Abstract
Connected and automated vehicles (CAVs) are a very promising alternative for reducing fuel consumption and improving traffic efficiency when vehicles merge at on-ramps. In this study, we propose a graph-based method to coordinate CAVs to merge at the highway ramp. First, the optimized vehicles were divided into groups to pass the merging point. Then we built a directed graph model for each group of vehicles, where each path of the graph corresponds to one of all possible merging sequences. The improved shortest path algorithm is proposed to find the optimal merging sequence for minimizing total fuel consumption. The results of the simulation showed that the proposed graph-based method reduced fuel consumption and ensured high traffic efficiency; moreover, the vehicles can form a platoon after passing the merge point.
Highlights
Optimal On-Ramp Merging of Traffic jams that occur at highway on-ramps are a significant challenge to overcome to improve traffic efficiency [1,2]
With the help of vehicle to everything technology (V2X) [5,6], including vehicle to vehicle (V2V) and vehicle to infrastructure (V2I), Connected and automated vehicles (CAVs) can shorten the gaps between vehicles, improve response speed and increase traffic efficiency
Our main contributions in this paper are as follows: (1) to establish a graph-based optimal on-ramp merging sequence model using predicted vehicle fuel consumption as the weight; (2) to solve the problem in real-time and present an improved shortest path algorithm with the quadratic polynomial computational complexity of O(N2 ) complexity, where N denotes the number of vehicles; (3) to conduct vehicle motion planning so that a group of vehicles passing through the merging point at the highway on-ramp is made to form into a platoon with a constant distance between vehicles
Summary
Optimal On-Ramp Merging of Traffic jams that occur at highway on-ramps are a significant challenge to overcome to improve traffic efficiency [1,2]. More intelligent management systems have been developed to coordinate CAVs to merge at freeway on-ramps efficiently [8,9] These systems roughly contain two main modules: merging sequences optimization and motion planning [10,11]. Our main contributions in this paper are as follows: (1) to establish a graph-based optimal on-ramp merging sequence model using predicted vehicle fuel consumption as the weight; (2) to solve the problem in real-time and present an improved shortest path algorithm with the quadratic polynomial computational complexity of O(N2 ) complexity, where N denotes the number of vehicles; (3) to conduct vehicle motion planning so that a group of vehicles passing through the merging point at the highway on-ramp is made to form into a platoon with a constant distance between vehicles
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