Vehicle Platoon Research Articles

A deep reinforcement learning based distributed control strategy for connected automated vehicles in mixed traffic platoon

This paper proposes an innovative distributed longitudinal control strategy for connected automated vehicles (CAVs) in the mixed traffic environment of CAV and human-driven vehicles (HDVs), incorporating high-dimensional platoon information. For mixed traffic, the traditional CAV control method focuses on microscopic trajectory information, which may not be efficient in handling the HDV stochasticity (e.g., long reaction time; various driving styles) and mixed traffic heterogeneities. Different from traditional methods, our method, for the first time, characterizes consecutive HDVs as a whole (i.e., AHDV) to reduce the HDV stochasticity and utilize its macroscopic features to control the following CAVs. The new control strategy takes advantage of platoon information to anticipate the disturbances and traffic features induced downstream under mixed traffic scenarios and greatly outperforms the traditional methods. In particular, the control algorithm is based on deep reinforcement learning (DRL) to fulfill car-following control efficiency and further address the stochasticity for the aggregated car following behavior by embedding it in the training environment. To better utilize the macroscopic traffic features, a general platoon of mixed traffic is categorized as a CAV-HDVs-CAV pattern and described by corresponding DRL states. The macroscopic traffic flow properties are built upon the Newell car-following model to capture the characteristics of aggregated HDVs' joint behaviors. Simulated experiments are conducted to validate our proposed strategy. The results demonstrate that the proposed control method has outstanding performances in terms of oscillation dampening, eco-driving, and generalization capability.

Online fault classification in Connected Autonomous Vehicles using output-only measurements

Different health-monitoring techniques were considered in the literature to enhance the safety and stability of Connected Autonomous Vehicle (CAV) platoons. Mitigating these faults is faster and more reliable if the fault structure is known. In this paper, we propose using transmissibility operators, which are relationships that relate a set of velocities with another in the platoon, to classify the faults. Transmissibility operators were shown to be exceptional in signal estimation. However, it is also shown to be noncausal and thus can only be used offline. To this end, we propose using Data Aggregation (DAgger), an extension to imitation learning, to transfer the classification experience from transmissibility operators to a novice machine learning agent to be used online. The integration of transmissibility-DAgger gives the ability to adapt to new fault classes without the need to re-train the diagnosis model from the beginning. A heterogeneous CAV platoon was modeled with three different faults separately. These faults are actuator disturbances, false data injection attacks, and communication time delays. The classification scheme depends on estimating the faulty signal in the case of each fault class. Next, the measured faulty signal is compared with the three estimations, and the closer fault estimation to the measured one is considered the actual fault on the platoon. The proposed algorithm is then tested on the platoon model and then applied to an experimental setup that consists of three autonomous robots. The proposed results are compared with six different machine learning classification models. The overall classification accuracy achieved was 95.8% for the experiment using the proposed approach.

Distributed integrated sliding mode control via neural network and disturbance observer for heterogeneous vehicle systems with uncertainties

A distributed sliding mode control based on neural network and disturbance observer is investigated for heterogeneous vehicle systems in this paper. The vehicle systems in this paper consider both parameter uncertainty and disturbance. First, the radial basis function neural network is applied to estimate the parameter uncertainty owing to its universal approximation ability, and the disturbance observer is designed to compensate for the external disturbance. Compared with the existing adaptive methods, the disturbance observer can estimate the external disturbance directly and effectively. Then, constant time headway policy is employed to regulate the inter-vehicle distance and improve the string stability. Unlike most existing sliding mode control strategy for vehicle platoons, the string stability is achieved via employing sufficient conditions on the control parameters rather than employing coupled sliding mode control. Afterward, modified constant time headway policy is designed to reduce the effect of nonzero initial inter-vehicle spacing and improve the traffic density. Finally, the simulation results with constant time headway policy and modified constant time headway policy are provided to demonstrate the effectiveness and advantages of the proposed approaches.

Explicitly incorporating surrogate safety measures into connected and automated vehicle longitudinal control objectives for enhancing platoon safety

The concepts of Connected and Automated Vehicles (CAV) and vehicle platooning have generated high expectations regarding the safety performance of future transportation systems. Existing CAV longitudinal control research primarily focuses on efficiency and control stability, by considering different inter-vehicle spacing policies. In very few cases, safety was also considered as a constraint, but not in the main control objectives. Theoretically, stability can only guarantee that CAV platoons eventually achieve an equilibrium state but is unable to promise safety along the process of achieving equilibrium. It is important to note that CAV does not mean absolutely safe, and its longitudinal or platoon control safety performance depends on how the control algorithms are designed, how accurately it can detect and predict its lead vehicle’s (could be a human-driven vehicle) next move, and other practical factors such as control and communication delays. To optimize CAV platoon safety, this study integrates surrogate safety measures (SSM) and model predictive control (MPC) into CAV longitudinal control for trajectory optimization. SSM has been widely adopted for modeling the safety consequences of various vehicle control strategies and identifying near-crash events from either simulated or field-captured traffic data. This study directly incorporates three typical SSM into the longitudinal control objectives of CAV and constructs a state-space MPC algorithm to model how these SSM vary as a result of CAV dynamics. Numerical examples are provided to show the performance of these SSM-based optimal CAV longitudinal control methods under traffic flow perturbations. To further confirm the necessity of explicitly considering SSM in CAV longitudinal control and its effectiveness in reducing rear-end collision risk, the proposed methods are compared with three classical longitudinal control models that do not consider SSM based on microscopic traffic simulation. It is noted that all SSM-based optimal control methods perform better than others as manifested by some key risk indicators, demonstrating the importance of explicitly considering SSM and safety in CAV longitudinal control.

Motion control of connected and automated vehicles in a lane‐changing scenario with guaranteed tracking performance

AbstractThe coupled, nonlinear vehicle dynamics and multi‐vehicle cooperative strategies make the lane‐changing maneuver control of vehicle platoons a challenging task. This paper presents the dynamic modeling and motion control of connected and automated vehicles (CAVs) for lane‐changing maneuver, utilizing the ideology of constraint‐following servo control. To guarantee a transient and steady tracking performance, a series of equality constraints and bilateral inequality constraints bounded by decaying functions or scalar constants are imposed on the dynamic state to formulate a constrained dynamical system. Since the original Udwadia‐Kalaba (UK) approach cannot handle inequality constraints and underactuated systems, a creative diffeomorphism method is applied to transform the bounded state into an unbounded one. Then, based on the UK approach and Lyapunov stability theory, a novel constraint‐following controller is designed to render the transformed states asymptotically stable in a full actuated system, and to achieve uniform stability performance in an underactuated system. Finally, numerical simulations are used to validate the effectiveness of the proposed control methodology.

Stability of connected and automated vehicles platoon considering communications failures

As a form of a future traffic system, a connected and automated vehicle (CAV) platoon is a typical nonlinear physical system. CAVs can communicate with each other and exchange information. However, communication failures can change the platoon system status. To characterize this change, a dynamic topology-based car-following model and its generalized form are proposed in this work. Then, a stability analysis method is explored. Finally, taking the dynamic cooperative intelligent driver model (DC-IDM) for example, a series of numerical simulations is conducted to analyze the platoon stability in different communication topology scenarios. The results show that the communication failures reduce the stability, but information from vehicles that are farther ahead and the use of a larger desired time headway can improve stability. Moreover, the critical ratio of communication failures required to ensure stability for different driving parameters is studied in this work.

Proximal Policy Optimization-Based Driving Control Strategy of Connected Cruise Vehicle Platoons to Improve Traffic Efficiency and Safety

This paper investigates connected cruise control (CCC) based on the deep reinforcement learning algorithm to mitigate oscillations and improve traffic safety in stop-and-go waves. A proximal policy optimization (PPO)-based control strategy is proposed for connected and autonomous vehicles (CAVs) in a heterogeneous longitudinal car-following platoon. The method receives the velocity and position signals of n human-driven vehicles ahead through wireless vehicle-to-vehicle communication to obtain an appropriate driving behavior and improve traffic efficiency and safety in real-time. The effectiveness and advantage of the proposed strategy are verified by using traffic simulation software (Simulation of Urban Mobility) for two simulation scenarios of trajectory curves with noticeable acceleration changes of the leading vehicle and two sections of actual traffic speed data. The results show that the proposed PPO-CCC method can successfully suppress the speed oscillations and improve the travel efficiency of CAVs in the car platoon.

Development of Connected and Automated Vehicle Platoons With Combined Spacing Policy

Vehicle platoon has the potential to significantly improve traffic throughput and reduce fuel consumption and emissions and thus has attracted extensive attention recently. In this study, we propose a vehicle platoon of connected and automated vehicles (CAVs) with a combined spacing policy to enhance traffic performance. First, a combined spacing policy composed of the constant time gap (CTG) and constant spacing (CS) is formulated for the proposed vehicle platoon, where the leader adopts the CTG and the followers use the CS policy. Based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$h_{2}$ </tex-math></inline-formula> -norm string stability criteria, the notion of exogenous-head-to-tail string stability is newly introduced, and the sufficient conditions of the local stability and string stability in the frequency domain are derived using the Routh-Hurwitz criterion and Laplace transform respectively. Numerical experiments are conducted to validate the string stability. The effectiveness of the proposed vehicle platoons is verified by theoretical analysis and numerical experiments using two typical scenarios and several measurements of effectiveness (MOE) in various performance aspects, including efficiency, safety, energy, and emission. The results show that the proposed vehicle platoon performs better than the CS-based vehicle platoon in all aspects except for efficiency. It also indicates that the proposed vehicle platoon has obvious advantages over the CTG-based vehicle platoon in efficiency and safety aspects. The findings have demonstrated the merits of the combined application of CTG and CS policies for the vehicle platoon in enhancing stability and traffic performance.

Empirical Investigative Analysis on the Effect of Triangular and Gaussian Membership Function on Fuzzy-based Controlled Vehicle Platooning

Abstract: Fuzzy controlled vehicle platoon system provides a simplified and robust approach to achieving platoon string stability and uniform inter-vehicular gap keeping in autonomous vehicle platoon. However, the fuzzification and defuzzification method adopted affects the characteristics of the platoon to a large extent, while also determining velocity stability timing, although researcher select a fuzzification/defuzzification method based on comfort, familiarity or simplicity. This work undertakes to investigate the effect of fuzzification and defuzzification method on vehicle platoon, to provide evidence on the selection criteria and how it affects the controlled system. The results obtained show that the best performing combination was reported as triangular/centroid with 4.44 secs velocity stability for vehicle V3, and 1.91 secs distance stability for follower vehicle, when compared to Gaussian/MoM with 79.88 secs velocity stability V3 and 85.89 secs for follower vehicle being the worst performing combination

Robustly String Stable Longitudinal Control for Vehicle Platoons Under Communication Failures: A Generalized Extended State Observer-Based Control Approach

This paper investigates the longitudinal control of vehicle platoons with a focus on external disturbances, parameter uncertainties, and communication failures. When vehicle platoons encounter vehicle to vehicle wireless communication failures, the preceding vehicle’s acceleration is unavailable, which degrades the performance of the system. However, the existing achievements can not be directly used to address the aforementioned three issues. To this end, a generalized extended state observer-based control (GESOBC) law is contrived. First, the parameter uncertainties and the external disturbances are together regarded as a lumped disturbance. Then, a generalized extended state observer is designed to estimate the lumped disturbance and the preceding vehicle’s acceleration, separately. Based on the estimation, a composite controller consisting of a state feedback control part and an estimation-based feedforward control part is developed. Furthermore, it is proved that the proposed GESOBC method can guarantee the exponentially bounded stability of the individual vehicle and the input to state string stability of the whole vehicle platoon. Finally, numerical simulations are conducted to demonstrate the effectiveness and feasibility of the proposed method.

Switching formation control of multi-lane autonomous vehicle platoons robust to relative position measurement noises

This paper studies formation control of multi-lane platoons of vehicles, endowed with local positioning capabilities, under the influence of noises and inconsistencies of inter-vehicle relative position measurements. We propose a distributed two-level control framework that consists of a high-level relative position based distributed formation control scheme and low-level individual dynamic controllers of the platoon vehicles. The proposed formation control scheme utilizes deadzone based switching for robustness against sensor noises and adaptive longitudinal controllers for enabling the platoon of heterogeneous vehicles to track the desired platoon velocity.

Path Loss and Shadowing Modeling for Vehicle-to-Vehicle Communications in Terrestrial TV Band

Vehicle platooning is considered as one of the key use cases for vehicle-to-vehicle (V2V) communications. However, its benefits can be realized only with highly reliable wireless transmission. As the 5.9GHz frequency band used for V2V suffers from high congestion, in this paper, we consider the use of the terrestrial TV frequencies for intra-platoon communications. In order to be able to evaluate the potential of the new bands fully, propagation models for V2V communications at such frequencies are needed. Therefore, this paper reports new V2V propagation measurements and their modeling results. Particularly, we propose a Double Slope Double Shadowing model as the most accurate one, based on a comparison of various models using the Bayesian Information Criteria. We also investigate the space-time autocorrelation properties of the shadowing, which turned out to be dependent on the speed of vehicles. The proposed path loss and shadowing model differs from the ones proposed for the 5.9GHz band. Mostly, in favor of the TV band, as shown by, e.g., no statistically significant impact of a blocking car.

A New Topology for Homogeneous Vehicular Platoon under Disturbances

Vehicle platooning has attracted a lot of interest in research in the last decade as it introduced a solution to many problems in traffic flow, safety, fuel economy, and reducing CO2 emissions. As the cost reduction is crucial and necessary to the business’s sector, in this paper, communication cost reduction is achieved via proposing a new topology called Bidirectional Odd Leader (BDOL). The BDOL topology is advantageous in terms of cost compared to the BDL topology due to the BDOL is a relaxation of the BDL topology. The string stability of the dynamical system with the proposed topology is first proven through numerical comparison with BDL topology. Then, the performance of the proposed topology has been evaluated by intensive computer-based simulation. A homogeneous platoon consisting of eight vehicles is created and tested on a straight and curved track, while keeping a small desired inter-vehicle distance. We evaluated the performance of our topology, and tested the string stability in many scenarios. These scenarios include platoon moving on a straight track with or without disturbance, lastly a velocity disturbance produced by a curved track affecting the platoon is also investigated. The results demonstrate the effectiveness of the proposed topology in achieving the intended objectives.

Prescribed Performance Tracking Control for 2-D Plane Vehicle Platoons with Actuator Faults and Saturations

This paper studies the multi-lane fusion tracking control problem for the two-dimensional third-order nonlinear vehicle platoons with actuator faults and saturations. A novel class of performance functions is designed to guarantee that the platoon tracking errors under asymmetric constraints satisfy the pre-given performance. To achieve the multi-lane fusion tracking control, new third-order prescribed performance sliding-mode surfaces are proposed for nonlinear vehicle platoons. Further, the saturation steering wheel input and the fault-tolerant throttle/brake input are designed by using the adaptive neural network sliding mode control technique, respectively. They can steer the platoon tracking errors approach the predefined region in the preset finite time while avoiding collisions and maintaining communications. Finally, the effectiveness of the proposed control scheme is verified by using two simulation examples with comparisons.

A constrained optimal control framework for vehicle platoons with delayed communication

&lt;abstract&gt;&lt;p&gt;Vehicle platooning using connected and automated vehicles (CAVs) has attracted considerable attention. In this paper, we address the problem of optimal coordination of CAV platoons at a highway on-ramp merging scenario. We present a single-level constrained optimal control framework that optimizes the fuel economy and travel time of the platoons while satisfying the state, control, and safety constraints. We also explore the effect of delayed communication among the CAV platoons and propose a robust coordination framework to enforce lateral and rear-end collision avoidance constraints in the presence of bounded delays. We provide a closed-form analytical solution to the optimal control problem with safety guarantees that can be implemented in real time. Finally, we validate the effectiveness of the proposed control framework using a high-fidelity commercial simulation environment.&lt;/p&gt;&lt;/abstract&gt;

Learning Based Longitudinal Vehicle Platooning Threat Detection, Identification and Mitigation

The security of cyber-physical systems, such as vehicle platoons, is critical to ensuring their proper operation and acceptance to society. In platooning, vehicles follow one another according to an agreed-upon control law that determines vehicle separation. We show that a vehicle within a platoon and under the control of a malicious actor could cause collisions or decrease the efficiency of surrounding vehicles. This paper focuses on detecting, identifying, and mitigating so-called destabilizing attacks that could cause vehicle collisions. Our approach is decentralized and requires only local sensor information for each vehicle to identify the vehicle responsible for the attack and then deploy an appropriate mitigating controller that prevents collisions. We use a Deep Learning approach (Convolutional Neural Network) with various data preprocessing techniques to detect and identify the malicious vehicle. Results indicate that even with decent noise levels in range and relative speed data, we achieve accuracy up to 96.3%. Also, once the adversarial vehicle is localized, we derive conditions for controller gains using the Routh Hurwitz criterion to mitigate the attack and ensure the stability of the platoon. Realistic simulator CARLA and MATLAB simulation results validate the effectiveness of our proposed approaches.

Predictive Model-Based and Control-Aware Communication Strategies for Cooperative Adaptive Cruise Control

Utilizing Vehicle-to-everything (V2X) communication technologies, vehicle platooning systems are expected to realize a new paradigm of cooperative driving with higher levels of traffic safety and efficiency. Connected and Autonomous Vehicles (CAVs) need to have proper awareness of the traffic context. The cooperative platoon’s performance will be influenced by the communication strategy. In particular, time-triggered or event-triggered are of interest here. The expenses related to communication will increase significantly as the number of connected entities increases. Periodic communication can be relaxed to more flexible aperiodic or event-triggered implementations while maintaining desired levels of performance. This paper proposes a predictive model-based and control-aware communication solution for vehicle platoons. The method uses a fully distributed Event-Triggered Communication (ETC) strategy combined with Model-Based Communication (MBC) and aims to minimize communication resource usage while preserving desired closed-loop performance characteristics. In our method, each vehicle runs a remote vehicle state estimator based on the most recently communicated model and the event-driven communication scheme only updates the model when the performance metric error exceeds a certain threshold. Our approach achieves a significant reduction in the average communication rate (82%) while only slightly reducing control performance (e.g., less than 1% speed deviation).

Characterization of Transient Communication Outages Into States to Enable Autonomous Fault Tolerance in Vehicle Platooning

The benefits of platooning, e.g., fuel efficiency, road throughput enhancement, driver offload, etc., have sparked an interest in a more connected, intelligent, and sustainable transportation ecosystem. However, efficient platooning is realized through wireless communications, characterized by transient connectivity, which is caused by occasional packet losses. Being a safety-critical system of systems, a platoon must be fail-operational even during transient connectivity. Moreover, a platoon should be capable of transitioning into a fail-safe state upon encountering a hazard. To this end, we propose a strategy for classifying the transient communication outages incurred by platooning vehicles into states. Furthermore, a state machine using these states to enable safe automated platooning is proposed that also defines the transitions between the states based on the nature and levels of transient connectivity and hazards. To achieve this, a graceful degradation and upgradation method is proposed, such that the platoon can remain fail-operational by adjusting, e.g., the automated controller and/or the inter-vehicle gaps based on the current communication quality. An emergency braking strategy is also proposed to enable a fast transition into a fail-safe state, should the platoon encounter a hazard. Rigorous simulation studies show that the proposed strategies enable fault-tolerant automated platooning also during transient connectivity.

Impact on Platooning of Autonomous Vehicles using Macro to Micro simulations

Social costs increase as traffic problems continue. Driverless vehicles are attracting attention as a way to overcome this issue. Many said that fully autonomous driving is not a distant future. As one of the autonomous vehicle operations, vehicle platooning has been paid attention. Therefore, this study analyzes the effects of vehicle platooning on the road network. As a large-scale network, Jeju island, one of the cities in Korea was selected to test the platooning arrangement. A macro simulation was developed and calibrated based on the collected traffic counts, and then the network and assigned paths were imported to the micro simulation, which is capable of forming the platoon. The analysis was conducted by changing various parameters of the platooning vehicle. According to the investigation, it can be seen that as the market share of autonomous vehicles increases, delays decrease, and speed increases. Traffic efficiency was improved through platooning in general. It is expected that the results of this study can be used as primary data to develop an optimal traffic flow management strategy for cluster operation.

Hybrid Cost Function Distributed MPC for Vehicle Platoons With Stability and String Stability Properties

Distributed MPC schemes for control of vehicle platoons typically employ a p-norms based cost function to achieve stability and string stability. Quadratic cost functions yield smoother trajectories, but they are not aligned with string stability conditions. Hence, in this paper, we develop distributed MPC controllers for vehicle platoons based on a hybrid cost function, which combines infinity norms and quadratic forms. Sufficient conditions for global platoon stability and leader-follower string stability are derived for the developed hybrid cost function and applied to lateral dynamics control. Simulation results show that the hybrid cost function yields lateral position errors that are 10 times smaller (for the maximum error) compared with an infinity norms based cost function.