With the development of autonomous driving technology, autonomous valet parking (AVP) has become a key technology to solve the problem of urban parking. Current commercial AVP systems generally adopt solutions based on semantic maps, which achieve high-precision parking in small-scale scenarios. However, when the parking environment is expanded to large underground parking lots, semantic and occupancy grid maps face bottleneck problems such as a sharp drop in path generation efficiency and delayed parking space retrieval response. In addition, traditional High-definition maps (HD maps) rely on manual annotation and complex post-processing. In response to the above challenges, this article proposes an efficient adaptive topology plan for AVP in large-scale occupancy map: first, a scale-adaptive index model based on the R-tree structure is constructed to achieve hierarchical storage and dynamic resolution selection of grid map data; secondly, a multi - scale feature fusion topology aware method is designed to generate the environment topology; finally, a multi-path parallel hybrid A${}^{\ast }$algorithm is proposed for efficient planning. A comparison of our framework with both traditional and state-of-the-art methods shows that the framework is capable of enhancing planning efficiency and reducing average path generation time in large parking lots. Through simulations and real-world tests, the method has been shown to reduce path search time whilst generating paths that are easier to track with less tracking error.

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 automation of agricultural vehicles represents a significant field of interest in the application of intelligent driving technologies. Agricultural vehicles operate in complex environments that require the cooperative movement of multiple units. Developing methods for effective cooperative planning and control of these vehicles is crucial for advancing autonomous driving in the agricultural sector. The technology of intelligent connected vehicles provides the fundamental condition for cooperative planning and control of multiple agricultural vehicles. Leveraging information from intelligent connected agricultural vehicles, this study introduces the concept of virtual lanes and proposes an innovative planning approach that integrates the graph method with the Artificial Potential Field (APF) method. The incorporated strategy aims to facilitate rapid and efficient cooperative planning of multiple vehicles operating within unstructured agricultural areas. Then, an Improved Nonlinear Model Predictive Control (INMPC) algorithm considering feedforward control is proposed, which improves the accuracy of tracking control and achieves precise cooperative movement of multiple vehicles. Simulation results indicate that the method proposed in this paper can quickly realize cooperative planning and control of multiple vehicles in complex agricultural areas, achieving platoon operation of multiple agricultural vehicles. This research can promote the application of intelligent connected vehicle technology in agricultural production and facilitate the development of agricultural automation.

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.

How to fully test the safety and functionality under different driving scenarios is a key issue for the development and application of autonomous vehicles. In this study, aimed at the test scenarios of autonomous vehicle, we propose a lidar-camera fusion approach for traffic environment sensing. Based on the successful Lift-Splat-Shoot (LSS) model, we propose a unique data enhancement strategy to develop the fusion accuracy. Through building a test dataset with the highprecision acquisition vehicle, the proposed method is verified that the new fusion authorism proposed in this paper can accurately distinguish the translation, scale, orientation and velocity of the target. This study can promote test scenario generation methods.

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.