Platoon Of Autonomous Vehicles Research Articles

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.

Lightweight and Bilateral Controllable Data Sharing for Secure Autonomous Vehicles Platooning Service

Autonomous vehicle platoons are emerging as a promising solution to modern urban governance challenges, such as traffic congestion, air pollution, and fuel waste. As a data-driven paradigm, ensuring secure data sharing is a key challenge for autonomous vehicle platoon applications. However, existing research on secure data sharing of autonomous vehicles platoon suffers from practical weaknesses such as privacy leakage, lack of flexible data source identification, and inefficiency. Motivated by that, this paper first proposes the LFBA scheme, which is the first fine-grained data sharing framework that supports bilateral access control and access policy hiding with lightweight and almost constant computational and storage resources consumption, this can be owed to the sophisticated algorithm structure we designed. We then construct an edge-assisted deployment model over LFBA (EA-LFBA) to further improve efficiency and security against common attacks. We then provide the security proof for LFBA, and security analysis for EA-LFBA. Simulation tests and comparative experiments demonstrate the practicality and superiority of our solution compared to state-of-the-art representative works.

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.

A distributed reinforcement learning and model predictive control architecture for routing decisions in urban road networks

In this paper, platoons of autonomous vehicles in urban road networks are considered. From a methodological point of view, the problem consists in characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data and a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle.

Surrogate Safety Measures: Review and Assessment in Real-World Mixed Traditional and Autonomous Vehicle Platoons

Surrogate safety measures (SSMs) are critical tools for evaluating the safety performance of mixed traffic. Crashes are rare events, and historical crash data is scarce for mixed traffic that includes autonomous and/ or connected vehicles. Recent safety review papers focus on traditional human-driven vehicles (TVs) and do not encompass advanced technology vehicles such as Autonomous Vehicles (AVs), Connected Vehicles (CVs), and Connected-Autonomous Vehicles (CAVs). This study examines the development, implementation, and shortcomings of SSMs, and SSM-based models used for mixed traffic safety evaluation. It reviews the current relevant literature and applies a case study analysis using a real-world mixed traffic dataset. The study summarizes the fundamental SSM guiding concepts, as well as their most significant metrics including threshold values employed in the past for SSMs and SSM-based models. Primary benefits and limitations of examined SSMs and SSM-based models are also underlined. This review reveals significant gaps in the literature that might guide future research paths in SSM-based mixed traffic safety assessment. Critical gaps include the absence of robust SSM threshold selection criteria, the suitability of current SSMs in mixed traffic research, microsimulation modeling that lacks proper calibration and validation, and the absence of a framework for selecting or combining multiple SSMs.

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

TD-SAS: A Trust-Aware and Decentralized Speed Advisory System for Energy-Efficient Autonomous Vehicle Platoons

TD-SAS: A Trust-Aware and Decentralized Speed Advisory System for Energy-Efficient Autonomous Vehicle Platoons

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.

Exploring the impact of damping on Connected and Autonomous Vehicle platoon safety with CACC

Connected and Autonomous Vehicle (CAV) platooning has gained attention for its potential for vastly enhancing capacity and safety of future transport systems. Previous research on CAV platoon control approximates stability as safety during CAV operations and primarily addressed instability and stability properties. Nevertheless, it is vital to note that stability does not stand for absolutely safe, since the behaviors of CAV platoons could also be impacted by the oscillation characteristics of damping that are essential for CAV platoons to boost reliability against variant continuous disturbances. To explore the impact of damping on CAV platoon safety, the time-domain analysis approach is introduced to reveal the relationship between damping characteristics and CAV platoon safety performance with Cooperative Adaptive Cruise Control (CACC) based on the constant time headway policy under stable conditions. Numerical simulations are carried out to verify the impacts of the damping on CAV platoon performance and evaluate the safety of a platoon possessing stability under different damping properties. Further, different control parameters based on damping and stability properties are applied to CAV platoons and their safety is evaluated by calculating the Deceleration Rate to Avoid a Crash (DRAC), Time Exposed Time-to-Collision (TET) and Time-Integrated Time-to-Collision (TIT) as surrogate safety measures of the rear-end collision risk. Numerical analysis results show that it could better maintain platoon safety, when control parameters make the damping ratio between 1 and 1.1.

Longitudinal traffic conflict analysis of autonomous and traditional vehicle platoons in field tests via surrogate safety measures

Autonomous vehicles (AVs) have been introduced into the traffic stream alongside traditional vehicles (TVs) with the expectation of improved transportation safety, efficiency, and reliability. The majority of AV safety research has been done through simulation. The results of such research on the safety performances of AVs are heavily influenced by the methodological framework, algorithms, and assumptions about AV driving characteristics in a simulated environment. There is a need for AV safety research based on real-world settings before any wide-scale deployment of this technology.This paper investigates the impact of the presence of SAE level 2 AVs in the traffic stream in reducing longitudinal traffic conflicts using Surrogate Safety Measures on a real-world open-source database of mixed traffic trajectories. The analysis is conducted for both AV-exclusive and mixed AV-TV platoons. Furthermore, we explore whether the presence of AVs decreases longitudinal traffic conflicts in two-vehicle platoons comprising AV and TV mixed leaders and followers.We find that an exclusive AV platoon behaves similarly to an exclusive TV platoon and produces similar longitudinal conflicts. However, mixed platoons with both AVs and TVs result in a higher number of longitudinal conflicts. Maintaining near-identical leader–follower conditions, we find that the number of conflicts in mixed platoons when an AV follows a TV is higher than when a TV follows an AV. The increase in conflict numbers in a TV-AV mixed platoon can be attributed to AV’s longer response time lag. In summary, analyses conducted in this paper indicate that exclusive platoons and pairs of vehicles exhibit fewer longitudinal conflicts than mixed platoons and pairs.

RFAP: A Revocable Fine-Grained Access Control Mechanism for Autonomous Vehicle Platoon

Autonomous Vehicle Platoon (AVP) is conceived as a promising solution to enhance the traffic capacity and reduce the energy consumption in the intelligent transportation system. Nevertheless, AVP without security guarantees are prone to various attacks, which probably lead to life-threatening accidents. Motivated by solving this issue, an outsourced attribute-based access control mechanism with direct revocation for AVP (RFAP) is introduced in this paper. Among them, attribute-based encryption is utilized to implement fine-grained access control during the encryption process. Furthermore, RFAP can not only realize the immediate revocation of platoon member who is about to leave the platoon without affecting others, but also achieve secure outsourced decryption with the help of edge computing units for minimizing the computational overhead of decryption on the vehicle side. Security analysis and simulation results indicate that our RFAP mechanism is practical in aspects of security and efficiency.

String Stability of Connected Vehicle Platoons Under Lossy V2V Communication

Recent advances in vehicle connectivity have allowed formation of autonomous vehicle platoons for improved mobility and traffic throughput. In order to avoid a pile-up in such platoons, it is important to ensure platoon (string) stability, which is the focus of this work. As per conventional definition of string stability, the power (2-norm) of the spacing error signals should not amplify downstream in a platoon. But in practice, it is the infinity-norm of the spacing error signal that dictates whether a collision occurs. We address this discrepancy in the first part of our work, where we reconsider string stability from a safety perspective and develop an upper limit on the maximum spacing error in a homogeneous platoon as a function of the acceleration maneuver of the lead vehicle. In the second part of this paper, we extend our previous results by providing the minimum achievable time headway for platoons with two-predecessor lookup schemes experiencing burst-noise packet losses. Finally, we utilize throttle and brake maps to develop a longitudinal vehicle model and validate it against a Lincoln MKZ which is then used for numerical corroboration of the proposed time headway selection algorithms.

A longitudinal inter-vehicle distance controller application for autonomous vehicle platoons.

Autonomous vehicle platoons are a promising solution to road safety, efficient road utilization, emission reduction, among other problems facing today’s transportation industry. However, consistently maintaining the desired inter-vehicle distance is one of the major problems facing autonomous vehicle platoons. In this study, we propose a proportional–integral–derivative (PID)-based cost-efficient algorithm to control the longitudinal inter-vehicle distance between successive members of an autonomous vehicle platoon. In our approach, calculations of the control algorithm are decentralized, and the data used in the control algorithm is obtained using one sensor per platoon member making the algorithm cost-efficient both computationally and financially. The proposed algorithm was implemented using the Robot Operating System (ROS) and applied to 3D vehicle models in simulations designed to mimic the natural environment in order to demonstrate and evaluate the suitability of the proposed algorithm for demanding and applicable scenarios. We performed meticulous simulations using the ROS framework in conjunction with the gazebo platform. In the proposed approach, the desired inter-vehicle distance between platoon members was successfully kept with a maximum absolute error of 5 m under any given scenario at any given time while maintaining platoon formation and ensuring that no collisions occur among platoon members.

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

Autonomous Vehicle Platoon (AVP) is the most anticipated application of 5G ultrareliable and low latency communications. By joining the preexisting platoon to form the real-time AVP (RAVP), individual vehicles could gain benefits such as fuel consumption reduction and traffic safety enhancement. Unlike the scheduled AVP which only contains fixed vehicles, members in RAVP change frequently since individual vehicles would want to join or leave the platoon any time. Besides, malicious vehicles may attempt to sneak into the RAVP and try to manipulate the platoon. Public key cryptography and access control mechanisms can be adopted to create a relatively isolated area for communication inside the platoon. Nevertheless, there exist few works that take into account the regulation of the dynamic change members in RAVP. In this paper, we propose a membership management scheme for 5G-enabled RAVP by integrating revocable attribute-based encryption (RABE) and blockchain, namely, GAP-MM. It realizes fine-grained access control of key distribution and malicious vehicle’s key revocation efficiently. The sufficient evaluations and security analysis indicate that GAP-MM is practical for RAVP scenario in terms of both efficiency and security.

A Reputation-Based Leader Election Scheme for Opportunistic Autonomous Vehicle Platoon

As an emerging concept in the intelligent transportation system (ITS), Opportunistic Autonomous Vehicle Platoon (OAVP) enables autonomous vehicles to self-organize a temporary platoon and travel together. Whereas, electing the platoon leader is a crucial issue to be solved in this scenario. The platoon leader not only suffers from more wind resistance but also spends more computation resources in dealing with tremendous information obtained from the surrounding environment. Hence, vehicles tend to be a follower rather than a leader. In this paper, we propose a reputation-based leader election system for OAVP named RLE, which contains two sub-systems: leader election and incentive mechanism. In the former one, a reputation-based election scheme is first constructed to elect a relatively trustful leader according to the reputation value recorded on the blockchain. The proposed scheme integrates previous experience with recommendations from other members. In the second sub-system, an incentive mechanism is designed to stimulate platoon members to participate actively in the process of election, which is based on the recorded real-time fuel economy among vehicles participating in a platoon. Security analysis shows that the system is sufficient to deal with potential security threats. Experimental results based on the simulated platform demonstrate the practicality and feasibility of our solution.