Abstract
In this paper, we aim to optimize the process of Connected and Automated Vehicles (CAVs) merging at a traffic intersection while guaranteeing the state, control and safety constraints. We decompose the task of automatic merging for all the CAVs in a control zone around a merging point into same-lane safety constraints and different-lane safe merging, and implement these requirements using control barrier functions (CBFs). We consider two main objectives. First, to minimize travel time, we make the CAVs reach the road maximum speed with exponentially stabilizing control Lyapunov functions (CLF). Second, we penalize energy consumption as a cost in an optimization problem. We then decompose the merging problem into decentralized subproblems formulated as a sequence of quadratic programs (QP), which are solved in real time. Our simulations and comparisons show that the method proposed here outperforms ad hoc controllers used in traffic system simulators and provides comparable results to the optimal control solution of the merging problem in earlier work.
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