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COLLISION AVOIDANCE FRAMEWORK FOR AUTONOMOUS VEHICLES UNDER CRASH IMMINENT SITUATIONS
Ninety-five percent of all roadway crashes are attributed fully or partially to human error, and a multitude of safety-related programs, policies, and initiatives have seen limited success in reducing roadway crashes and their accompanying fatalities, injuries, and property damage. For this reason, safety professionals have lauded the emergence of autonomous vehicles (AVs) as a promising palliative to the persistent problem of road crashes. Such optimism is reflected in recent literature that have argues from a conceptual standpoint, that road safety enhancement will be one of the prospective benefits of AV operations because automation removes humans from vehicle driving operations and therefore criminates or mitigates human error. It can be argued that the safety benefits of AVs will be manifest when AV market penetration reaches 100%. However, it seems clear from a practical standpoint that the transition from a system of exclusively human-driven vehicles (HDVs) to that of exclusively AVs will not only be necessary but also an arduous journey. This transition period will be characterized by heterogeneous traffic, where human-driven vehicles (HDVs) and AVs share the road space, and whence the prospective safety benefits of AVs may not be fully realized due to human error arising from the HDV operations in the mixed traffic space. These traffic conflicts, which may lead to collisions, could arise from any of several contexts of driving maneuvers, one of which is aggressive lane changes, the focus of this thesis. From the literature, it is clear that lane changing is inherently more collision-prone compared to most other maneuvers including car following, and therefore the consequences of errant human driving behavior such as inattention of misjudgment during lane changing, are more severe. To address this problem, this thesis developed a control framework to be used by AVs to help them avoid collision in a mixed traffic stream with human drivers who exhibit aggressive lane-changing behavior. The developed framework, which is based on a Model Predictive Control (MPC) approach, is designed to control the AV’s movements safely by duly accommodating potential human error from the HDVs that could otherwise lead to any of two common collision patterns: rear-end and side-impact. Further, the thesis investigated how connectivity between the HDVs, and AVs could facilitate joint operational decision-making and sharing of real-time information, thereby further enhancing the safety of the entire traffic stream. Finally, the thesis presents the results of driving simulations carried out to test and validate the performance of the control framework under different traffic conditions.