Cooperative Driving Research Articles

Research on Vehicle Risk Field Model for Edge Infrastructure at Cooperative Driving

Research on Vehicle Risk Field Model for Edge Infrastructure at Cooperative Driving

A Research Testbed for Intelligent and Cooperative Driving in Mixed Traffic

A Research Testbed for Intelligent and Cooperative Driving in Mixed Traffic

A Traffic-Aware Beacon Scheme for Cooperative Driving and Network Routing Trade-Off

A Traffic-Aware Beacon Scheme for Cooperative Driving and Network Routing Trade-Off

A homogeneous multi-vehicle cooperative group decision-making method in complicated mixed traffic scenarios

Connected and Automated Vehicles (CAVs) are expected to reshape the transportation system, and cooperative group intelligence of CAVs has great potential for improving transportation efficiency and safety. One challenge for CAV group driving is the decision-making under scenarios mixed with CAV and human-driven vehicles (HDV). Current studies mainly use methods based on single physical rules such as platoon driving or formation switch control, failing to reach a balanced and homogeneous state of optimal efficiency and risk in mixed traffic environments. In addition, most studies focus only on one specific type of scene, lacking the scene adaptability to various surrounding conditions. This paper proposes a homogeneous multi-vehicle cooperative group decision-making method targeting mixed traffic scenarios. A bi-level framework composed of behavior-level and trajectory-level decision-making is established to achieve balanced optimal cooperation. A region-driven behavioral decision mechanism is designed to decompose vehicle actions into a unified form of sequential target regions. Solutions are derived based on Cooperative Driving Safety Field, a risk assessment module inspired by field energy theory. The trajectory-level decision module takes the target regions as input and generates the control quantities of the CAVs through target point selection, conflict reconciliation, and dynamic constraint consideration. Experimental results on 19 various scenarios and continuous traffic flow scenes indicate that the proposed method significantly increases passing efficiency, reduces driving risk, and improves scene adaptability. In addition, experiments on multiple kinds of scenarios including intersections, ramps, bottlenecks, etc. prove that our method can adapt to various road topology structures. Feasibility is also verified through scaled physical platform validations and real-vehicle road tests.

Non-Cooperative and Cooperative Driving Strategies at Unsignalized Intersections: A Robust Differential Game Approach

Non-Cooperative and Cooperative Driving Strategies at Unsignalized Intersections: A Robust Differential Game Approach

Anti-Rollover Trajectory Planning Method for Heavy Vehicles in Human–Machine Cooperative Driving

The existing trajectory planning research mainly considers the safety of the obstacle avoidance process rather than the anti-rollover requirements of heavy vehicles. When there are driving risks such as rollover and collision, how to coordinate the game relationship between the two is the key technical problem to realizing the anti-rollover trajectory planning under the condition of driving risk triggering. Given the above problems, this paper studies the non-cooperative game model construction method of the obstacle avoidance process that integrates the vehicle driving risk in a complex traffic environment. Then it obtains the obstacle avoidance area that satisfies both the collision and rollover profit requirements based on the Nash equilibrium. A Kmeans-SMOTE risk clustering fusion is proposed in this paper, in which more sampling points are supplemented by the SMOTE oversampling method, and then the ideal obstacle avoidance area is obtained through clustering algorithm fusion to determine the optimal feasible area for obstacle avoidance trajectory planning. On this basis, to solve the convergence problems of the existing multi-objective particle swarm optimization algorithm and analyze the influence of weight parameters and the diversity of the optimization process, this paper proposes an anti-rollover trajectory planning method based on the improved cosine variable weight factor MOPSO algorithm. The simulation results show that the trajectory obtained based on the method proposed in this paper can effectively improve the anti-rollover performance of the controlled vehicle while avoiding obstacles.

Cooperative Driving of Connected Autonomous vehicle using Responsibility Sensitive Safety Rules: A Control Barrier Functions Approach

Cooperative Driving of Connected Autonomous vehicle using Responsibility Sensitive Safety Rules: A Control Barrier Functions Approach

Cooperative Control Research on Emergency Collision Avoidance of Human–Machine Cooperative Driving Vehicles

Cooperative Control Research on Emergency Collision Avoidance of Human–Machine Cooperative Driving Vehicles

An Innovative Cooperative Driving Strategy for Signal-Free Intersection Navigation with CAV Platoons

We present an innovative cooperative driving strategy known as Dynamic Resequencing and Platooning (DRP) designed to ensure the safe and efficient traversal of Connected and Automated Vehicles (CAVs) through signal-free intersections. By employing a Resequencing and Platooning Algorithm (RPA) grounded in state transition networks and CAV platooning, the optimal crossing sequence for CAVs is ascertained within a finite time. Through the utilization of a decentralized energy-optimal control framework, optimal trajectories are devised for CAVs, thereby facilitating optimal coordination among them. Simulation results underscore the substantial performance benefits of the DRP strategy compared to traffic light, First-In-First-Out (FIFO), and Local Dynamic Resequencing (LDR) strategies, with notable reductions observed in both travel delay and fuel consumption.

Game Theory-Based Interactive Control for Human–Machine Cooperative Driving

To address the safety and efficient driving issues of human–machine shared control vehicles (HSCVs) in future complex traffic environments, this paper proposes a game theory-based interactive control method between HSCVs and surrounding autonomous vehicles (SVs) and involves considering different driving behaviors. In HSCV, a comprehensive driver model integrating steering control and speed control is designed based on the brain emotional learning circuit model (BELCM), and the control authority between the driver and the automation system is dynamically allocated through the evaluation of the driving safety field. Factors such as driving safety and travel efficiency that reflect personalized driving style are considered for modeling the uncertain behavior of SVs. In the interaction between HSCVs and SVs, a method based on game theory and distributed model predictive control (DMPC) that considers the uncertainty of SVs’ driving behavior is established and is finally integrated into a multi-objective constraint problem. The driver control input in an HSCV will also be introduced into the solution process. To demonstrate the feasibility of the proposed method, two test scenarios considering the driving characteristics of different SVs are established. The test results show that automation control systems can promptly stop the human driver’s dangerous operations in an HSCV and safely interact with multiple AVs with different driving characteristics.

Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method

One of the most important goals of cooperative driving is to control connected automated vehicles (CAVs) passing through conflict areas safely and efficiently without traffic signals. As a typical application scenario, allocating right-of-way reasonably at unsignalized intersections can effectively avoid collisions and reduce traffic delays. Proposed here is a new cooperative driving strategy for CAVs at unsignalized intersections based on distributed Monte Carlo tree search (MCTS). A task-area partition framework is also proposed to decompose the mission of cooperative driving into three main tasks: vehicle information sharing, passing order optimization, and trajectory control. Based on the schedule tree of the vehicle passing order, the root parallelization of MCTS combined with the majority voting rule is used to explore as many feasible passing orders (leaf nodes) as possible in a distributed way and find a nearly global-optimal passing order within the limited planning time. The aim is for CAVs to perform proper trajectory adjustments based on the obtained passing order to minimize traffic delays while making the slightest acceleration adjustments. A coupled simulation platform integrating SUMO and Python is developed to construct the unsignalized intersection scenarios and generate the proposed distributed cooperative driving strategy. Comparative analysis with conventional driving strategies demonstrates that the proposed strategy significantly enhances efficiency, safety, comfort, and emission, aligning well with innovative and environmentally friendly urban mobility aspirations.

Towards the Next Level of Vehicle Automation Through Cooperative Driving: A Roadmap From Planning and Control Perspective

Towards the Next Level of Vehicle Automation Through Cooperative Driving: A Roadmap From Planning and Control Perspective

Human–Machine Cooperative Driving and Real-Time Algorithm Implementation Based on Double-Loop Control System

The double loop structure has been implemented to verify the feasibility of human-machine cooperative control in real environment in this paper, which can fully reflect the performance of the controller and realization of the sharing steering control between human driver and automatic control system. The outer loop adopts fuzzy proportional-integral-derivative(PID) controller to deal with different working conditions, on which the complexity of the algorithm is reduced and the real-time performance of the controller is improved. The inner loop adopts the active disturbance rejection controller (ADRC), which can effectively suppress the interference such as drivers’ faulty operations etc. and improve the tracking accuracy and system performance. The stability for extended state observe(ESO) and ADRC is described in theory to ensure the feasibility of the controller in the real world environment. The experiment results show that the human-machine cooperative control system based on the double-loop controller can effectively reduce the lateral error of the vehicle and the steering torque of the driver in the process. And the control performance of the vehicle is improved.

Infrastructure 2D Camera-based Real-time Vehicle-centered Estimation Method for Cooperative Driving Support

Infrastructure 2D Camera-based Real-time Vehicle-centered Estimation Method for Cooperative Driving Support

Theoretical Analysis of Cooperative Driving at Idealized Unsignalized Intersections

Theoretical Analysis of Cooperative Driving at Idealized Unsignalized Intersections

Toward Consumer Acceptance of Cooperative Driving Systems: A Human-Centered Shared Steering Control Approach Within a Hierarchical Framework

Toward Consumer Acceptance of Cooperative Driving Systems: A Human-Centered Shared Steering Control Approach Within a Hierarchical Framework

Investigating the Effects of Human–Machine Interface on Cooperative Driving Using a Multi-Driver Co-Simulation Platform

Investigating the Effects of Human–Machine Interface on Cooperative Driving Using a Multi-Driver Co-Simulation Platform

Towards Cooperative Driving among Heterogeneous CAVs: A Safe Multi-Agent Reinforcement Learning Approach

Towards Cooperative Driving among Heterogeneous CAVs: A Safe Multi-Agent Reinforcement Learning Approach

Theoretical Trade-Off Between Fairness and Efficiency in the Cooperative Driving Problem for CAVs at On-Ramps

Theoretical Trade-Off Between Fairness and Efficiency in the Cooperative Driving Problem for CAVs at On-Ramps

A Bi-Level Network-Wide Cooperative Driving Approach Including Deep Reinforcement Learning-Based Routing

A Bi-Level Network-Wide Cooperative Driving Approach Including Deep Reinforcement Learning-Based Routing