project systems modelling language (SysML) for automotive software development and integration. The OEM is interested in how a SysML model could supplement or even replace paper specifications whereas the suppliers are more interested in finding how the resulting SysML model could be used for software development. The project focuses on practical aspects so that deployment of the language and related technology is possible smoothly. The case study involves a driver information system for a premium vehicle. Our industry partner supplied the requirements and specification documents. The progress to-date including the challenges faced, results so far and the plan for further work are detailed

Incorporating ISO 26262 Concepts in an Automated Testing Toolchain Using Simulink Design Verifier

The introduction of ISO 26262 concepts has brought important changes in the software development process for automotive software. While making the process more robust by introducing various additional methods of verification and validation, there has been a substantial increase in the development time. Thus, test automation and front loading approaches have become important to meet product timelines and quality. This paper proposes automated testing methods using formal analysis tools like Simulink Design Verifier™ (SLDV) for boundary value testing and interface testing to address the demands of ISO 26262 concepts at unit and component level. In addition, the method of automated boundary value testing proposed differs from the traditional methods and the authors offer an argument as to why the traditional boundary value testing is not required at unit (function) level. There are two aspects of the proposed method: automated test case generation and automated test case execution. The paper discusses the benefits of automatic test case execution when combined with automatic test case generation. Traditional test automation implements the former and has limited advantages. One of the challenges with traditional application of the formal analysis tool is the time taken by the tool to reach to a conclusive decision for the triggered activity, i.e., the execution time of the tool. This shortcoming is overcome by an automated setup where the test framework is triggered during out of office hours, which saves developer’s work time. As a work product of the automated test execution, the developers receive test documentation which provides them with an overview of the results and specific test vectors for further analysis.