Importance

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Abstract

A system and methods are provided for implementing mitigation action selection, which utilizes a cooperative approach that recognizes how many other connected vehicles are in the vicinity and considers other factors for determining which control action to undertake for congestion mitigation. Various parameters in combination with traffic models are used to evaluate the susceptibility of each lane to propagation of disturbances. For example, a connected vehicle may first request a lane change, and an impact of that lane change on traffic is analyzed. If feasible, the lane change and define an optimal control action to mitigate any disturbance on traffic. If the impact is considered significant, then the lane change request may be denied. Additionally, a learning approach can be used to reinforcement learning model to perform the decision-making process. Accordingly, an optimal control action that lessens the effect of disturbances on traffic congestion can be determined for vehicles.

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First Claim