Abstract
The active safety control systems of highly automated vehicles for SAE level 3 and higher are still not fully developed and facing some unresolved issues. The deployment of automated driving systems and the functional safety development present challenges in driver – machine control relationship when there is a system failure or malfunction. The current definition of the product development and controllability classes of the road vehicles functional safety (ISO26262) are not feasible in highly automated vehicles (HAV). This research developed an overview of fault or disturbance injection on the steering system of highly automated model to study the impact of steering system sensors malfunction. The approach was to study the fault propagation using a model-based engineering development in a virtual environment of MATLAB. Subsequently, the steering control system of automated vehicle was developed using an adaptive model predictive control structure to study the control system sensors failures on a system-feature level of the vehicle. It was concluded that the steering wheel angle sensor failure has a significant impact on the planned trajectory of the vehicle and thus it was classified as ASIL D, which represents the highest critical safety component and requires comprehensive safety mechanisms to meet the safety goals of the system. The study also introduced a new criterion for controllability classes suitable for highly automated systems based on the global vehicle position relative of the lane marker lines, to deal with the active safety systems and risk handling strategies. The drivers – vehicle control systems are changing significantly in SAE level 3 automated vehicle and above that driving functions are controlled by the vehicle control systems. This presents human factors challenge in this interactive system with moving to SAE levels 4 and 5. Hence, several human machine interfaces and scenario-based testing are introduced to mitigate any risk or safety uncertainty resulting from control handing-over between the driver and the vehicle control system.
Highlights
Automated and intelligent transportation driving systems have attracted extensive attention and interest from academia, industry, and the public
The research redefined the controllability classes or categories of high automated vehicles based on the vehicle global position related to the lane marker lines to accommodate for the machine in the loop controlling the dynamic driving task (DDT) in autonomous vehicle maneuvering
The driver – automated control system engagement in the steering system of the vehicles is one of the crucial control complex scenario that add uncertainty and potential risk when handing over the steering control between the driver and-or the automated control system
Summary
Automated and intelligent transportation driving systems have attracted extensive attention and interest from academia, industry, and the public. The traffic safety, fuel efficiency and enhanced driver experience are the main motivations for automated and connected vehicles. The connected automated vehicles are considered as mitigations of issues such as traffic congestion, road safety, inefficient fuel consumption and pollutant emissions that current road transportation system suffers from [1]. There are still some challenges reported by the research group of [1] such as: 1. Ideal working conditions of the communication channel (e.g., no packet loss, communication failure, noise, etc.). 2. Perfect knowledge of vehicle dynamics (vehicle parameters, road friction conditions, etc.). 3. Perfect knowledge of the positions of the vehicles. Additional investigation is required to understand how the aforementioned uncertainties affect cooperating driving scenarios
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