Qin Xiaohui

TL;DR: This paper extends existing studies on distributed platoon control to more generic topologies with complex eigenvalues, including both internal stability analysis and linear controller synthesis, and proposes a Riccati inequality based algorithm to calculate the feasible static control gain.

TL;DR: In this paper, a robust distributed control method for vehicular platoons with bounded parameter uncertainty and a broad spectrum of interaction topologies is presented, where the nonlinear node dynamics are reduced to an uncertain linear model by using inverse model compensation.

TL;DR: In this paper, a unified hierarchical framework for platoon control of connected vehicles with two different types of control modes, i.e., desired acceleration control and desired velocity control, is proposed, where an observer is designed for following vehicles to observe the leading vehicle's states through vehicle-to-vehicle communication.

TL;DR: In this paper, an anti-collision control method based on depth reinforcement learning was proposed, which includes the steps that first, vehicle parameters and environmental vehicle parameters are extracted; second, a virtual environment model is constructed through the vehicle parameters, the environmental vehicle and the virtual environment, basic parameters of the depth deterministic policy gradient method are defined; fourth, according to the basic parameters defined in the third step, a neural network in depth RL is used for constructing an anticollision controller decision making system, and the anti-coder decision-making system comprises a strategy network

TL;DR: Vehicle event data recorder data is utilized to model driver behavior in critical situations for PTA and can provide accurate models of driver behaviorIn critical situations and contribute to the development of TA for IVs.