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[Abstract]:The intelligent of automobile is the key technology to deal with the problems such as safety, congestion and environmental protection in the development of automobile industry. It is also an inevitable trend of the development of automobile technology. As one of the key technologies of the intelligent vehicle, the track planning needs to carry out the accurate risk assessment of the planning state, and the running route and the speed of the vehicle are planned based on the planning state, thereby ensuring the running safety of the intelligent vehicle in the traffic environment. This requires that a reasonable prediction of the motion trajectory of the traffic vehicle must be made in the trajectory planning. However, in the case of intelligent vehicles, the future movement of the traffic vehicle is uncertain and has a certain randomness. Ignoring the uncertainty of traffic vehicle motion will result in a lack of accuracy in the risk assessment, which will affect the travel safety of the smart vehicle. Therefore, it is not only important to ignore the uncertainty of the movement, but also to obtain the accurate probability characteristic of the traffic vehicle. At the same time, the outcome of the risk assessment of certainty has also been unable to accurately reflect the safety of the planned state, and its safety can only be expressed in the form of collision probability. In order to improve the performance of the trajectory planning and to guarantee the running safety of the intelligent vehicle, the impact of the collision probability caused by the motion uncertainty of the traffic vehicle must be fully considered. The behavior-based motion model framework is an effective method for predicting the motion track of a traffic vehicle. But the driver's different driving styles have different modes of motion under the same driving behavior. If that difference is ignore, the probability characteristic of the prediction is not accurate enough. Therefore, in order to improve the accuracy of the prediction, it is necessary to establish a motion model of different modes and to realize the identification of the motion pattern. A classifier based on support vector machine is an effective method to solve the problem of identification. Traditional classifiers treat the input samples as individuals that are independent, and the results depend on the classifier's own performance and the current input samples. However, since it is difficult to acquire that internal parameters of the traffic vehicle and the driver state and the real-time data of the vehicle state by the on-vehicle sensor, the mode identification can only depend on the limited external sensing information. Therefore, it is difficult to ensure the accuracy of the single-classifier to the single-sample identification result. In the framework of the behavior-based motion model, the Gaussian process motion model is an effective method to describe the randomness of the motion of the automobile, and the motion model corresponding to the establishment of the different motion modes is the basis for realizing the track prediction of the traffic vehicle. However, the probability characteristic of the motion uncertainty of the traffic vehicle is not accurately characterized by the motion model, and the influence of the prior vector matching the real-time motion track on the probability of the prediction vector must be taken into consideration. And the motion pattern recognition only determines the motion model of the real-time track, the prior vector matched with the motion model is still unknown, and the solution to the problem in the prior research is rarely mentioned. The ability of fast search of random tree with probability completeness and fast finding feasible solution is an effective method to solve the problem of automobile track planning. The traditional research focuses on the problems such as kinematics, dynamics and real-time performance of the vehicle, and is less concerned with the influence of the traffic uncertainty of the traffic vehicles. Therefore, the traditional method generally expresses the security of the nodes in the search tree in the form of a logical judgment, and realizes the search and decision-making of the trajectory based on the method. And the uncertainty of the movement of the traffic vehicle makes the above-mentioned conditions no longer established. Therefore, even though the risk assessment of the planning state is accurately carried out by the collision probability, the planning mechanism of the traditional method can not effectively deal with the impact of the probability of the collision on the search process, so that the blindness in the processing of the uncertainty is certain, Thus it is difficult to guarantee the performance of the planning method and the running safety of the intelligent vehicle. In view of the deficiency in the current research, this paper studies the trajectory planning of the vehicle motion uncertainty, and the main contents are as follows: First, this paper presents a method for identifying the motion pattern of the traffic vehicle. Methods The recognition architecture based on the "a pair of" error correction output code was established, and the problem of identification of motion pattern was transformed into a number of two-class problems. Then, based on the comparative analysis, the probability estimation model is established and the estimation of the actual probability is completed with the minimum relative entropy as the optimization target, so that the identification of the samples is realized by the multi-classifier instead of the single classifier. A Bayesian inference model is established to reveal the relationship between the successive probability estimation results and the result of the final identification mode, so as to realize the identification of the motion mode with a variety of the substitute single samples. The experimental results of the method and the traditional method show that the method can effectively eliminate the error identification result caused by the single classifier and the single sample, thereby effectively improving the accuracy of the identification. Secondly, the paper presents a method of trajectory prediction based on the Gaussian process motion model. Firstly, the mode clustering of the motion track is completed and a motion model based on the Gaussian process is established. In the course of trajectory prediction, a priori vector calculation method based on the Markovian distance is proposed and the matching relation between the real-time motion trajectory and the motion model is effectively established. The trajectory prediction method based on conditional Gaussian distribution is then derived to obtain the probability characteristic of the future motion trail of the traffic vehicle. The experimental results show that the method can accurately calculate the dimension of the prior vector, so as to ensure the accuracy of the probability characteristic of the prediction. Thirdly, this paper puts forward a method of trajectory planning which takes into account the uncertainty. The method comprises the following steps: taking into account the sampling strategy of the vehicle running environment characteristic and the node distance measurement strategy taking into account the automobile motion characteristic. in order to consider the influence of the probability of the collision caused by the motion uncertainty of the traffic vehicle in the course of the trajectory planning, the method expresses the risk assessment of the planning state with the collision probability expression, and the model is the cost of the node in the search tree, So that the influence of the collision probability is taken into consideration in the planning mechanism. Based on this, the search tree node sort, the sampling node expansion, the target deflection expansion and the track evaluation and decision can be carried out, and the performance of the planning method under the uncertainty can be ensured. In the end, the simulation experiment under various working conditions is carried out. The single-step comparison experiment shows that the more accurate risk assessment can be realized by considering the trajectory planning of the uncertainty, so that the safer track can be determined; and the dynamic obstacle avoidance experiment shows that based on the accurate risk assessment, In the planning process, the search tree is expanded towards a more secure area, and the blindness and randomness of the planning under the uncertainty are effectively eliminated. Based on the key projects of the National Natural Science Foundation of China, a real-vehicle experimental platform is set up to verify the contents and methods of this paper. First, a design of a path following controller based on a single-point pre-scan and a research effort based on the speed of the pre-scan acceleration follow the design of the controller are performed. Secondly, in order to verify the real-vehicle experiment, the overall scheme design of a car as a real-vehicle platform is completed, including the design of the software and hardware architecture of the platform, the mechanical, the communication and the design of the power supply system, the final modification and the completion of the construction of the real-vehicle platform. Then, according to the real-vehicle experimental requirements, the platform parameters involved in the trajectory planning and track following control algorithm are estimated. Finally, the method involved in this paper is verified by the built platform. The experimental results show that the method can effectively improve the accuracy of the intelligent vehicle trajectory planning, and further ensure the running safety of the intelligent vehicle, and verify the effectiveness of the method described herein.
【學位授予單位】：吉林大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：U463.6

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