Although there has been substantial research on longitudinal and lateral controllers for an automated driving system, stability issues with respect to the effect of uncertainties due to parameter variations (e.g. in the vehicle mass and the cornering stiffness) and disturbances or perturbations to the vehicle system (e.g. in the road gradient and the wind) still need to be addressed. Thus, an automated driving system needs to be made robust to those influences. For this purpose, the model-matching control applied to longitudinal and lateral automated driving is investigated by vehicle dynamics simulation. The design of the model-matching controller is obtained by using the characteristics of a two-degree-of-freedom controller. It can make various characteristics of automated driving vehicles equivalent to a specific transfer function, which is suggested as the reference model. The vehicle dynamics models including the model-matching controller are constructed for computer simulation. Then, simple examples of open-loop simulation and closed-loop simulation are solved to check the robustness of the model-matching controller. As a practical example, an automated driving system is adopted. It is proved that the model-matching control is effective and adequate for uncertainties due to parameter variation and disturbances or perturbations to the vehicle system, which are shown in the responses of the changes in the vehicle mass, the road gradient and the cornering stiffness.

This research aims to optimize the traffic signal cycle and the green light time per traffic signal cycle at ramps and intersections in arterials to maximize the passing traffic volume and minimize the delaying traffic volume in freeway corridors. For this purpose, we developed the MATDYMO (multi-agent for traffic simulation with vehicle dynamics model) and validated it with comparison to commercial software, TRANSYT-7F, for an interrupted flow model and to URFSIM (urban freeway traffic simulation model) for an uninterrupted flow model. These comparisons showed that MATDYMO is able to estimate the traffic situation with only incoming traffic volume. Using MATDYMO, ramp metering and traffic signal control can be optimized simultaneously. We extracted 80 sampling points from the DOE (Design of Experiment) and derived each response from MATDYMO. Then, a neural network was adopted to approximate the objective function, and simulated annealing was used as an optimization method. There are three cases of the objective function: maximization of the freeway traffic volume, minimization of the delay of ramps and arterials, and the satisfaction of both cases. The optimization results showed that traffic flow in freeway corridors can be maintained to a steady stream by ramp metering and signal control.

Freeway corridors consist of urban freeways and parallel arterials for alternative use. Ramp metering in freeways and signal control in arterials are contemporary traffic control methods that have been developed and applied in order to improve the traffic conditions of freeway corridors. However, most existing studies have focused on either optimal ramp metering in freeways or progressive signal strategies between arterial intersections. For efficient control of freeway corridors, ramp metering and signal control must be considered simultaneously, as otherwise the control strategies for freeway operation may disturb arterial traffic. On the other hand, traffic congestion and arterial bottlenecks that arise with increasing traffic volume at peak hours and ineffective signal operation may cause problems with accessibility to freeway ramps and degrade the urban freeway's ability to act as a through-traffic process. This research dynamically estimates the traffic stream between an urban freeway and its ramps according to changes in the freeway structure, traffic passing demand, and control methods due to restricted valid information. The results are then compared with those from other methods. Finally, the integrated control in the urban freeway traffic axis is optimized based on the expected traffic stream, by using design of experiment (DOE), neural network (NN), and a simulated annealing algorithm.

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