Qiangqiang Guo

TL;DR: Six types of CAV-based traffic control methods are summarized and a conceptual mathematical framework is proposed that can be specified to each of six three types of methods by selecting different state variables, control inputs, and environment inputs is proposed.

TL;DR: A model-based predictive controller for fuel-saving ACC is presented to improve the performances on tracking accuracy and fuel consumption by simultaneously considering the road elevation information, nonlinear powertrain dynamics, and spatiotemporal constraint from preceding vehicle.

TL;DR: A short-term traffic states forecasting algorithm based on partial least square (PLS) to help enhance real-time decision-making and build better insights into traffic data.

TL;DR: A hybrid deep Q-learning and policy gradient method is developed for vehicles driving along multi-lane urban signalized corridors that can enable the controlled vehicle to learn well-established longitudinal fuel-saving strategies, and to perform appropriate lane-changing operations at proper times to avoid congested lanes.

TL;DR: Simulations in both uniform and natural traffic flows demonstrate that this algorithm achieves a significant fuel-saving benefit in automated car-following scenarios up to 8.9% in naturalistic traffic flow (when coasting at neutral gear), compared with a linear quadratic (LQ) controller.