With the continuous increase in motor vehicle ownership in recent times, traditional transportation has been unable to meet people’s travel needs. Research on autonomous driving technology will help solve a series of problems associated with driving, such as traffic accidents, traffic congestion, energy consumption, and environmental pollution. In this paper, an improved artificial potential field method is proposed to complete the planning of automatic driving trajectories by adding the distance adjustment factor, dynamic road repulsive field, velocity repulsive field, and acceleration repulsive field. The invasive weed algorithm is introduced to solve the defects associated with the traditional artificial potential field method. The prediction model—for which corresponding constraint variables were set and an optimal objective function was established to build up the MPC model controller to achieve the goal of trajectory tracking—was linearized and discretized from a vehicle dynamics model. Finally, co-simulation based on MATLAB and CarSim was used to verify the practicability of the model.

To improve the utilization rate of parking space resources and reduce the cost of installing and maintaining sensor recognition, this paper proposed an improved computer vision-based parking space status recognition method. The overall recognition accuracy was improved by graying the video, filtering smoothing noise reduction, image enhancement pre-processing, introducing texture feature extraction method based on LBP operator, improving the background difference method, and then, we used a perceptual hash algorithm to calculate the Hamming distance between the background image and the hash string of the current frame of the video, excluding the influence of light and pedestrian on recognition accuracy. Finally, a parking space status recognition system is developed relied on the Python environment, and parking spaces are recognized in three environmental states: with direct light, without direct light, and in rain and snow. The overall average accuracy of the experimental results was 97.2%, which verifies the accuracy of the model.