Abstract
Vehicle safety, ride comfort, formation control and fuel economy are the main control objectives of cooperative adaptive cruise control (CACC) of vehicle strings. This paper considers the multi-objective CACC problem of a mixed vehicle string, i.e., the vehicle string is composed of CACC-, ACC- and human-driven vehicles. A new switching multi-objective receding horizon predictive control method is proposed for CACC of mixed vehicle strings. The longitudinal models of ACC- and human-driven vehicles are used to estimate the driving behaviors of the individual vehicles. Then three multi-objective predictive controllers are separately designed for different vehicle scenarios. According to different preceding vehicles, the multi-objective predictive controllers are switched online in the normal situation, and in the dangerous situation, the predictive controller is switched to the safety controller to ensure driving safety of vehicles in the string. To verify the effectiveness of the proposed control method, a mixed vehicle string consisting of six cars is used in the simulation experiment with complex traffic scenarios.
Highlights
In the recent years, increasing vehicles cause many problems in most cities such as traffic jams, pollution and frequent traffic accidents [1]–[4]
Note that here we focused on the cooperative adaptive cruise control (CACC) issue of constrained mixed vehicle strings travelling a single lane
The leading vehicle is an automated car that broadcasts its information to the other vehicles through the V2X network, the second, fourth and sixth vehicles are the CACC-driven cars, the third vehicle is a human-driven car, and the fifth vehicle is an adaptive cruise control (ACC)-driven car
Summary
In the recent years, increasing vehicles cause many problems in most cities such as traffic jams, pollution and frequent traffic accidents [1]–[4]. Among many technologies to solve these problems, adaptive cruise control (ACC) is a widely adopted one in the context of current traffic infrastructure [5]–[8]. ACC can take less time from finding dangerous situation to reacting than human’s driving, which greatly improves the driving safety of vehicles. ACC can quickly react to the traffic scenarios of front vehicles in advance and it reduces the workloads of drivers and improves the ride comfort of cars [12]–[15]. In order to further increase the traffic capacity of roads, recently cooperative ACC (CACC) has been developed by the wireless Vehicle-to-Everything (V2X) communication network [16], [17].
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