Abstract
The development of connected and automated vehicle (CAV) techniques brings an upcoming revolution to traffic management. The control of CAVs in potential conflict areas such as on-ramps and intersections will be complex to traffic management when considering their deployment. There is still a lack of a general framework for dispatching CAVs in these bottlenecks, which is expected to ensure safety, traffic efficiency, and energy consumption in real time. This study aimed to fill the technique gap, and a comprehensive cooperative intelligent driving framework is put forward to study the problem, which can be used in both on-ramp and intersection scenarios. Based on a multi-objective evolutionary algorithm, CAVs are denoted as a sequence to be searched in solution space, while a multitask learning neural network with adaptive loss function is implemented for optimization target feedback to surrogate the simulation test procedure. The simulation results show that the proposed framework can get satisfying performance with low time and energy consumption. It can reduce time consumption by up to 16.51% for the on-ramp scenario and 9.8% for the intersection scenario, while reducing energy consumption by up to 16.39% and 11.39% for the two scenarios. Meanwhile, an analysis of computation time is carried out, illuminating the flexibility and controllability of the new strategy.
Highlights
Connected and automated vehicles are considered to play an important role in improving traffic efficiency and saving energy [1]. e fickle driving behaviors can lead to a series of problems, including traffic congestion, energy consumption, and accident [2,3,4], but transport systems consisting of intelligent vehicles can make a difference using vehicle-to-everything (V2X) communication and advanced control techniques [5,6,7]
We choose the FCFS strategy as a baseline, whereas it is generally used in the domain. e iteration step and population size in multi-objective discrete evolutionary algorithm (MODEA) are set to 30 and 40, respectively
While in the on-ramp scenario, the gap between the two methods becomes more significant with the increase in connected and automated vehicle (CAV). us, the capability of global optimization of MODEA can be verified, while the rule-based FCFS method is regarded as weak to get satisfying solutions
Summary
Connected and automated vehicles are considered to play an important role in improving traffic efficiency and saving energy [1]. e fickle driving behaviors can lead to a series of problems, including traffic congestion, energy consumption, and accident [2,3,4], but transport systems consisting of intelligent vehicles can make a difference using vehicle-to-everything (V2X) communication and advanced control techniques [5,6,7].e development of connected and automated vehicles (CAVs) brings both opportunities and challenges to traffic management. Connected and automated vehicles are considered to play an important role in improving traffic efficiency and saving energy [1]. E fickle driving behaviors can lead to a series of problems, including traffic congestion, energy consumption, and accident [2,3,4], but transport systems consisting of intelligent vehicles can make a difference using vehicle-to-everything (V2X) communication and advanced control techniques [5,6,7]. E development of connected and automated vehicles (CAVs) brings both opportunities and challenges to traffic management. Considering the traffic environment composed of CAVs, traffic signals can be eliminated because the information on the road can be fully obtained [13], while the vehicles on the road can be fully controlled. Ann and Colombo [15] pointed out that an effective cooperative driving framework can work in different traffic scenarios such as intersections, merging roadways, and roundabouts. On account of the significance of cooperative driving, the researchers proposed many theoretical
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