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Automated Vehicle Lane Centering System Requirements Informed by Resilience Engineering and a Solution Using Infrastructure-Based Sensors

Publication Type
Journal
Journal Name
IEEE Access
Publication Date
Page Numbers
97605 to 97620
Volume
12

Advancements in driving automation systems such as Lane Keeping Assist has shown improvements in safety by reducing traffic accidents. Nonetheless, despite the promise of these technologies, some studies have shown statistical data that such systems have a poor resilient operation. Therefore, there is a need to design more robust and resilient Advanced Driver Assistance Systems (ADAS) while broadening their operational design domain. This study proposes a set of non-functional system level requirements informed by applying Resilience Engineering (RE) principles to a Lane Centering (LC) system and comparing it to a conventional LC system when subjected to disruptive events. The LC system was integrated with a monitoring system based on infrastructure sensor technology as a potential solution to address the proposed non-functional system-level requirements. These two LC systems were developed in the CARLA simulator and tested in a lane line occlusion and adaptive speed scenario. RE metrics such as the resistance and recovery were measured to assess the ability of the system to sustain and recover from disruptive events. Simulation results indicate that in the lane line occlusion scenario, the LC system with the proposed requirements achieved a 135% increase in the resistance score and an 11% increase in the recovery score compared to the conventional LC system. In contrast, in the adaptive speed scenario, the LC system with the proposed requirements achieved a 123% increase in the resistance score but a 6% decrease in the recovery score compared to the conventional LC system. These results demonstrate that the proposed safety non-functional system requirements can be used cope with more unknown events and that RE principles can provide the stepping stones to design and enable resilient automated driving operations.