Inappropriate lane changes are responsible for one-tenth of all accidents, due to human drivers’ inaccurate estimation and prediction of the surrounding traffic, illegal maneuver, and inefficient driving skill. Autonomous lane changing is regarded as a solution to reduce these human errors. At present, there are many obstacles to developing automated lane-changing technology, including interactions between vehicles, complex routing choice, and interactions between vehicles and the environment. Building on our prior work on lane keeping and lane changing, this collaborative research project aims to take a significant step forward to develop innovative solutions for autonomous lane change maneuvers.
Autonomous vehicles (AV) and connected vehicles (CV) technology has been much of the focus of transportation industry lately, and they will likely make a vast impact on the future of transportation systems. This project will combine AV and CV technologies for connected and autonomous vehicles (CAVs) to reduce congestion and improve network performance and safety by developing new tools and methods using reinforcement learning and nonlinear and optimal control techniques.
In order to guarantee the safety of autonomous vehicles (AV), improve passenger comfort, and increase traffic efficiency, we aim to develop innovative learning-based control methods for lane changing of connected and autonomous vehicles (CAVs) in mixed traffic by a combined use of reinforcement learning and optimal control techniques.