This paper presents a solution for the license plate recognition problem in residential community administrations in China. License plate images are pre-processed through gradation, middle value filters and edge detection. In the license plate localization module the number of edge points, the length of license plate area and the number of each line of edge points are used for localization. In the recognition module, the paper applies a statistical character method combined with a structure character method to obtain the characters. In addition, more models and template library for the characters which have less difference between each other are built. A character classifier is designed and a fuzzy recognition method is proposed based on the fuzzy decision-making method. Experiments show that the recognition accuracy rate is up to 92%.

In order to make Advanced Driver Assistance Systems (ADAS) work effectively, a driver intention recognition system is proposed. Continuous Hidden Markov Model is applied to recognize drivers' lane change maneuver. Subjects performed lane change maneuvers with driving simulator which simulated highway scenes, and various sensor data was collected simultaneously. A series of testings and comparisons were done to obtain the optimal model structure and feature set. Results show that, taking the steering wheel angel and the steering wheel angle velocity as the optimal observation signals, the accuracy can achieve up 80%.

The human-vehicle collaboration in automated vehicles is an effective transitional means to overcome the difficulty of rapidly transitioning to a highly automated level of intelligence. Furthermore, it can fully leverage the strengths of both drivers and autonomous driving systems, embodying a design philosophy of human-centered. Therefore, this paper provides a review and perspectives of the human-vehicle collaboration for automated vehicles. First, the concept, forms and methods of human-vehicle collaboration are reviewed. Then, a human-vehicle mutual trust collaboration framework based on complementary advantages of humans and vehicles and brain-like intelligence is proposed. Specifically, the framework focuses on driver behavior understanding and brain-like cognitive decision planning. After that, the methods of driver behavior understanding and brain-like cognitive decision planning are summarized. Finally, challenges and future works are analyzed to contribute the develop of understandable, trustable, and acceptable human-vehicle collaboration systems.