Cooperative Autonomous Vehicles Research Articles

Dynamic, fair, and efficient routing for cooperative autonomous vehicle fleets

This paper addresses challenges in agricultural cooperative autonomous fleet routing through the proposition, modeling, and resolution of the Dynamic Vehicle Routing Problem with Fair Profits and Time Windows (DVRP-FPTW). The aim is to dynamically optimize routes for a vehicle fleet serving tasks within assigned time windows, emphasizing fair and efficient solutions. Our DVRP-FPTW accommodates unforeseen events like task modifications or vehicle breakdowns, ensuring adherence to task demand, vehicle capacities, and autonomies. The proposed model incorporates mandatory and optional tasks, including optional ones in operational vehicle routes if not compromising the vehicles’ profits. Including asynchronous and distributed column generation heuristics, the proposed Multi-Agent-based architecture DIMASA for the DVRP-FPTW dynamically adapts to unforeseen events. Systematic Egalitarian social welfare optimization is used to iteratively maximize the profit of the least profitable vehicle, prioritizing fairness across the fleet in light of unforeseen events. This improves upon existing dynamic and multi-period VRP models that rely on prior knowledge of demand changes. Our approach allows vehicle agents to maintain privacy while sharing minimal local data with a fleet coordinator agent. We propose publicly available benchmark instances for both static and dynamic VRP-FPTW. Simulation results demonstrate the effectiveness of our DVRP-FPTW model and our multi-agent system solution approach in coordinating large, dynamically evolving cooperative autonomous fleets fairly and efficiently in close to real-time.

Sustainable Smart Cities through Multi-Agent Reinforcement Learning-Based Cooperative Autonomous Vehicles

As urban centers evolve into smart cities, sustainable mobility emerges as a cornerstone for ensuring environmental integrity and enhancing quality of life. Autonomous vehicles (AVs) play a pivotal role in this transformation, with the potential to significantly improve efficiency and safety, and reduce environmental impacts. This study introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm tailored for multi-AV lane-changing in mixed-traffic scenarios, a critical component of intelligent transportation systems in smart cities. By incorporating a local reward system that values efficiency, safety, and passenger comfort, and a parameter-sharing scheme that encourages inter-agent collaboration, our MA2C algorithm presents a comprehensive approach to urban traffic management. The MA2C algorithm leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing both environmental sustainability and urban living standards. The actor–critic architecture is refined to minimize variances in urban traffic conditions, enhancing predictability and safety. The study extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the model of Minimizing Overall Braking Induced by Lane changes (MOBIL), contributing to more accurate and effective traffic management strategies. Empirical results indicate that the MA2C algorithm outperforms existing state-of-the-art models in managing lane changes, passenger comfort, and inter-vehicle cooperation, essential for the dynamic environment of smart cities. The success of the MA2C algorithm in facilitating seamless interaction between AVs and HDVs holds promise for more fluid urban traffic conditions, reduced congestion, and lower emissions. This research contributes to the growing body of knowledge on autonomous driving within the framework of sustainable smart cities, focusing on the integration of AVs into the urban fabric. It underscores the potential of machine learning and artificial intelligence in developing transportation systems that are not only efficient and safe but also sustainable, supporting the broader goals of creating resilient, adaptive, and environmentally friendly urban spaces.

5G Networks Enabling Cooperative Autonomous Vehicle Localization: A Survey

5G Networks Enabling Cooperative Autonomous Vehicle Localization: A Survey

Phased Feature Extraction Network for Vehicle Search Tasks Based on Cross-Camera for Vehicle-Road Collaborative Perception.

The objective of vehicle search is to locate and identify vehicles in uncropped, real-world images, which is the combination of two tasks: vehicle detection and re-identification (Re-ID). As an emerging research topic, vehicle search plays a significant role in the perception of cooperative autonomous vehicles and road driving in the distant future and has become a trend in the future development of intelligent driving. However, there is no suitable dataset for this study. The Tsinghua University DAIR-V2X dataset is utilized to create the first cross-camera vehicle search dataset, DAIR-V2XSearch, which combines the cameras at both ends of the vehicle and the road in real-world scenes. The primary purpose of the current search network is to address the pedestrian issue. Due to varying task scenarios, it is necessary to re-establish the network in order to resolve the problem of vast differences in different perspectives caused by vehicle searches. A phased feature extraction network (PFE-Net) is proposed as a solution to the cross-camera vehicle search problem. Initially, the anchor-free YOLOX framework is selected as the backbone network, which not only improves the network's performance but also eliminates the fuzzy situation in which multiple anchor boxes correspond to a single vehicle ID in the Re-ID branch. Second, for the vehicle Re-ID branch, a camera grouping module is proposed to effectively address issues such as sudden changes in perspective and disparities in shooting under different cameras. Finally, a cross-level feature fusion module is designed to enhance the model's ability to extract subtle vehicle features and the Re-ID's precision. Experiments demonstrate that our proposed PFE-Net achieves the highest precision in the DAIR-V2XSearch dataset.

A camera-based vehicular visible light communication system using modulated taillights in public road scenarios

This paper proposes a camera-based vehicular visible light communication system using modulated LED taillights for vehicle-to-vehicle communication. The taillights are modulated using on-off keying with a carrier frequency above the critical flicker frequency threshold to appear steady to the human eye and only a camera with a short exposure time can perceive the signal. For receiving the signal, a common camera with a recording frame rate of 30 frames per second was used for the experiments. This limits the throughput of the communication system as only one bit per frame and per individually modulated light source can be transmitted, thus the system is not suitable for transmitting time-critical data. However, the main advantage is that the transmitted data as well as the transmitting vehicle are visible in the camera image, hence we can associate the message and its origin.For the communication between two cooperative autonomous vehicles, e.g., members of a platoon, a secure wireless channel with low latency is needed. To establish an encrypted connection in this main channel our system can be used as an out-of-band channel to verify the identity of the vehicle in front by transmitting a verification code.In this paper we evaluate how the communication quality in terms of bit error rate of the raw data transmission without any error correction methods is influenced by various environmental factors, e.g., communication distance, traveling speed, weather, brightness, etc. This is done by testing our system in controlled scenarios as well as on public roads. The results show that our system is capable of transmitting data with a raw bit error rate of 1.59% on average in platooning relevant situations on the highway in dry conditions. Rain affects the system due to raindrops on the windshield, spray thrown up by vehicles in front, and the windshield wipers occluding the modulated taillights of the transmitting car. In light to medium rain conditions the mean bit error rate is between 2.7% and 6.2%, depending on the intensity.

Off-Policy Learning-Based Following Control of Cooperative Autonomous Vehicles Under Distributed Attacks

This paper investigates the resilient distributed secure output path following control problem of heterogeneous autonomous ground vehicles (AGVs) subject to cyber attacks based on reinforcement learning algorithm. Most existing results are subject to the same attack models for all communication channels, however multiple channels launched by different attackers are considered in this paper. First, a predictor-acknowledgement clock algorithm for each vehicle is proposed to judge whether the communication channel among neighboring vehicles is attacked or not by receiving or transmitting an acknowledgement. Then, a resilient distributed predictor is proposed to predict the pinning vehicle’s state for each vehicle. In addition, a resilient local control protocol consisting of the feedforward state provided by the predictor and the local feedback state of each vehicle is developed for the output path following problem, which is further converted to the optimal control problem by designing a discounted performance function. Discounted algebraic Riccati equations (AREs) are derived to address the optimal control problem. An off-policy reinforcement learning (RL) algorithm is put forward to learn the solution of discounted AREs online without any prior knowledge of vehicles’ dynamics. It is shown that the RL-based output path following control problem of AGVs imposed by cyber attacks can be achieved in an optimal manner. Finally, a numerical example is provided to verify the effectiveness of theoretical analysis.

Assessing the impacts of shared autonomous mobility with M3ICA: A multi-impact, multi-actor, multi-criteria approach

The impacts of connected cooperative autonomous vehicles on society can be significant. We propose a holistic framework that brings together stakeholder assessment with quantitative evaluation of impacts. In this paper, we apply the first part of the framework, and present our findings from a stakeholder workshop within the context of the H2020 SHOW project. 6 groups of stakeholders in the Linköping pilot site participated in a MAMCA workshop and evaluate scenarios of autonomous mobility. Performance scores of each scenario were calculated using the scores and weights assigned to each criteria by their level of importance for each actor group. The findings show a general positive perception of autonomous mobility, with public actors preferring autonomous shared modes, while users and mobility service providers found modes that supported flexible transport to achieve their objectives better.

Automatic Decentralized Behavior Tree Synthesis and Execution for Coordination of Intelligent Vehicles

This paper proposes a systematic approach for automatic tasking and coordination of a heterogeneous team of cooperative autonomous vehicles forming an intelligent vehicle. Each vehicle is equipped with different resources, operating in a shared dynamic environment, and capable of executing a set of specific tasks. To coordinate such a heterogeneous team of vehicles, we develop a hierarchical modular coordination algorithm for generating local Behavior Trees (BTs) for tasking individual vehicles so that they, as a team, can collectively achieve a global mission. A hierarchical auctioning algorithm is embedded in the proposed framework to effectively assign tasks among the vehicles, so that they can complement each other and complete a mission that may not be achieved individually. Furthermore, by introducing operational and duration cost weight terms, the proposed approach provides the possibility to adjust the total operation cost and duration of the tasks. The details of the developed algorithms are illustrated through different case studies.

A Trajectory Optimization-Based Intersection Coordination Framework for Cooperative Autonomous Vehicles

Since vehicles from multiple roads frequently merge at intersections, it formulates a typical traffic bottleneck of modern transportation systems. Proper vehicle coordination and motion plan at road intersections are of importance to guarantee safety as well as improving the traffic throughput, fuel efficiency and so on. In this paper, we try to present a general dedicated intersection coordination framework for autonomous vehicles, where both high- and low-level planners are appropriately designed and integrated. In the high-level planner, two different strategies are formulated to coordinate the autonomous vehicles to generate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reference trajectories</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feasible “tunnels</i> ”, respectively. Especially, a novel space-time-block based resource allocation scheme is presented to describe the feasible tunnels. Furthermore, to avoid collisions with unexpected obstacles such as pedestrians, bicycles or other vehicles with human drivers, a low-level planner is designed to generate practical trajectories based on the solutions from the high-level planner, according to their local on-board observations. Simulations and practical experiments are carried out, to show that our proposed coordination framework can achieve obvious performance advantages in various traffic metrics, including the throughput, fairness in driving maneuvers and driving comfort, etc. We also find that the high-level planner is effective in eliminating possible <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deadlocks</i> among autonomous vehicles, which is rarely discussed in existing investigations.

Pillar-Based Cooperative Perception from Point Clouds for 6G-Enabled Cooperative Autonomous Vehicles

3D object detection is a significant aspect of the perception module in autonomous driving; however, with current technology, data sharing between vehicles and cloud servers for cooperative 3D object detection under the strict latency requirement is limited by the communication bandwidth. The sixth-generation (6G) networks have accelerated the transmission rate of the sensor data significantly with extreme low-latency and high-speed data transmission. However, which sensor data format and when to transmit it are still challenging. To address these issues, this study proposes a cooperative perception framework combined with a pillar-based encoder and Octomap-based compression at edges for connected autonomous vehicles to reduce the amount of missing detection in blind spots and further distances. This approach satisfies the constraints on the accuracy of the task perception and provides drivers or autonomous vehicles with sufficient reaction time by applying fixed encoders to learn a representation of point clouds (LiDAR sensor data). Extensive experiment results show that the proposed approach outperforms the previous cooperative perception schemes running at 30 Hz, and the accuracy of the object bounding box results in further distances (greater than 12 m). Furthermore, this approach achieves a lower total delay for the procession of the fusion data and the transmission of the cooperative perception message. To the best of our knowledge, this study is the first to introduce a pillar-based encoder and Octomap-based compression framework for cooperative perception between vehicles and edges in connected autonomous driving.

Cooperative Distributed Estimation and Control of Multiple Autonomous Vehicles for Range-Based Underwater Target Localization and Pursuit

We study the problem of using single or multiple cooperative autonomous vehicles, called trackers, to localize and pursue an unknown underwater moving target using measurement of the ranges between the tracker(s) and the target. At the motion planning level, each tracker is assigned a spatial–temporal (S-T) curve to track that is generated by the composition of two types of motion: along the target’s trajectory and on a path encircling the target. At the control level, we derive control laws for robust trajectory tracking and show that under mild assumptions on the convergence of the target’s state estimate provided by a suitably designed filter, the tracker converges to and remains in a desired vicinity of the target. For the case of multiple trackers, we propose an efficient distributed estimation and control (DEC) strategy for the trackers that consider explicitly the constraints on the intertracker communication network. To this end, a distributed extended Kalman filter (DEKF) and a distributed control law for cooperative S-T curve tracking are developed to cooperatively pursue and localize the target. Using this setup, all trackers converge to a specified vicinity of the target while keeping an optimal tracker-target relative geometry that maximizes the range information acquired to estimate the target’s state. The stability of the complete closed-loop DEC system is analyzed rigorously and the efficacy of the proposed strategy is illustrated with extensive computer simulations for the 2-D and 3-D cases.

Research on Takagi-Sugeno Fuzzy-Model-Based Vehicle Stability Control for Autonomous Vehicles

Human–machine cooperative driving is an important stage in the development of autonomous driving technology. However, in emergencies, the problem of vehicle stability control for human–computer cooperative autonomous vehicles is still worthy of attention. This paper mainly realizes the stability control of the human–machine cooperative driving vehicle through active steering and considers the influence of the change of vehicle speed on the vehicle stability control performance. Therefore, a vehicle stability control method based on the superposition of steering torque is proposed, in which the Takagi-Sugeno fuzzy model is used to solve the variable parameter problem. Firstly, a vehicle system model with steering moment as input is established to ensure that the driver can participate in the steering control. Secondly, the nonlinear T-S fuzzy model is established by fuzzifying the local linear model. Then, the parallel-distributed-compensation (PDC) method is used to design the vehicle stability controller, and the asymptotic stability of the system in the range of variable parameters is proved by using the Lyapunov stability principle. Finally, the simulation and experimental results show that the control method can improve the handling stability of the human–machine cooperative driving vehicle under the condition of vehicle speed variation.

An embedded software architecture for the development of a cooperative autonomous vehicle

An embedded software architecture for the development of a cooperative autonomous vehicle

An embedded software architecture for the development of a cooperative autonomous vehicle

An embedded software architecture for the development of a cooperative autonomous vehicle

Ecological control strategy for cooperative autonomous vehicle in mixed traffic considering linear stability

Purpose This paper aims to present a cooperative adaptive cruise control, called stable smart driving model (SSDM), for connected and autonomous vehicles (CAVs) in mixed traffic streams with human-driven vehicles. Design/methodology/approach Considering the linear stability, SSDM is able to provide smooth deceleration and acceleration in the vehicle platoons with or without cut-in. Besides, the calibrated Virginia tech microscopic energy and emission model is applied in this study to investigate the impact of CAVs on the fuel consumption of the vehicle platoon and traffic flows. Under the cut-in condition, the SSDM outperforms ecological SDM and SDM in terms of stability considering different desired time headways. Moreover, single-lane vehicle dynamics are simulated for human-driven vehicles and CAVs. Findings The result shows that CAVs can reduce platoon-level fuel consumption. SSDM can save the platoon-level fuel consumption up to 15%, outperforming other existing control strategies. Considering the single-lane highway with merging, the higher market penetration of SSDM-equipped CAVs leads to less fuel consumption. Originality/value The proposed rule-based control method considered linear stability to generate smoother deceleration and acceleration curves. The research results can help to develop environmental-friendly control strategies and lay the foundation for the new methods.

Safety Evaluation of Turbo-Roundabouts with and without Internal Traffic Separations Considering Autonomous Vehicles Operation

The paper presents a microsimulation approach for assessing the safety performance of turbo-roundabouts where Cooperative Autonomous Vehicles “CAVs” have been introduced into the traffic mix alongside conventional vehicles “CVs”. Based on the analysis of vehicle trajectories from VISSIM and subsequent analysis of traffic conflicts through the Surrogate Safety Assessment Model (SSAM), the research aims to evaluate the safety benefits of turbo-roundabouts where the lanes are physically separated by raised curbs, compared to roundabouts without such curbs. The paper will then describe the methodological path followed to build VISSIM models of turbo-roundabouts with and without raised curbs in order to calibrate the simulation models and estimate the potential conflicts when a higher percentage of CAVs are introduced into the traffic mix. A criterion has been also proposed for setting properly the principal SSAM filters. The results confirmed both higher safety levels for turbo-roundabouts equipped with raised lane dividers compared to turbo-roundabout solutions without curbs, and better safety conditions under the traffic mix of CVs and CAVs. Therefore, it follows that, in absence of crash data including CAVs, the surrogate measures of safety are the only approach in which the safety performance of any roundabout or road entity can be evaluated.

Collaborative Autonomous Driving-A Survey of Solution Approaches and Future Challenges.

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

AVDM: A hierarchical command-and-control system architecture for cooperative autonomous vehicles in highways scenario using microscopic simulations

Microscopic agent-based traffic simulation is an important tool for the efficient and safe resolution of various traffic challenges accompanying the introduction of autonomous vehicles on the roads. Both the variety of questions that can be asked and the quality of answers provided by simulations, however, depend on the underlying models. In mixed traffic, the two most critical models are the models describing the driving behaviour of humans and AVs, respectively. This paper presents AVDM (Autonomous Vehicle Driving Model), a hierarchical AV behaviour model that allows the holistic evaluation of autonomous and mixed traffic by unifying a wide spectrum of AV functionality, including long-term planning, path planning, complex platooning manoeuvres, and low-level longitudinal and lateral control. The model consists of hierarchically layered modules bidirectionally connected by messages and commands. On top, a high-level planning module makes decisions whether to join/form platoons and how to follow the vehicle’s route. A platooning manoeuvres layer guides involved AVs through the manoeuvres chosen to be executed, assisted by the trajectory planning layer, which, after finding viable paths through complex traffic conditions, sends simple commands to the low-level control layer to execute those paths. The model has been implemented in the BEHAVE mixed traffic simulation tool and achieved a 92% success rate for platoon joining manoeuvres in mixed traffic conditions. As a proof of concept, we conducted a mixed traffic simulation study showing that enabling platooning on a highway scenario shifts the velocity-density curve upwards despite the additional lane changing and manoeuvring it induces.

Control Architecture for Cooperative Autonomous Vehicles Driving in Platoons at Highway Speeds

The situation in which a vehicle has to avoid a collision with an obstacle can be difficult to realise in optimum conditions when the roads are crowded. This paper uses the advantages of vehicle grouping and vehicle-to-vehicle (V2V) communication, and proposes a control architecture, which ensures a safe merging between the vehicles from two platoons. The architecture is formed by three layers, with the following tasks: i) to analyse the environment and to decide the best action for a certain vehicle, ii) to plan the new trajectory, and iii) to follow it at an imposed velocity or distance to the vehicle in front. The vehicles are equipped with a trajectory planner designed using two methods: the first one is based on a polynomial equation, and the second one is based on the model predictive control (MPC) algorithm. Each vehicle is also equipped with a trajectory follower, which has a cooperative adaptive cruise control (CACC) functionality based on a distributed model predictive control (DMPC) formulation. Also, the paper proposes a solution to compensate the data-packet-dropouts that are induced by the wireless communication network used to exchange information between vehicles. Moreover, to accommodate various realistic scenarios in the same control framework, the cost function for the DMPC algorithm was designed to take into account different communication topologies. The proposed architecture was tested in a simulation scenario, in which two platoons have to merge in order to avoid a fixed obstacle and the results show its efficiency.

RACE: Reinforced Cooperative Autonomous Vehicle Collision Avoidance

With the rapid development of autonomous driving, collision avoidance has attracted attention from both academia and industry. Many collision avoidance strategies have emerged in recent years, but the dynamic and complex nature of driving environment poses a challenge to develop robust collision avoidance algorithms. Therefore, in this paper, we propose a decentralized framework named RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE. Leveraging a hierarchical architecture we develop an algorithm named Co-DDPG to efficiently train autonomous vehicles. Through a security abiding channel, the autonomous vehicles distribute their driving policies. We use the relative distances obtained by the opponent sensors to build the VANET instead of locations, which ensures the vehicle's location privacy. With a leader-follower architecture and parameter distribution, RACE accelerates the learning of optimal policies and efficiently utilizes the remaining resources. We implement the RACE framework in the widely used TORCS simulator and conduct various experiments to measure the performance of RACE. Evaluations show that RACE quickly learns optimal driving policies and effectively avoids collisions. Moreover, RACE also scales smoothly with varying number of participating vehicles. We further compared RACE with existing autonomous driving systems and show that RACE outperforms them by experiencing 65% less collisions in the training process and exhibits improved performance under varying vehicle density.