Connected and automated vehicle platoon maintenance under communication failures

https://doi.org/10.1016/j.vehcom.2022.100467Get rights and content

Highlights

  • A dynamic communication topology-based CAV-following model is proposed.

  • An improved IDM named Dynamic Cooperative Intelligent Driver Model (DC-IDM).

  • An adaptive Kalman filter (AKF) is used to reduce the impact of perceptual errors on platoon status.

  • Simulations are presented to evaluate the impact of communication failures on a platoon.

  • The effectiveness of the proposed method is verified in multiple scenarios.

Abstract

Connected and automated vehicles (CAVs) use vehicle-to-vehicle (V2V) wireless communication to drive in platoons, which can improve traffic efficiency and reduce energy consumption. However, some vehicles in a platoon may experience communication failures for various reasons, such as packet loss, signal blocking, or damage to vehicle communication module hardware. This will result in two phenomena: the platoon communication topology will be changed, and the behavior of vehicles without communication capabilities will degenerate to that of automated vehicles (AVs). The platoon will be unstable under the influence of these two phenomena. To mitigate the impact of these two phenomena on platoons, we propose a dynamic communication topology-based CAV-following model. This model takes into account changes in communication topology in a platoon due to communication failures or perceptual errors resulting from vehicle degradation. Two core features ensure that this model is able to perform platoon maintenance under communication failure. First, the CAVs in a platoon dynamically adjust the weight values of the acquired driving information based on the communication topology. Second, an adaptive Kalman filter (AKF) method is applied to reduce perceptual error. The effectiveness of the platoon maintenance scheme proposed in this study is verified by a series of simulation experiments. The results show that the proposed model can effectively maintain CAV platoons regardless of communication failures under steady-state or non-steady-state traffic flows. This finding indicates that CAV designers should design control schemes that take into account possible changes in the communication topology. Furthermore, our method has potential advantages for improving the platoon stability of CAVs driving on roads where communication is poor.

Introduction

Connected and automated vehicles (CAVs), which are typically characterized by vehicle-to-vehicle (V2V) communication capabilities [1], are considered to be the future of vehicle technology. A vehicle can transmit its precise status information to the vehicle behind it via wireless communication. Based on this information, rear vehicles can make more accurate and timely driving judgments [2]. In this way, multivehicle group driving and platoon formation can be realized, which can improve traffic efficiency [3] and driving safety [4] and reduce energy consumption [5].

Most previous studies on vehicle platoons are based on an important implicit assumption that V2V communication is ideal and that no communication failure will occur [6]. However, a connected state is not always guaranteed for V2V communication in practice. Various factors, such as packet loss, poor communication due to signal shielding, or damage to communication hardware [7], [8], [9], [10], [11], [12], [13], may cause communication to fail. When communication with some vehicles in a platoon has failed, there are two types of vehicles in the platoon: vehicles with and without communication capabilities. The status of a platoon with partial communication failure is significantly different from that of a platoon consisting purely of CAVs. The changes to the platoon include two main points: communication topology changes and perception errors from non-communicating vehicles. These will affect platoon stability and traffic flow efficiency [3], [14], [15].

Therefore, to maintain platoon stability, it is crucial to have a model adapt to changes in the platoon after some vehicles lose their communication capabilities. First, the model must determine how the vehicles in the platoon that still have the ability to communicate can obtain effective information about the vehicles ahead after communication failure occurs for some vehicles. In other words, the model should be clear in terms of which vehicles ahead will continue to provide status information that can be utilized, and the weights of the obtained information should be automatically updated. Second, the model should take into account possible perceptual errors and mitigate those errors to the lowest possible level.

Addressing the above problems should make it possible for a platoon to remain stable when communication failures occur. To this end, this study proposes a unified following model for CAVs. The model is able to automatically adapt to the changes in communication topology caused by communication failures and reduce the impact of perceptual errors on a platoon. The contributions of this study are as follows.

(1) We propose a unified dynamic communication topology-based CAV-following model that can adapt to topological changes due to communication failures and considers perceptual errors.

(2) The intelligent driver model (IDM) is adapted into a dynamic communication topology-based CAV-following model. The new model is named the dynamic cooperative intelligent driver model (DC-IDM).

(3) The effectiveness of methods for platoon maintenance is verified by simulation experiments in various scenarios.

This paper is organized as follows. Related work is summarized in Section 2. Section 3 presents a description of the research problem. Section 4 introduces the dynamic communication topology-based CAV-following model. Section 5 describes how the adaptive Kalman filter AKF is used in the model to eliminate perceptual errors due to communication failures. The simulation results are presented in section 6. Section 7 concludes the paper.

Section snippets

Related works

Researchers have recognized the importance of high-quality communications for CAV capability enhancement. For example, [16] mentions, “The performance and effectiveness of a vehicle platoon rely on the topology of information flow and quality of communications.” However, ideal communication is currently difficult to achieve. Inevitably, there is communication packet loss and other phenomena that cause some vehicles to experience brief communication failures. Improving platoon performance in the

Problem definition

The communication relationship among the vehicles in a platoon is described by the communication topology, which defines from which vehicles in the platoon information can be obtained for driving judgments. Taking the platoon depicted in Fig. 1(A) as an example, each vehicle in the platoon can drive based on the state information of the two preceding vehicles; this is a typical MPF communication topology. However, one of the vehicles in the platoon may experience a communication failure. A

Model of the dynamic communication topology

We model the communication topology among the vehicles in a platoon as follows. The communication topology is described as a directed graph G=(V,E), where V={1,2,,n} is the set of nodes, representing the vehicles in the platoon, and EV×V={e1,1,e2,1,,ei,j} is the set of edges, representing communication connections. The existence of an edge ei,j=1 means that vehicle i can receive information from vehicle j; otherwise, ei,j=0. The adjacency matrix A(G) of graph G can be expressed as shown in

Perceptual error elimination

When communication failures occur in a platoon, some vehicles must drive using information that contains errors, and the data input to the model are expressed as shown in Eq. (11). If these erroneous data were to be used directly in the model, this would lead to changes in the platoon state and might even prevent the platoon from being maintained. To improve the accuracy of sensor information, filtering is typically applied because of its simplicity and efficiency, making it suitable for the

Simulations and results

To verify the effectiveness of our proposed method for platoon maintenance after communication failure, the DC-IDM was used in simulation experiments. Perceptual errors were randomly added into the model.

Conclusion

The purpose of this study was to compensate for changes in platoon status that may result from communication failures in some vehicles in a platoon, thereby allowing the platoon to maintain driving conditions that are as stable as possible. To this end, we proposed a dynamic communication topology-based CAV-following model. This model can adapt to the communication topology changes that occur when communication with some vehicle fails and allows every vehicle in the platoon to dynamically

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This study was supported by the National Natural Science Foundation of China (U1964206).

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