The increasing demand for capacity in railway transportation has spawned the concept of virtual coupling (VC), which can further shorten the intertrain distance by applying communication technology. In this article, a centralized model predictive control (MPC) is developed for virtually coupled train set (VCTS) with a nonlinear safety equilibrium spacing policy over gradient rolling terrains. The tracking target of the controller in cruise phase is given based on the braking distance difference and speed difference of trains. A more precise braking distance model of trains explicitly considering the gradient is introduced for safety, based on which the state space applicable to the controller for VCTS is derived. To deal with the nonlinearities of the controller, a new optimization solving algorithm based on the continuation method and generalized minimum residual (GMRES) method is presented in this article. Finally, controller performances and empirical string stability of VCTS are verified by numerical simulations. It is proven that the proposed controller has a good effect for the safety and smoothness of the train operation on the railway line over gradient terrain through the test. The actual intertrain distance under the proposed controller is about 1 m closer to the equilibrium state than that under the same controller without considering the gradient at low speed (about 10 m/s) on the 0.3% downhill line, which verifies the safety of the controller. Besides, the influence of the parameters of the controller and optimization method on controller efficiency is analyzed, and for the latter, the simulation data show that the applied algorithm for the optimal control solver can save the calculation time more than seven times than the traditional algorithm.

This paper presents an innovative bird‐view control framework for connected automated vehicles (CAV). Most recently tested automated vehicles are based on sensing systems equipped on the car body, which require the self‐driving policy to be robust and adaptive to various environmental uncertainties. Inspired by the vehicle to infrastructure technologies, the self‐driving technology can also be achieved through the communication between road infrastructure and the vehicle, where sensors are mainly installed on the road in a high position, which can collect traffic information from a bird‐view. To this end, we developed a fusion‐based Q‐learning method to yield an optimal bird‐view control policy for a CAV on a multi‐lane road. With our control policy, the CAV can drive smartly under complicated traffic environment, interacting with leading vehicles and crossing traffic simultaneously. A series of case studies show our CAV control policy is string stable and can avoid collisions under various scenarios.

Problem definition: Many cities worldwide are embracing electric vehicle (EV) sharing as a flexible and sustainable means of urban transit. However, it remains challenging for the operators to char...

This paper presents a stochastic calibration method based on Approximate Bayesian Computation (ABC). This method is applied to calibrate two car-following control models: linear control and model predictive control (MPC). The method is likelihood-function-free, where the likelihood function is replaced by simulation to approximate the posterior distribution of model parameters. This structure affords flexibility to calibrate posterior joint distributions of complex models, even those without analytical closed forms such as MPC. Two experiments were conducted to evaluate how well the proposed method reproduces: (i) marginal and joint distributions of model parameters, using synthetic data and (ii) vehicle trajectories (acceleration, speed, and position), using field data involving two commercial adaptive cruise control (ACC) systems. The results showed that the ABC method can reproduce marginal and joint distributions reasonably well for the linear controller as well as the non-analytical MPC-based controller, which was previously infeasible. The method can also robustly characterize the commercial ACC behavior at the trajectory level, which suggests that the simple linear controller better describes their behavior.

Obtaining sufficient trajectory data of human-driven vehicles (HDVs) is critical for effective control of connected automated vehicles (CAVs) in mixed traffic of HDVs and CAVs. However, due to limited sensing and communication capabilities, only a fraction of HDVs’ trajectories are often observed. This paper proposes a completion method to recovery all HDVs’ trajectories in a mixed platoon based on partial observations. The trajectory completion problem is formulated as an optimization problem, aiming to minimize the error between observed and completed trajectories with car-following constraints defined by Newell’s simplified car-following model. The method also allows various model parameters of different drivers, which is known as the inter-driver heterogeneity, to reduce the completion error. Validation using empirical trajectory data shows that the proposed method greatly lowers the completion error than other typical trajectory completion methods under sparse observation (6s/sample).