Coordinated Driving in Connected and Autonomous Vehicle System: Optimal Advance Lane Change Zones and Coordinated Platoon Car Following Control.

摘要

The connected and autonomous vehicle (CAV) system enables countless innovative coordinated driving approaches, such as coordinated lane change and car-following in microscopic CAV control, and coordinated rounding and parking in macroscopic traffic flow guidance, which will improve the performance of our transportation system by enhancing traffic mobility, providing safe driving environment and reducing fuel consumption. Since the lane change and car-following behavior are indicated as crucial factors of traffic safety and efficiency, this dissertation focuses on developing the coordinated driving schemes in microscopic control and operation of lane change and car-following maneuvers. In particular, I develop an lane change zone optimization strategy and the coordinated platoon car-following control for a pure CAV platoon and a mixed platoon (i.e. mixed with human-drive vehicles and CAVs) respectively.;This dissertation first explore the management strategy of the mandatory lane change near a two-lane highway off-ramp by optimizing the location of advance warning. The proposed approach considers that the area downstream of the advance warning includes two zones: the green and yellow zones corresponding to their respective most like lane change maneuvers. An optimization model is proposed to search for the optimal green and yellow zones. Traffic flow theory such as Greenshield model and shock wave analysis are used to analyze the impacts of the S-MLC and D-MLC maneuvers on the traffic delay. Numerical experiments indicate that the proposed optimization model can identify the optimal location to set the advance MLC warning nearby an off-ramp so that the traffic delay resulting from lane change maneuvers is minimized, and the corresponding capacity drop and traffic oscillation can be efficiently mitigated.;Then, this research develops a novel car-following control scheme for a platoon of connected and autonomous vehicles on a straight highway. The platoon is modeled as an interconnected multi-agent dynamical system subject to physical and safety constraints. A constrained optimization based control scheme is proposed to ensure an entire platoon's transient traffic smoothness and asymptotic dynamic performance. This dissertation develops dual based distributed algorithms to compute optimal solutions with proven convergence. Furthermore, the asymptotic stability of the unconstrained linear closed-loop system is established. These stability analysis results provide a principle to select penalty weights in the underlying optimization problem to achieve the desired closed-loop performance for both the transient and the asymptotic dynamics.;By the motivation that CAVs and human-drive vehicles will co-exist on the road for a long period in the near future, the third part of this dissertation extends the pure CAV coordinated platooning control to the mixed flow environment. By integrating the Newell car-following model, a real-time curve matching algorithm is implemented to calibrate the ca-following model and anticipate the movement of human-drive vehicle by the real-time trajectory data. The constrained MPC are developed for each CAV platoon, considering their movement interaction through the human-drive vehicle platoon. Furthermore, this study provide a modified dual based distributed algorithm to improve convergence speed of the primal problem for the dual based distributed algorithm in Chapter 4. Several requirements of the penalty weights selection are provided by stability analysis under the unconstrained conditions. The numerical experiments based on field data will be conducted to illustrate the effectiveness and efficiency of the proposed the solution approach and the platoon control schemes.