Traffic congestion and shockwave damping through advanced driver assistance system (ADAS) longitudinal vehicle control

The Advance Driver Assistance System (ADAS) helps vehicle drivers drive more efficiently, comfortably, and safer. Longitudinal Autonomous Vehicle (AV) control models such as Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) models have been intensively studied over the last decade. One limitation identified in this study is the accelerating effect of congestion shockwaves by autonomous vehicle platoons due to the instant response of those systems to preceding stimuli. Stop-and-go waves or traffic shockwaves can be generated naturally by human drivers or roadway bottlenecks such as merging sections. In some cases, if a small perturbation occurs downstream, the traffic shockwave speed could instantly propagate upstream and even lead to congestions with aggressive driving behaviors or badly designed vehicle control models. This case can amplify the traffic instability and deteriorate the traffic flow. This study designs ADAS longitudinal control models for AVs to dampen the traffic shockwave propagation speed and mitigate the congestion. The rolling horizon control model, also called Model Predictive Control (MPC), is implemented to balance the trade-offs among multiple objectives, including safety, efficiency, and driving comfort. Vehicles models are designed separately depending on their capability of wireless communication. On the one hand, vehicles without wireless communication capability, such as ACC vehicles, can only obtain the velocity and position of the preceding vehicle through sensors, such that the congestion and shockwave propagation speed can be dampened by implementing the proposed cost function terms. With the proposed cost terms, the controlled vehicle will reduce the spacing with larger by still safe velocity according to the preceding vehicle’s position and velocity. On the other hand, vehicles with Vehicle-to-Vehicle (V2V) wireless communication capability, such as CACC vehicles, enable the ego vehicle to acquire the acceleration, velocity and position of preceding vehicles or even vehicles far downstream. A control model is proposed for vehicles to smoothly decelerate to a target speed to adapt to the detected shockwave downstream. The proposed framework is applied in both ACC and CACC platoons mixed with manual vehicles, and five different control models are explored. The stability conditions of the proposed models have been derived analytically and analyzed numerically. Three experimental studies, including platoon, ring-road, and traffic simulation studies, indicate promising results of proposed models in reducing shockwave propagation speed and mitigating traffic congestion under different environments compared with existing ACC and CACC models.Ph.D.Includes bibliographical reference