Publications

This paper develops a stochastic learning approach to integrate multiple CF models, rather than relying on a single model. The framework is based on approximate Bayesian computation that probabilistically concatenates a pool of CF models based on their relative likelihood of describing observed behavior. The approach, while data-driven, retains physical tractability and interpretability. Evaluation results using two datasets show that the proposed approach can better reproduce vehicle trajectories for both human-driven and automated vehicles than any single CF model considered.

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. 

This paper introduces an innovative data-driven methodology aimed at the analysis of disturbance amplification behavior within the context of automated vehicle car-following. This approach can accommodate situations involving unfamiliar controllers characterized by nonlinearity, and it leverages an empirical frequency response function for its implementation. 

This paper develops a sequencing-enabled hierarchical connected automated vehicle (CAV) cooperative on-ramp merging control framework. The proposed framework consists of a two-layer design: the upper-level control sequences the vehicles to harmonize the traffic density across mainline and on-ramp segments while enhancing lower-level control efficiency through a mixed-integer linear programming formulation.

This study introduces an integrated approach to optimize the placement of wireless charging lanes and fast-charging stations, featuring a bi-level optimization framework that addresses traffic congestion and includes an effective algorithm to solve the non-convex bi-level optimization problem.

This paper introduces a personalized stochastic optimal adaptive cruise control algorithm for automated vehicles, considering human drivers' risk sensitivity amidst uncertainties. The algorithm, based on the linear exponential-of-quadratic Gaussian framework, adjusts control based on deviations from desired car-following patterns. By integrating a risk-sensitive parameter, it accommodates varied risk preferences among drivers. Additionally, it offers both costly and cost-effective control modes by adjusting the cost-function weighting matrix.

This paper investigates these impacts on surrogate safety measures (SSMs) within mixed vehicular platoons through a two-level analysis structure. Numerical simulations were employed to generate trajectories considering communication and vehicle dynamics, facilitating the construction of an active safety evaluation framework. Microscopic analysis focused on controller dynamics and car-following policies, capturing local and aggregated driving behaviors' influence. The macroscopic analysis explored market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment effects on platoon safety due to behavioral differences.