The simplified vehicle model often results in inaccuracy with respect to the conventional model predictive control (MPC) as it causes steady error in tracking control, which has negative implications for vehicle cornering. This study presents a trajectory planning and tracking framework, which applies artificial potential to obtain target trajectory and MPC with PID feedback to effectively track planned trajectory. The experimental and simulation results are then presented to demonstrate the improved performance in tracking accuracy and steering smoothness compared to that of the conventional MPC control. Especially during negotiating a curve, its steady state error is close to 0.

In scenarios characterized by significant curvature, such as curves, the presence of steering system hysteresis and linearization of the vehicle model may lead to understeer and steady-state errors, thereby exerting an adverse impact on the performance of the tracking control system. To address these formidable challenges, this study introduces an innovative path planning and tracking framework. This framework leverages batch informed trees to derive the reference path and employs Nonlinear Model Predictive Control (NMPC) with pre-steering to accomplish efficient tracking of the predetermined path. Additionally, a smart car experiment platform has been established for simulating and validating various scenarios, with results demonstrating improved performance in tracking accuracy and steering smoothness when compared to Traditional Model Predictive Control (TMPC). Particularly in scenarios involving substantial curvature, such as curves, the proposed framework exhibits a nearly 50% reduction in root mean square error, underscoring its enhanced performance.

In real traffic environment, a single control mode of traditional autonomous vehicles cannot meet various driving requirements for different drivers, which will decrease the acceptance of autonomous vehicles, and even may further cause traffic risks. This paper studies the cooperative strategies between ego vehicle and surrounding vehicles with the naturalistic experiment data, and then designs an autonomous vehicle control method based on the distributed Model Predictive Control (MPC) in order to consider the interaction relationship of ego vehicles and surrounding vehicles. Finally, the proposed method is verified by software simulation and Hardware in the Loop (HIL) simulation experiments, and the experiment results demonstrate that the control method proposed in this paper not only can control the vehicle to complete the typical driving tasks smoothly, in terms of car-following and lane-changing, but also can reflect the different cooperative strategies among different driving behavior characteristics, which can improve safety and acceptance of autonomous vehicles to promote the practical application of autonomous vehicle technology.

Model Predictive Control (MPC) method is widely used in autonomous vehicle control technology. The adjustment of MPC weights is crucial for optimizing its control performance, ensuring precise and reliable operation. Traditionally, these weights are adjusted manually, which is inefficient. This study introduces a novel Butterfly Optimization Algorithm (BOA) learning-based method to determine the optimal MPC weights in an efficient way. By adopting the data-driven idea in machine learning, the trajectory data of field experiment human drivers is used to train the controller weights. A simulation-based training platform that enables the automatic training of the MPC controller with varying weights is also developed. Simulation results demonstrate the superior control accuracy and stability performance of BOA learning-based method compared to Linear Quadratic Regulator (LQR) and pure pursuit strategies. The findings suggest that the control method proposed in this research can significantly improve autonomous vehicle control performance and their reliability, thereby contributing to the advancement of autonomous driving technology.

The conventional Simultaneous Localization and Mapping (SLAM) algorithm assumes a static world, which is easily influenced by dynamic elements of the surrounding environment. For high-precision localization in dynamic scenes, a dynamic SLAM algorithm combining instance segmentation and dynamic feature point filtering is proposed to address this issue. Initially, YOLACT-dyna, a one-stage instance segmentation network, was developed in order to perform instance segmentation on the input image, eliminate potential moving objects in the scene, and estimate the camera pose roughly. Second, based on the camera pose and polar constraint, the motion probability of each possible moving object was computed. Finally, the moving feature points were filtered out, and the static feature points were used to calculate the pose. The experimental results reveal that this algorithm’s recall rate in the dynamic regional KITTI dataset was 94.5% in public datasets. Accuracy is enhanced in environments with dynamic object location. At the same time, it can guarantee the positioning accuracy of a static scene, effectively enhancing the visual SLAM system’s position precision and robustness in a dynamic environment. It can meet the requirements of the automatic driving system’s real-time operation.

The Automated Valet Parking (AVP) system is a pivotal application of cyber-physical systems in autonomous driving, particularly for the initial and final segments of a journey. AVP system can reduce driver’s parking space cruising time and increase the capacity of parking lots. Traditional path planning approaches within AVP systems often struggle with long time for identifying target parking spaces and low efficiency in route planning, necessitating frequent adjustments. This study introduces an innovative path planning methodology that integrates high-definition mapping with loop closure detection to overcome these limitations. During parking, loop closure detection aligns the target parking space with a reference parking space from the database, streamlining the completion of parking maneuvers using established pathways. Taking parallel parking scenarios as an example, field experiments have shown that our method reduces the path length by 23.07% compared to HB-RRT, decreases the frequency of frequent adjustments by 40.0%, and improves vehicle positioning and attitude accuracy by 5.26% -10.52%. By rapidly pinpointing target spaces and calculating the most direct routes, this method enhances both parking efficiency and vehicle attitude accuracy compared to conventional methods.