This paper proposes a novel lane change path optimization algorithm for emergency collision avoidance in intelligent vehicles. Traditional path planning often faces challenges such as optimizing many variables, being unsuitable for emergency scenarios, or producing paths that are difficult for vehicles to follow accurately. Our approach employs lagged longitudinal and lagged curvature models with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G^{2}$</tex-math></inline-formula> continuous curves, generating reliable collision avoidance trajectories under low-level actuator time lag. The lagged longitudinal model is designed to be differentiable in the spatial domain, allowing curvature optimization in the curvilinear coordinates. The lagged curvature model facilitates rapid lane changes, making it suitable for emergency collision avoidance. The proposed method optimizes only dual connecting points and enhances computational efficiency using analytic gradient-based nonlinear programming. Comparisons with existing path planning methods–namely, the fifth-order spline optimization method, model predictive control-based optimization, and the time-domain fifth-order polynomial optimization method–on straight and curved roads demonstrate that the proposed method minimizes collision avoidance areas more effectively. The proposed path is verified with a simple tracking controller in high-fidelity simulation using TruckSim.

This study proposes an innovative fault diagnosis algorithm developed for the 4WIS4WID vehicle system, aiming to overcome fault location and identification challenges. The 4WIS4WID system utilizes hardware redundancy and fault-tolerant control to maintain vehicle operation even when a fault occurs. However, because the number of degrees of freedom of the vehicle is less than the number of faults, it is impossible to distinguish analytically between faults. The proposed algorithm employs residual analysis to address this limitation, capturing the differences between predicted vehicle behavior and actual sensor data. The residuals are converted into a three-channel image using a 2-D histogram and a logical function to form a single fault image. A convolutional neural network (CNN) learns these fault images to detect the occurrence of a fault and accurately determine its location. The Recursive Least Square (RLS) algorithm is utilized to classify fault types. This method identifies the fault's size and type, and the vehicle's output in which the fault occurred is estimated. The proposed algorithm's fault diagnosis and classification performance are verified through CarMaker-based simulation. This systematic approach to fault diagnosis enhances vehicle safety and reliability in intelligent vehicle applications.