Platoon Of Autonomous Vehicles Research Articles

An improved eco-driving strategy for mixed platoons of autonomous and human-driven vehicles

The emergence of autonomous vehicles (AVs) could transform traffic flow characteristics and affect energy consumption subsequently. The primary objective of this study is to propose an improved eco-driving strategy for the mixed platoons by exploring the impacts of AVs on energy consumption characteristics. Initially, leveraging precise AV control, this study utilizes a Model Predictive Control (MPC) system to optimize AV trajectories, aiming to minimize energy consumption within mixed platoons of AVs and human-driven vehicles (HDVs). Given the differing collaborative capabilities of HDVs and AVs in mixed platoons, exceptional situations may arise at intersections where not all platoon vehicles cross the stop line during the same green light cycle (GLC). To address these challenges, we then propose a splitting approach to optimize the platoon operation mode. This approach allows for flexible splitting decisions based on AVs market penetration and current traffic conditions. The simulation results indicate that the energy consumption reduction ranges from 15.35% to 35.39% and 2.75–3.64% compared to the traditional mode and the eco-driving mode, respectively. In addition, through an analysis of the energy consumption of various vehicle orders within a mixed platoon, we determined that positioning the second AV in the middle platoon maximizes stability, resulting in a more consistent energy consumption pattern. These findings highlight the advantages of the strategy in terms of energy consumption, providing theoretical support management of mixed platoons.

Robust adaptive control for a class of autonomous vehicle platoons

SummaryThis article studies robust adaptive control for a class of autonomous vehicle platoons. In particular, two innovative adaptive control laws are proposed to address both position and velocity tracking for a vehicle platoon. In addition, it is shown that robust asymptotic string stability can be delivered by the underlying adaptive control laws for the vehicle platoon, in the sense that these adaptive control laws are capable of mitigating parameter uncertainties involved in the nonlinear vehicular dynamics and achieving truly cooperative adaptive cruise control while ensuring the required safety spacing between each neighboring vehicle pair asymptotically. It is also shown that the control performance of the vehicle platoon can be further improved if the operating equilibrium points of all vehicles can be adaptively estimated, leading to two linear time‐invariant control laws for individual vehicles under both position and velocity controls and for the vehicle platoon. Simulation studies illustrate the effectiveness of the proposed control method, validating the results obtained for the class of vehicle platoons.

Enhancing platoon performance: A novel approach to speed and direction control using V2X communication

Autonomous vehicle platoons have become an increasingly popular solution to enhance driving system performance and facilitate the safe integration of these vehicles on the roads. To ensure efficient platoon traffic, it is essential to effectively manage both speed and direction within this group of vehicles. It is in this context that this research is situated. Our primary objective is to enhance the performance of speed and direction controllers, as the ability of vehicles to adjust these parameters in a coordinated manner is crucial for the success of platoon traffic. To achieve this, we have developed a novel speed control approach based on neural networks and fuzzy logic, utilizing V2X communication. By incorporating environmental parameters through vehicle-to-vehicle communication and considering the specific goals of the platoon, our neuro-fuzzy system can accurately calculate optimal speeds and directions for the vehicles. Our experiments have demonstrated the effectiveness of this approach compared to traditional and advanced methods, improving both energy efficiency and temporal coordination of autonomous vehicles within the platoon.

Autonomous Vehicle Platoons in Urban Road Networks: A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

Autonomous Vehicle Platoons in Urban Road Networks: A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

Multi-Agent Reinforcement Learning for Highway Platooning

The advent of autonomous vehicles has opened new horizons for transportation efficiency and safety. Platooning, a strategy where vehicles travel closely together in a synchronized manner, holds promise for reducing traffic congestion, lowering fuel consumption, and enhancing overall road safety. This article explores the application of Multi-Agent Reinforcement Learning (MARL) combined with Proximal Policy Optimization (PPO) to optimize autonomous vehicle platooning. We delve into the world of MARL, which empowers vehicles to communicate and collaborate, enabling real-time decision making in complex traffic scenarios. PPO, a cutting-edge reinforcement learning algorithm, ensures stable and efficient training for platooning agents. The synergy between MARL and PPO enables the development of intelligent platooning strategies that adapt dynamically to changing traffic conditions, minimize inter-vehicle gaps, and maximize road capacity. In addition to these insights, this article introduces a cooperative approach to Multi-Agent Reinforcement Learning (MARL), leveraging Proximal Policy Optimization (PPO) to further optimize autonomous vehicle platooning. This cooperative framework enhances the adaptability and efficiency of platooning strategies, marking a significant advancement in the pursuit of intelligent and responsive autonomous vehicle systems.

Adaptive Truck Platooning with Drones: A Decentralized Approach for Highway Monitoring

The increasing demand for efficient and safe transportation systems has led to the development of autonomous vehicles and vehicle platooning. Truck platooning, in particular, offers numerous benefits, such as reduced fuel consumption, enhanced traffic flow, and increased safety. In this paper, we present a drone-based decentralized framework for truck platooning in highway monitoring scenarios. Our approach employs multiple drones, which communicate with the trucks and make real-time decisions on whether to form a platoon or not, leveraging Model Predictive Control (MPC) and Unscented Kalman Filter (UKF) for drone formation control. The proposed framework integrates a simple truck model in the existing drone-based simulation, addressing the truck dynamics and constraints for practical applicability. Simulation results demonstrate the effectiveness of our approach in maintaining the desired platoon formations while ensuring collision avoidance and adhering to the vehicle constraints. This innovative drone-based truck platooning system has the potential to significantly improve highway monitoring efficiency, traffic management, and safety. Our drone-based truck platooning system is primarily designed for implementation in highway monitoring and management scenarios, where its enhanced communication and real-time decision-making capabilities can significantly contribute to traffic efficiency and safety. Future work may focus on field trials to validate the system in real-world conditions and further refine the algorithms based on practical feedback and evolving vehicular technologies.

The Impact of Connected and Autonomous Vehicle Platoon’s Length on Expressway Traffic Flow Characteristics Based on Symmetry Lane Changing Rules

This study mainly investigates the maximum length of CAV (Connected and Autonomous Vehicle) platoons in a heterogeneous traffic flow environment. By employing MATLAB to simulate the heterogeneous traffic flow on expressways, this study focuses on the maximum platoon length of CAV platoons and explores their impact on the traffic flow characteristics on expressways. Firstly, based on four different car-following modes of heterogeneous traffic flow, F-STCA (Flexible–Symmetric Two-Lane Cellular Automata Model) and the symmetric lane-changing strategy, the study refines and improves the construction of the NaSch (Nagel and Schreckenberg) model introduced into the Gipps safety distance formula. The whole improvement process is based on the acceleration decay characteristics of vehicles on expressway. Secondly, the congestion situations under different maximum platoon lengths are compared using fundamental heat maps of the simulation data. The evolution of the fundamental diagram with changes in maximum platoon length is studied to investigate the impact and magnitude of maximum platoon lengths on the road capacity under different CAV permeabilities. Finally, the study explores the stability and safety of heterogeneous traffic flow involving CAV platoons using SD (Standard Deviation) and TERCRI (Time-Exposed Rear-End Collision Risk Index). The results show that when the CAV’s permeability does not reach a high level on heavily trafficked sections of the expressway, considering the overall average speed, the maximum platoon length should not be set too long and should be around five vehicles. This restriction does not apply when the CAV permeability approaches 100%.

Retracted: GAP-MM: 5G-Enabled Real-Time Autonomous Vehicle Platoon Membership Management Based on Blockchain

Retracted: GAP-MM: 5G-Enabled Real-Time Autonomous Vehicle Platoon Membership Management Based on Blockchain

Mixed traffic system with multiple vehicle types and autonomous vehicle platoon: Modeling, stability analysis and control strategy

This paper focuses on platoon control of autonomous vehicles (AVs) equipped with V2X technology and the phenomenon that the large platoons are split into multiple sub-platoons due to the communication restrictions. At the same time, with the popularity of AVs and connected human-driven vehicles (CVs), this paper also carries out the research of mixed traffic flow mixed with AV platoons, CVs and regular human-driving vehicles (RVs). According to the characteristics of the different types of vehicles, this paper extends the corresponding car-following models based on intelligent driver model (IDM), and considers the functional degradation of AVs under different car-following modes. In this paper, the AVs are classified into the second-level degenerated AV (D2AV), the first-level degenerated AV (D1AV) and the normal functioning AV (NAV) based on the differences in car-following modes. The degradation of AVs is represented via the change of communication topologies and models. Linear stability analysis is conducted. Numerical simulation shows that the higher level of AV degradation, the worse the stability of traffic flow. Under the same conditions, the more sub-platoons and the larger maximum size of sub-platoons, the better the stability of the traffic flow. The increase in communication strength of AVs is also beneficial to stability of traffic flow. In order to compensate for degradation, an adaptive gain feedback control strategy is proposed. Simulation shows that the adaptive gain feedback strategy can effectively improve the ability of the traffic flow to resist disturbances, reduce the fluctuations of velocity and acceleration, and contribute to the stability of platoon system and traffic flow.

Comparative Analysis on the Effect of Triangular and Gaussian Membership Functions on Fuzzy Controlled Vehicle Platoon

Fuzzy controlled vehicle platoon system provides a simplified yet robust approach to achieving platoon string stability and uniform inter-vehicular gap keeping in autonomous vehicle platoon. Most truck platoon is affected by delay of platoons which causes trucks not to maintain a constant inter-vehicular gap and speed, unknown uncertainties which may result in crash or accident. However, the fuzzification and defuzzification method adopted affects the final platoon 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 paper proposes to compare the effect of fuzzification and defuzzification method on vehicle platoon, to provide evidence on the selection criteria and how it affects the controlled system. Simulation was done in MATLAB environment, and the fuzzy control approach was applied to a 3-vehicle autonomous platoon, which was a combination of triangular/centroid, triangular/bisector, triangular/mean with Gaussian/centroid, Gaussian/bisector and Gaussian/mean of maxima under the same platoon scenario. It was found that the best performing combination is the 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 which is the worst performing combination.

A Scalable Compositional Falsification Approach for Identifying Challenging Scenarios in Cyber-Physical Systems

This article presents a scalable approach for identifying system inputs and trajectories that lead to undesirable scenarios in a cyber-physical system (CPS). The proposed approach falls under the broad class of falsification methods. The objective in falsification is to find initial conditions and input signals resulting in trajectories that violate a system property expressed as a formal specification. The existing falsification methods are not suitable for handling systems where the input at a specific time depends on the state history of the system. Autonomous vehicle platoons and certain multi-agent systems that are required to maintain connectivity are some examples of such systems. In the first part of this article, we employ a graph-search-based motion planning algorithm to develop a falsification method capable of identifying falsifying inputs for the aforementioned class of systems. We demonstrate the effectiveness of the approach through a case study involving a vehicle platoon consisting of three autonomous vehicles, wherein one of the vehicles is tasked to execute a lane-change maneuver. In the second part, we extend the proposed approach to analyze a network of three unmanned underwater vehicles, wherein our objective is to identify system inputs resulting in trajectories that lead to a deadlock. The approach leverages the component-based structure inherent in the considered multi-agent system and employs a surrogate model in place of computationally demanding components present in the system. These modifications are shown to improve the scalability of the approach and reduce the computational time in finding falsifying trajectories.

Cooperative platoon forming strategy for connected autonomous vehicles in mixed traffic flow

Connected Autonomous Vehicle (CAV) platoon was recently proposed to improve road utilization and reduce fuel consumption. However, the mixed traffic flow of CAVs and human-driven vehicles (HDVs) will exist on the road for a long time; thus, the organization of CAVs needs to be reconsidered from the aspects of the platoon forming strategy. In this paper, a novel cooperative platoon forming (CPF) strategy is proposed to enhance the formation of CAV platoons in mixed traffic flows. A four-lane cellular automaton traffic modeling framework is constructed to simulate the interaction between CAVs and HDVs. Numerical results show that the CPF strategy is effective in enabling CAVs to be assembled into platoons. The increase in the CAV penetration rate can effectively alleviate traffic congestion and improve traffic capacity, thereby improving traffic flow and fuel economy. The findings may help improve the management of vehicle platoons.

Secure Data Sharing With Flexible Cross-Domain Authorization in Autonomous Vehicle Systems

As an increasingly prevalent technology in intelligent autonomous transportation systems, autonomous vehicle platoon has been indicated the ability to significantly reduce fuel consumption as well as heighten highway safety and throughput. However, existing efforts rarely focus on protecting data confidentiality and authenticity in autonomous vehicle platoons. How to ensure secure and high-fidelity platoon-level communication is still in its infancy. This paper makes the first attempt for efficient and secure communication across autonomous vehicle platoons. Specifically, we present PDSM-FC, the first privacy-preserving data share mechanism with flexible cross-domain authorization over distinctive platoons. The key insight of PDSM-FC is the design of a new ciphertext conversion technique, which allows a ciphertext to be easily converted into another type of ciphertext, facilitating efficient access by all entities holding the legitimate authorization. As a result, PDSM-FC can achieve high-fidelity data communication between two unique platoons in ciphertext, so as to complete specific tasks including platoon integration. Rigorous security analysis shows that PDSM-FC is secure against various attacks such as collusion, forgery and chosen-plaintext attacks. Moreover, theoretical evaluation and extensive experiments demonstrate the practicability of PDSM-FC in terms of functionality, storage and computation overheads.

On Connected Autonomous Vehicles With Unknown Human Driven Vehicles Effects Using Transmissibility Operators

This study proposes an algorithm for fault detection and mitigation of mixed autonomous and human-driven vehicle platoons based on transmissibility identification. This work is motivated by the fact that on-road human-drivers&#x2019; behaviour is unknown and difficult to be predicted. Transmissibility operators are mathematical operators that relate one subset of outputs to another in the same system. The transmissibility superiority is represented in the in-dependency on the system excitation signals. We reformulate the system dynamics to render the system inputs, external disturbances, as well as the human-drivers&#x2019; behaviour along with any other nonlinearities as independent excitation signals on the system. Therefore, the transmissibility operators become independent of the human-drivers&#x2019; behaviour and robust against external disturbances. Transmissibilities are then applied to detect and localize physical and cyber faults within the platoon. Then these faults are mitigated using a transmissibility-based sliding mode controller. The controller stability and the string stability are investigated while the controller is active and the faults are mitigated. We validate the proposed algorithm on a model of the platoon obtained using the bond graph approach. Moreover, we apply the proposed algorithm experimentally to a platoon consisting of three robots (i.e. two autonomous robots and a human-driven robot), that is connected using wireless communications. <i>Note to Practitioners</i>&#x2014;The existence of connected autonomous vehicles depends greatly on the smooth transition between the current on-road human-driven vehicles to autonomous vehicles. The typical methods of securing dynamic systems depend on estimating the system behaviour and responses. Increasing the number of autonomous vehicles on roads necessitates the typical securing techniques to estimate the human-drivers&#x2019; behaviour. Thus, securing the connected autonomous vehicles during this transition is challenging since the on-road human-driver behavior is unknown and difficult to be estimated. Moreover, connected autonomous vehicles should adapt to their environment while maintaining their role within the autonomous platoon. This adaptation includes adapting to the unknown human-driver behaviour. This inspired the authors to develop the proposed transmissibility-based fault mitigation. The proposed technique is shown to be able to handle unknown human-driver behaviors, different driving conditions such as road irregularities and different weather conditions, and different physical and cyber faults (i.e., in the vehicles or in the communication links). The platoon stability is then investigated while the faults are mitigated, and shown to guarantee the platoon stability.

Eco-CSAS: A Safe and Eco-Friendly Speed Advisory System for Autonomous Vehicle Platoon Using Consortium Blockchain

Future worldwide 6G research will drive the evolution of emerging intelligent control technologies, such as intelligent speed advisory systems (ISA), to a more advanced generation. As a special type of ISA, consensus-based speed advisory systems (CSAS) can be widely used to recommend a consensus speed for a vehicle platoon, enabling minimizing energy consumption or emissions over a planned route. Recently, speed recommendation services that protect data privacy (i.e., how to obtain an optimal speed in a privacy-preserving way) have drawn tremendous attention. However, current approaches could still encounter service trust issues with central servers and the malicious behavior of vehicles. Furthermore, existing research lacks considering road safety constraints (i.e., safe distance between adjacent vehicles and road speed limits) that are essential for the practical deployment of CSAS. To address the above issues, this paper proposes Eco-CSAS, a safe and eco-friendly consensus speed advisory system using blockchain. We formulate an optimization problem subject to the minimum following distance and maximum road speed limit to minimize the energy consumption of the automatic vehicle platoon. In addition, we introduce a consortium blockchain and cryptographic primitives to ensure service trust and data privacy. We implement the system on the Hyperledger platform, and experimental results show that the system can achieve speed recommendations in a trustworthy and privacy-preserving manner while ensuring a secure platoon.

Transmissibility-Based Fault Detection for Robotic Applications With Time-Varying Parameters

This article investigates transmissibility operators for time-variant systems with bounded nonlinearities. Transmissibility operators are mathematical objects that characterize the relationship between two subsets of responses of an underlying system. The underlying system parameters and nonlinearities are assumed to be unknown. Time-domain transmissibility operators are independent of the system inputs and initial conditions. In this article, we propose an algorithm to identify time-varying transmissibilities using recursive least-squares and noncausal finite impulse response models. The identified transmissibilities are then used for fault detection in three different systems. The first system is an autonomous multirobotic system formulated to emulate connected autonomous vehicle platoons, where the varying parameters are the robot mass and the ground friction coefficient. The second system is a flexible structure with unknown excitation, where the variant parameter is the location of the excitation. Finally, the third system is a robotic manipulator that picks objects with different mass values.

MONITORING THE ENVIRONMENT IN SMART CITIES: THE IMPORTANCE OF GEOSPATIAL LOCATION REFERENCING

Abstract. The Smart City concept is taking momentum recently as big metropolises as well as mid-size cities are intensifying their efforts to improve the life of people living in dense urban environment. Local governments are eager to have up-to-date information of every aspect of city life, including environmental data, such as air and water quality parameters; mobility data, such as traffic flow, including vehicles, transit passengers; crowd control, such as public events, mobility in hospitals; life quality data, such as social status, education level, health records; etc. Monitoring all these very different data streams in space and time is a formidable challenge. While on the data acquisition side, tremendous progress has been achieved, as sensors have been deployed in increasingly large numbers on both mobile and static platforms, there is a lack of creating accurate geotags, as the quality of georeferencing varies over a large scale. It is important to note that the data acquisition is becoming largely customer-based, as smart devices are efficient sensor systems and with advancing communication technologies, crowdsourcing is quickly becoming the dominant data source on mobile platforms. In this paper, we investigate the potential to exploit the ranging capabilities of imaging and communication sensors and use the strength of the spatial network formed by the sensors to improve the georeferencing of a group of platforms operating in a close environment, such as UAS swarm or a platoon of autonomous vehicles. Transportation in cities and in general mobility are of great interest to Smart Cities, they represent one of the most significant components of the activities, so having an optimized transportation system is essential to reduce carbon footprint, decrease commute time, and just improve the quality of life. To assess the performance of collaborative navigation based accurate georeferencing, data was acquired at a simulated intersection area at The Ohio State University, where multiple vehicles, pedestrians and cyclists were moving around. In addition, drones were flying above the area. Here we report about our initial results.

Hierarchical cooperative eco‐driving control for connected autonomous vehicle platoon at signalized intersections

AbstractVehicles in the platoon can sufficiently incorporate the information via V2X communication to plan ecological speed trajectories and pass the intersection smoothly. Most existing eco‐driving studies mainly focus on the optimal control of a single vehicle at an individual signalized intersection, while rarely involving the cooperative optimization of a group of vehicles at successive signalized intersections. In this study, a hierarchical cooperative eco‐driving control for a connected autonomous vehicle (CAV) platoon is proposed to enhance traffic mobility and energy efficiency, wherein the velocity trajectory of the leading vehicle at each isolated signalized intersection is planned using the pseudo‐spectral method, and then the cooperative optimization of following vehicles in the platoon is conducted via rolling optimization, with the aim of improving driving comfort, safety and energy economy for the platoon. The simulation results highlight that the proposed hierarchical cooperative eco‐driving strategy can lead to preferable vehicle‐following behaviours and platoon driving performance, and the overall energy consumption and trip time of vehicle platoon are respectively reduced by 26.10% and 2.83%, compared with that under manual driving. Furthermore, the overall energy economy is promoted by 4.95% and 4.60%, compared with cooperative adaptive cruise control and intelligent driver model‐based platoon control strategies.

Cooperative Adaptive Cruise Control for Connected Autonomous Vehicles Using Spring Damping Energy Model

Cooperative adaptive cruise control (CACC) has been widely considered as a potential solution for reducing traffic congestion, increasing road capacity, reducing fossil fuel consumption and improving traffic safety. Traditional CCAC methods rely heavily on the vehicle-to-vehicle communications to achieve cooperation. However, in the real-world scenarios, unreliable communication will degrade CACC to adaptive cruise control, which may bring negative influences on safety (i.e., increase the risk of collisions). To overcome this drawback, this paper innovatively applies a spring damping energy model to construct a robust autonomous vehicle platoon system. The proposed design of the energy model ensures that the stability and safety of the platoon system be maintained in the event of such sudden degradation. Based on this technique, a distributed control protocol which only utilizes local information from neighbors is then proposed. Furthermore, some practical constraints such as the connectivity of the vehicle platoon system and the bound of the control inputs are guaranteed. Finally, the effectiveness of the proposed CCAC strategy is validated by multiple simulation experiments in Unreal Engine.

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.