This study presents an adaptive motion planning strategy for automated vehicle collision avoidance systems to be associated with the variation of collision speed region based on the position of the obstacle. This is done by designing the motion planner using an artificial potential field (APF) with the incorporation of an adaptive multi‐speed scheduler using fuzzy system in the motion planning structure. The knowledge database information is developed based on the risk perception of the driver that consists of APF parameters and was optimised by using particle swarm optimisation algorithm. This study contributes to the improvement of a feasible reference motion generated by the motion planner that can be converted into desired control signals. The reference motion resulted to provide the control command that managed to avoid collision successfully by evasive manoeuvre without lane departure when adapting to variation in the vehicle speeds with different obstacle positions. The results indicated the reduction of the lateral error with respect to the reference avoidance trajectory data of up to 87% compared to base‐type APF with maximum reference lateral motion is reduced of up to 26%. Then, a hardware‐in‐loop test is conducted to verify the proposed strategy using a steering wheel system.

In terms of vehicle dynamics, motion sickness (MS) occurs because of the large lateral acceleration produced by inappropriate wheel turning. In terms of passenger behavior, subjects experience MS because they normally tilt their heads towards the direction of lateral acceleration. Relating these viewpoints, the increment of MS originates from the large lateral acceleration produced by the inappropriate wheel’s turn, which then causes greater head movement with respect to the lateral acceleration direction. Therefore, this study proposes the utilization of fuzzy-proportional integral derivative (PID) controller for an MS minimization control structure, where the interaction of the lateral acceleration and head tilt concept is adopted to diminish the lateral acceleration. Here, the head movement is used as the controlled variable to compute the corrective wheel angle. The estimation of the head movement is carried out by an estimation model developed by the radial basis function neural network (RBFNN) method. An experiment involving a driving simulator was conducted, to verify the proposed control system’s performance in regard to the autonomous vehicle’s passengers. The results show that the averages of motion sickness incidence (MSI) index can be lowered by 3.95% for single lap and 11.49% for ten laps.

This research presents a new hybrid deep-learning model for object identification and classification. The model combines the excellent classification performance of a Convolutional Neural Network (CNN) with the strong detection capabilities of YOLOv8. The suggested approach generates bounding boxes that indicate probable objects, using YOLOv8 for initial object localization. Subsequently, CNN further enhances these detections by categorizing the recognized items into compost and non-compost. This dual-phase technique enhances detection accuracy and classification precision, successfully tackling common obstacles encountered in real-world visual identification tasks, such as intricate backgrounds and diverse item sizes. The effectiveness of the hybrid model is evaluated using a specialized dataset related to waste recycling, which demonstrates significant improvements compared to individual models. The findings indicate that the hybrid YOLOv8 and CNN model attains an F1 score of 0.86, precision of 0.85, recall of 0.87, and accuracy of 0.88, surpassing the performance of both YOLOv8 and CNN models. The findings indicate that this comprehensive method provides a hopeful resolution for tasks that require the precise and effective identification of objects, thereby making it a valuable addition to computer vision and deep learning. The research analyses the model’s structure, the training process, and the experimental outcomes, highlighting the advantages of combining YOLOv8 and CNN approaches to enhance the overall performance in tasks related to object recognition and classification.

Motion sickness (MS) usually occurs when travelling in a moving vehicle, and especially experienced by the passengers compared to the driver. The difference in their head movements with respect to the direction of lateral acceleration affects the MS severity level. When experiencing curvature, the passengers normally tilt their head in the same direction as the lateral acceleration, while the driver tilts his/her head against it. This study proposes a correlation model between the lateral acceleration of the vehicle and the head movements of the driver and a passenger via an artificial neural network. Experimental datasets were used in the modelling process. The influence of the number of hidden neurons with respect to the model accuracy has also been investigated. Then, the correlation from the model was expressed as a mathematical equation. This mathematical representation model can be beneficial in the design of vehicle motion control systems in order to mitigate the MS effect.