Formation control for connected and automated vehicles on multi‐lane roads: Relative motion planning and conflict resolution

Abstract

Existing research has revealed that multi‐vehicle coordinated decision making and control can achieve an improvement in both traffic efficiency and driving safety. In the multi‐lane scenarios, a typical coordination method is multi‐vehicle formation control. The existing formation control methods predefine the formation switching process and have not considered or explained the collision‐free behaviour of vehicles in detail. In this paper, a multi‐lane formation control strategy for connected and automated vehicles (CAVs) is proposed. The planning framework is bi‐level, which can switch the structure of multi‐vehicle formation in different scenarios smoothly and effectively. In the upper‐level, the relative coordinate system is built to plan the collision‐free relative paths for vehicles. In the lower‐level, multi‐stage trajectory planning and tracking are modelled as an optimal control problem with path constraints. The case study verifies the function of the formation control method in three lane‐number‐changing scenarios. Large‐scale simulations in the lane‐drop bottleneck scenario are conducted under different input traffic volumes, and the numerical results indicate that the proposed formation control method reduces congestion and improves both traffic efficiency and fuel economy at high traffic volumes.

Research on a local path planning algorithm based on multivehicle collaborative mapping and a potential field method

“…On-board sensors collect information about the surrounding environment, use wireless communication networks to share their own environmental information with other vehicles, and realize collaborative perception through grid map integration. A large number of scholars at home and abroad have demonstrated through research that in various traffic scenarios, multi vehicle coordination can improve driving safety and efficiency compared to single vehicle path planning methods [29,30].…”

Priority Selection of Road Traffic Net-Works in Emergency Situations Based on Internet of Vehicles

“…The ac-celeration, de-celeration, lane changing, and overtaking behaviors of EVsare determined within the road section based on traffic density and the driving status of surrounding vehicles.Under normal circumstances, EVs will choose the inner lane when driving on real roads. However, vehicles on the inner lane will turn to adjacent lanes after receiving commands, and there are rarely cases of entering the outer vehicle lane from adjacent lanes [23][24][25]. Therefore, the study aims to focus on the complexity of the model and select a twoway two-lanehigh-way as the research object to analyze the entire process of vehicles automatically giving way in emergency situations.…”

Enhancing Multi-Robot Formation Control and Navigation Using Virtual Structures and Improved Path Planning Algorithms

“…The literature has explored a range of formation control problems, taking into account the sensing capability and interaction topology of the vehicle. The application of formation control has been using in different field of unmanned vehicles such an Unmanned Arial Vehicle (UAV) [2], Unmanned Surface Vehicle (USV) [3], Unmanned Ground vehicle (UGV) [4], [5], Autonomous Underwater Vehicle (AUV) [6], Mobile Robots [7] and also in the on-road vehicles [8]. The applications of formation control methods are utilizing in various sectors such as military surveillance and combats by UGVs, agricultural approaches [9], transportations, energy efficiency and fuel cost minimization and these aspects are attracting researchers and manufacturers to research on researching on formation control [4] showed in Fig.…”