With the increasing integration of Autonomous Vehicles (AVs) into traffic systems, the interaction between different types of vehicles presents a significant challenge for cooperative decision-making, particularly in mixed traffic environments where AVs coexist with Human-driven Vehicles (HVs). Among various scenarios, highway on-ramp merging is a critical and complex one where effective interaction is essential for ensuring traffic safety and efficiency, and decisions of AVs and HVs should be made efficiently. In this paper, we propose a Game-Theoretic Reinforcement Learning (GTRL)-based vehicle behavior interaction framework designed for various merging scenarios in mixed traffic environments. This framework includes Stackelberg leader-follower interactions between AVs and HVs, as well as Nash cooperative interactions between vehicles of the same type, such as AV-AV or HV-HV. We formulate the problem as a Partially Observable Markov Decision Process (POMDP) and employ the Bi-level Reinforcement Learning (Bi-RL) method and Safe Multi-Agent Deep Q-Network (MADQN) method to address two distinct types of interaction problems. A gym-based simulation environment is developed to evaluate four interaction scenarios between vehicles in mixed traffic. Comprehensive experimental results demonstrate the potential of the GTRL-based behavior interaction framework to adapt to the dynamic and uncertain nature of vehicle interactions.

This study investigates the problem of road side unit (RSU) location optimization considering vehicle-to-RSU (V2R) communication channel competition. To hedge against the uncertainty of vehicle density, the problem is formulated as a stochastic mixed-integer nonlinear program with equilibrium constraints. This program aims to minimize the expectation of weighted sum of V2R communication delay, packet loss rate and packet collision rate and age of information in V2R communication over all scenarios given RSU location budget limit. Decision variables are RSU locations and the number of connected autonomous vehicles (CAVs) communicating with each located RSU. Equilibrium constraints in the program model V2R communication channel competition among CAVs and ensures the choice of CAVs on RSUs to satisfy user equilibrium principle. The V2R communication is calculated under 6G technology. The program is linearized by using piecewise linearization method. To enhance the solution efficiency, a progressive hedging algorithm is developed to decompose the relaxed linearized model into several subproblems. The optimal solution to the relaxed linearized model is found by iteratively formulating and the solving subproblems. A branch and bound algorithm is introduced to obtain the optimal integer solution to the linearized model. The numerical results show that the proposed model can achieve 20.55% lower total communication delay than the state-of-the-art model only optimizing total V2R information propagation delay, when CAVs choose RSUs for communication in a competitive manner.

This paper proposes a methodology combining Long-Short-Term-Memory (LSTM)-assisted kinematic motion prediction with a hybrid coding algorithm for compressing the trajectory data of Connected Autonomous Vehicles (CAVs). The vehicle locations after the first two time steps are predicted based on the vehicle positions at the first two time steps and the kinematic equation. The vehicle velocities and accelerations are predicted based on the vehicle locations and LSTM. The hybrid coding algorithm integrates differential coding, Binary Coded Decimal (BCD) coding and arithmetic coding. Differential coding converts the original data into the difference between the original data and the predicted data. Since the length of the original data is large but the difference between it and predicted data is small, the required space for storing the data can be greatly reduced. BCD coding converts subsequences of different lengths to the subsequences with the same length so that the original information can be correctly reproduced after decompression. Arithmetic coding expresses the information in small space by converting the character sequence into a decimal between 0 and 1. The proposed algorithm is evaluated on the Next Generation Simulation Trajectory dataset. The experiment results show that the compression ratio and compression rate obtained by the proposed algorithm are respectively higher and lower than those obtained by the baseline algorithms. Also, the sum of compression time, decompression time and transmission time associated with the proposed algorithm is less than that associated with most baseline algorithms and transmission without compression.

Cooperative Adaptive Cruise Control (CACC) systems can significantly improve traffic safety and roadway capacity utilizing short following gaps of vehicles enabled by inter-vehicle communications. However, due to merging processes occurring at freeway merging areas, existing CACC operation approaches are generally not applicable and the operation will have to revert back to Adaptive Cruise Control (ACC) or human-driven mode, which in turn will result in a capacity drop. This paper proposes an optimal trajectory optimization strategy for Connected and Automated Vehicles (CAVs) to cooperatively carry out mainline platooning and on-ramp merging. Firstly, a control framework of the CACC is adopted for a longitudinal control of CAVs, which helps individual CAVs to join platoons and to maintain platoon operations. Secondly, to ensure smooth lane-changing executions while achieving stable platoons, an optimal controller that considers lane-changing motivation of merging vehicles and impact of merging on platoons, is proposed. Third, a Legendre pseudo-spectral algorithm is applied to transform the controller into a simpler nonlinear programming problem and to efficiently solve it. Simulation assessments of the proposed method are conducted at both individual vehicle level and traffic-flow level. At the individual vehicle level, the proposed method has the potential to improve the traffic safety without compromising fuel consumption and emissions compared with unoptimized feasible schemes. At a traffic-flow level, an online evaluation platform is implemented, and a typical freeway on-ramp area is studied. The simulation results have demonstrated that the proposed controller provides significant improvements in terms of efficiencies in traffic operations.

This study deals with the problem of road side unit (RSU) location optimization for origin-destination (OD) demand estimation. With the point-to-point measurement provided by RSUs in connected vehicle environment, the errors of OD demand estimation come from two sources: 1) the lack of enough path flow information; and 2) the vehicle-to-RSU (V2R) communication delay. However, increasing the amount of path flow information collected by RSUs results in the increase of V2R communication delay encountered by each collected data packet. Moreover, it is difficult to find a global optimal solution by formulating the problem as a single objective program. To address the investigated problem, this study proposes a novel framework consisting of solving a bi-objective RSU location optimization problem and an OD demand estimation problem. This RSU location optimization problem is formulated as a bi-objective nonlinear binary integer program to balance the maximization of the amount of path flow information and the minimization of V2R communication delay. The OD demand estimation problem is formulated as a least square estimator to identify the RSU location scheme with the smallest OD demand estimation error, among the Pareto optimal solutions to the bi-objective program. An efficient$\varepsilon $-constraint method is developed to generate the Pareto optimal solutions. The numerical example demonstrates that the proposed framework achieves 6.95 lower root-mean-square error of OD demand estimation, compared with the baseline framework.

In this study, a Human-Lead-Platoon CACC ((HLP-CACC) controller is proposed for connected and automated vehicles to “include” human drivers in platooning process. The goal is to form a platoon between automated vehicles and human drivers so that turbulence caused by human drivers could be smoothed out by automated vehicles. Unlike the conventional CACC where only longitudinal control is automated, the proposed HLP-CACC regulates both longitudinally and laterally. In other words, the followers in an HLP-CACC platoon are fully autonomous. The controller is formulated utilizing model predictive control (MPC) solved by Chang-Hu’s method. The technology has the following advantages: 1) take advantage of human drivers’ perception to enable conditional full autonomy; 2) accommodate actuator delay in system dynamics to improve actuator control accuracy; 3) automates both longitudinally and laterally; and 4) ensures string stability in partially connected and automated vehicles environment. Both simulation tests and field tests were conducted to verify the effectiveness of the proposed algorithm. Four scenarios, including straight cruising, lane changing, U-turn and circling were tested. Sensitivity analysis was conducted for speed, turning radius, communication delay and oscillation acceleration. The results confirm that the proposed CACC controller is ready for field implementation. The computation time of the proposed optimal control is approximately$4~\sim ~8$milliseconds when running on an NVIDIA Drive PX 2 computer. Under the control of the proposed HLP-CACC, maximum longitudinal error and lateral error are both within 40 cm.