Research On Environment Perception For Autonomous Vehicles

With the the development of computer electronics and autocontrol technology, intelligent moving systems are more and more widely applied to military affairs, civil utilizes, scientific fields and so on. As one of the 20th-century great inventions, autonomous vehicles are also becoming the research object for strategic high techniques all over the world. Meanwhile, they have promoted the development of many subjects such as Pattern Recognition, Intelligent Systems Integration, Sensors Fusion. As one of the key orientation, Computer Vision based road surroundings apperceiving technology is the challenging problem for intelligent technologies as well. Although many efforts have been put on Autonomous-Vehicle technology by lots of researchers, there remain some questions unsolved. The main reason is due to the increasing complexity and interfering factors for the environment. In terms of the above situation, deep research and study are carried out for many different road circumstances in this thesis, including Diffused Region of Hough method, Shape-Modeling FCM on Unstructured Roads, New Approach to Road Detection Based on Particle Swarm Optimization, and Vision Dynamic Modeling based on Road Tracking and Detection. They are introduced in details as follows:According to the characteristics of structured road environment, a Diffused Region based Hough Algorithm at global information level is proposed. As for the interfering factors of the road surroundings, the method incorporates road edge modality hint and general assistant information for the diffused region in Hough space. Meantime, Orientation estimation from other sensors and knowledge from previous experience could be utilized to decrease the time and computation complexity in experiments. Experiments specify the favorable performance on road edge identification and the effective immunity to interference with regard to structured and unstructured circumstances.Concerning the unstructured environment understanding for different roads, our thesis introduces a SMFCM (Shape-Modeling FCM) algorithm, which combines road geometry feature for the sampled images. This algorithm structures shape trait based membership matrix, which improved as shape-modeling fuzzy clustering algorithm. Besides, some parameters of the SMFCM are connected with the road scenes. Through experiments on many road images with more interference, this method is proved as preferable performance on unstructured roads and the promising value in actual applications.To get more accurate detection and understanding on road edges, and reduce the bad effect caused by shadows, blurred edge features, etc., a novel method is put forward. This algorithm is based on line deformable model, and defines a posteriori likelihood matching function with Priori information. Particle Swarm Optimization (PSO) is also applied to search the optimal solution in feature space. Furthermore, a friendly region associated with images is defined to decrease computation difficulties and other interfering factors. Experiments show the satisfactory impression for edge or lane recognition, and the ability to avoid the bad infection led by noise.Vision Dynamic Modeling based road tracking and identification mainly integrates the relation of road observation, and our thesis introduces a new algorithm with the continuity characteristic of object motion. Firstly, under the hypothesis of road edges being parallel, line deformable model is used to depict the road structure and road recognition is viewed as the problem of posterior likelihood probability all the same. Secondly, observation model for the camera is utilized to construct the relation between the vehicle state and model parameters in the image plane. Meanwhile, dynamic model for motion state is established in base of the vehicle environment. During the process of the algorithm, due to the non-concavity of the posterior likelihood function, PSO could be used to estimate the model parameters for the sampled first frame image, of which the results are employed as the initialization conditions for the motion model. With the non-linear and non-Gaussian merits, Particle Filtering (PF) is applied to reckon the road shape and vehicle state recursively. This method is proved to settle efficiently the road recognition under the condition of faint edge feature and discontinuous lanes, etc.