This paper proposes a combined observer/controller method that estimates the states and simultaneously improves the ride comfort and stability of a full vehicle active suspension system using a single inertial measurement unit (IMU) in the presence of noise and centre of gravity uncertainties. The derived model is based on a channel-by-channel estimation technique using filtered white noise excitation signals on all the wheels, as well as active suspension actuators. The transfer functions of the vehicle system are estimated by analysing the IMU signals in the frequency domain. The derived result is then used in a linear quadratic regulator to calculate the actuators' forces to improve the vehicle's ride comfort and road holding stability within the limits of the rattle space. The simulation results show the efficacy of the proposed approach. Specifically, the observer estimates the actual behaviour of the vehicle with $95\mathrm{\%}$ accuracy with up to $20\mathrm{\%}$ uncertainty. Finally, a parametric study has been investigated to ascertain the applicability of the proposed approach in estimating the vehicle dynamics with simultaneously improving the ride comfort and road holding stability.

This paper presents a solution to the time-optimal control of the relative formation of multiple vehicles. This is a problem in cooperative time-optimal control with a free terminal state constraint. In this paper, a canonical formulation of the problem is first derived. Then, a numerical technique to solve this class of problem is proposed. Numerical results demonstrate the efficacy of the proposed formulation and solution to the problem of expeditiously building and controlling formations of cooperative autonomous vehicles.

This paper presents a technique to reconstruct a three-dimensional (3D) road surface from two overlapped images for road defects detection using a downward-facing camera. Since some road defects, such as potholes, are characterized by 3D geometry, the proposed technique reconstructs road surfaces from the overlapped images prior to defect detection. The uniqueness of the proposed technique lies in the use of near-planar characteristics of road surfaces‘ in the 3D reconstruction process, which solves the degenerate road surface reconstruction problem. The reconstructed road surfaces thus result from the richer information. Therefore, the proposed technique detects road surface defects based on the accuracy-enhanced 3D reconstruction. Parametric studies were first performed in a simulated environment to analyze the 3D reconstruction error affected by different variables and show that the reconstruction errors caused by the camera’s image noise, orientation, and vertical movement are so small that they do not affect the road defects detection. Detailed accuracy analysis then shows that the mean and standard deviation of the errors are less than $0.6$ mm and 1 mm through real road surface images. Finally, on-road tests demonstrate the effectiveness of the proposed technique in identifying road defects while having over 94% in precision, accuracy, and recall rate.