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   Autonomous Vehicles Should Start Small, Go Slow. Self-driving vehicles can already work well on campuses where traffic moves slowly
 

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2. Learning from All Vehicles

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3. Learning from All Vehicles

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4. A Highway Toll Lane Framework that Unites Autonomous Vehicles and High-occupancy Vehicles

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5. Distributed Constraint Optimization Problem for Coordinated Response of Unmanned Aerial Vehicles and Ground Vehicles

   https://doi.org/10.1109/CISS50987.2021.9400249  

   Darko, Justice; Folsom, Larkin; Deshpande, Niharika; Park, Hyoshin (March 2021, The 55th Annual Conference on Information Sciences and Systems)

   While there has been significant progress on statistical theories in the information community, there is a lack of studies in information-theoretic distributed resource allocation to maximize information gain. With advanced technologies of unmanned aerial vehicles (UAVs) in response to corresponding revised FAA regulations, this study focuses on developing a new framework for utilizing UAVs in incident management. As a result of new computing technologies, predictive decision-making studies have recently improved ERV allocations for a sequence of incidents; however, these ground-based operations do not simultaneously capture network-wide information. This study incorporates a real-time aerial view using UAVs with three key improvements. First, aerial observations update the status of the freeway shoulder, allowing an ERV to safely travel at full speed. Second, observing parameters of the congestion shockwave provides accurate measurements of the true impact of an incident. Third, real-time information can be gathered on the clearance progress of an incident scene. We automate UAV and ERV allocation while satisfying constraints between these vehicles using a distributed constraint optimization problem (DCOP) framework. To find the optimal assignment of vehicles, the proposed model is formulated and solved using the Max-Sum approach. The system utility convergence is presented for different scenarios of grid size, number of incidents, and number of vehicles. We also present the solution of our model using the Distributed Stochastic Algorithm (DSA). DSA with exploration heuristics outperformed the Max-Sum algorithm when probability threshold p=0.5 but degrades for higher values of p. 

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