Motion control considering human driver characteristics for driving safety enhancement of connected and automated vehicles



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Driving safety enhancement is an eternal topic to study. Recent progress in sensing, computing, and vehicle-to-vehicle (V2V) communications has led the automotive industry into an information-rich era. Various advanced driving assistance systems (ADAS) and automated driving systems (ADS) have been invented for leveraging such information richness and helping a driver cope with challenging situations. However, the competition among different control algorithms for the limited onboard implementation resources, e.g., the processor time and memory space, intensifies. Reducing the implementation resources consumption while guarantying the control performance of ADAS/ADS, or equivalently, enhancing the control performance of ADAS/ADS with the same amount of implementation resources consumption, constitutes the first research subject of this dissertation. With both the ADAS and the human driver concurrently controlling a vehicle, ADAS should consider individuals' driving characteristics and preferences to mitigate the human-machine conflict and enhance the overall performance of the driver-vehicle system. Moreover, driving assistance systems should detect driver behavior variation online for adapting their intervention in real time. Designing human-centric ADAS becomes the second research subject of the dissertation. Finally, V2V communication can bestow drivers better situation awareness and thereby reduce traffic-related casualties. However, the Federal Communication Commission in the United States mandated that only one fixed channel, among the seven available channels within the Dedicated Short-Range Communication (DSRC) spectrum, could be employed to transmit the safety messages. This restriction can entail severe package transmission delay and impair the safety benefits of V2V communication. To reduce the transmission delay and package missing rate, we design and experimentally validate a dynamic channel selection algorithm, which forms the third research subject of the dissertation. The three research subjects, focusing on, respectively, vehicle computing unit level, single vehicle level, and vehicular network level, complement each other and answer the question of driving safety enhancement from a human-cyber-physical perspective.


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