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Autonomous vehicles offer many benefits to driving, including safety, comfort, and efficiency. The significant extended benefit of autonomy in traffic comes from the connectivity through communications among vehicles, vehicle and infrastructure, and vehicle and other road users (also known as V2V, V2I, and V2X.) Connected Autonomous Vehicles or CAVs connectivity allows automated trajectories, plans, and coordinated speed adaptations that are safer, enhance traffic throughput and reduce energy consumption. CAVs will have more comprehensive situational awareness in the vehicle's vicinity for safety assurance. Driving tasks like lane changing, merging, navigating automated intersections, and various collision avoidance scenarios benefit significantly from the connectivity and the enhanced collective perception of the surrounding areas. Appropriate algorithms based on CAVs' connectivity data can also improve driving task decision-making and navigation plans within the traffic. This talk presents a few examples of advanced methods and algorithms that demonstrate various benefits and challenges of CAVS through simulation and laboratory experiments. It serves as an overview referring to reference co-authored articles by the presenter.
Azim Eskandarian (Mon,) studied this question.