Lab of Intelligent and Connected Vehicles

Coordinated Control - Multi-vehicle Coordinated Control

1. Intersection Signals and Vehicle Coordinated Control Method in Mixed Traffic Environment

  • Research Background

As a common spot in cities, intersection is an important factor affecting road traffic efficiency and vehicle fuel economy. When traffic congestion occurs, intersection signals will lead to frequent start-stops and idles of vehicles, resulting in the decline of vehicle fuel economy. The signal light control method with fixed time distribution is difficult to solve the traffic congestion problem. The appearance of intelligent and connected vehicles (ICVs) provides the possibility for further traffic optimization. On the one hand, it can obtain such traffic environment information as signal timing to optimize the driving track of its own; on the other hand, it can acquire information of other vehicles around, and improve the regional traffic efficiency through self-control. However, the popularity of ICVs still needs decades, and the existing research on vehicle coordinated control algorithm for the coexistence of human driving vehicles (HDVs) and connected automatic driving vehicles fails to fully consider the driver's driving information. Based on the mixed traffic environment, this research proposes the queue formation method of ICVs and hierarchical optimization control method of signal lights, which can optimize the traffic efficiency and fuel economy in accordance with different market permeability.

  • Research Achievements

In consideration of the driving situation of HDVs, a hybrid queue formation method under mixed traffic is proposed. By combining the ICVs and HDVs into a "1 + N" queue mode, ICVs can actively guide the following HDVs through the intersection. By means of system analysis, the necessary and sufficient conditions to ensure the stability and controllability of the hybrid queue are proved, which provides a theoretical basis for the control of the hybrid queue.

By studying the vehicle-following model of HDVs, a method of determining the optimal following distance and the maximum number of passable vehicles under fixed timing is proposed. For the process of a fixed length queue entering an intersection, an optimal control model of hybrid vehicle queue is presented. The optimal control based on pseudo spectral method is used for trajectory planning, and thus the optimization of fuel economy can be realized.

Drivers need to be guided by signal lights when driving through intersections. Therefore, aiming at the proposed mixed traffic queue model, a reservation signal control method under mixed traffic is designed. This research extends the common "first come, first served" priority allocation model of single ICV, designs a comprehensive priority model allocation method in accordance with queue length in mixed traffic, realizes the dynamic allocation of signal light time under eight phases, and comprehensively optimizes the traffic efficiency of the overall intersection. A simulation platform based on microscopic traffic flow model is built to verify the effectiveness of the hybrid queue algorithm.

Leading Research Member: CHEN Chaoyi

2. Research on Multi-vehicle Coordinated Control System under Unreliable Communication

  • Research Background

In recent years, with the development of communication technology, the related connected technology has brought more possibilities for intelligent vehicles to break through the bottlenecks existing in a number of sections including perception, decision-making and control, and improve the performance of the current intelligent vehicles in the aspects of safety, efficiency, comfort and energy saving. However, in the application process of intelligent connected technology, some inherent defects or restrictions of the existing communication technology inevitably affect the application scope and performance, such as network delay, packet loss, quantization error, time-varying topology, channel attenuation and bandwidth limitation. Some factors even seriously threaten the safety of the application in some cases. Therefore, it is necessary to study vehicle control methods and theories under unreliable communication factors, to set the theoretical foundation for safe, reliable and high performance vehicle control under existing communication restrictions, to provide the design ideas for degraded controller in the case of some sudden communication failures, and to improve the safety of intelligent connected vehicle system.

  • Research Achievements

The original research in the laboratory considered the Markov Linear Jump System with logarithmic quantitative feedback, derived Riccati inequality in random sense, and calculated the jump control parameters corresponding to different modes using the method of solving linear matrix inequality. Based on the above methods, the study describes the network log quantization feedback control system with finite jump delay by matrix augmentation. The control parameters of stabilization in different delay states are obtained. The control amount of current time stabilization is calculated by using the control amount of the cache limited step historical time. In the actual control, the corresponding control parameters are switched according to the communication delay at the current time, in order to stabilize the system. Through the simulation test of the transverse and longitudinal control scenarios of the ICVs and the comparison with the conventional controller control performance, a feasible controller design idea for ICV control with jump delay is provided and verified in the study.

Leading Research Member: PAN Ji'an

Representative Paper:

  1. Pan J, Xu Q, Li K, et al. Controller Design for V2X Application Under Unreliable Feedback Channel[C].2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019: 2496-2502.

 

3. Common Decision Making and Formation Control Method for Intelligent and Connected Vehicle Groups in Multiple Traffic Scenarios

  • Research Background

Traditional control methods for Intelligent and Connected Vehicles (ICVs) usually focus on simple and single traffic scenarios, which makes it difficult to be applied in the real world or switched among different scenarios. This project aims to propose a common formation control method for ICVs that can cover multiple traffic scenarios and improve traffic efficiency and fuel economy.

  • Research Achievements

The formation control method for ICVs is proposed based on the concept of coordinated assignment. In the scenario of intelligent highway that contains on-ramps and off-ramps, the geometric topology of the formations is generated according to the number of lanes and vehicles. The Hungarian Algorithm is adopted to solve the vehicle-to-goal assignment problem. Bezier curves are used to generate trajectories for vehicles. The proposed method extends the traffic scenarios of coordinated control of ICVs while improving traffic efficiency, driving safety and fuel economy.

The coordinated control method for ICVs in multi-lane intersections without signal lights is proposed. The conflict model for traffic flow movements in multi-lane intersections is built and the depth-first spanning searching algorithm is chosen to calculate conflict-free passing sequence for vehicle groups. The vehicle regrouping method is proposed to reform sub-formations where vehicles have the same turning expectations. This method can cover the multi-lane intersection scenario and improve traffic efficiency and fuel economy compared with the existing methods.

Leading Research Member: CAI Mengchi

Representative Paper:

  1. Cai M, Xu Q, Li K, et al. Multi-lane Formation Assignment and Control for Connected Vehicles[C]//2019 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2019: 1968-1973.

 

4. Performance Study and Optimal Controller Design in Mixed Traffic System

  • Research Background

The traditional technology of ICV is mostly aimed at the condition that all vehicles are operated automatically. In the actual process of promoting ICV technology, there is bound to be a long-term transitional stage, that is, the mixed traffic condition that ICV and HDV coexist. It is of great significance to study the hybrid transportation system, and to design ICV technology for this environment, so as to understand the influence mechanism of ICV on the transportation system, and enhance the positive role of ICV in the transition stage.

  • Research Achievements

In order to study the influence mechanism of ICV on the traffic system, the stability, controllability, stabilizability and accessibility of the closed-ring hybrid traffic system are analyzed theoretically. The stabilizability of the ring hybrid traffic system composed of one ICV and several HDVs with homogeneous / heterogeneous dynamic characteristics is proved under certain conditions. The corresponding solutions are obtained through ICV's active influence on improving the upper bound of traffic flow speed. And the internal factors and development potential of ICV in reducing traffic disturbance and improving traffic efficiency under low permeability is revealed.

In order to handle with ICV’s control strategy design under the condition of the limited communication ability in mixed traffic, this research constructs an optimal controller under structural constraints, and uses the principle of sparsity invariance to make the convex relaxation of the original problem, so as to obtain the sub-optimal ICV control strategy. In addition, the traditional ACC, CACC and other strategies focus on the performance of ICV itself. This strategy directly optimizes the overall traffic system by controlling ICV, so that ICV can significantly improve the traffic performance under low permeability.

Leading Research Member: WANG Jiawei

Representative Papers:

  1. Zheng Y, Wang J, Li K. Smoothing traffic flow via control of autonomous vehicles[J]. IEEE Internet of Things Journal, 2020.
  2. Wang J, Zheng Y, Xu Q, et al. Controllability Analysis and Optimal Controller Synthesis of Mixed Traffic Systems[C]//2019 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2019: 1041-1047.
Created: Mar 05, 2021 | 10:56