Research On Hidden Markov Model Recognition Method For Vehicle Driving Tasks

After decades of development,autonomous vehicle is an important research direction in the automotive industry at home and abroad.The intellectualization of autonomous vehicle requires that the vehicle can have human-like behavior,and can meet the psychological needs of people in decision-making strategies.During the driving process,people will determine traffic-level driving tasks based on their own understanding of traffic rules and road structure,and adopt different decision-making strategies for different driving tasks.The recognition of traffic-level driving tasks can help automatically obtaining driving process data for each driving task during the driving process of a vehicle,which is the premise of studying the characteristics of human decision-making behavior under each driving task.Aiming at the recognition research of driving tasks,at present,the sliding time window method is mostly used for the recognition of a vehicle driving process by sliding interception.However,the vehicle driving process is random,and the completion time of different driving tasks is not the same.The selection of window size affects the results of driving task recognition.In order to solve the above problems,this research will focus on the segmentation method of vehicle driving process and the recognition method of driving tasks.The specific research contents are as follows:First,the recognition model for driving tasks is established.Firstly,based on the motion characteristics of vehicles completing different driving tasks and the similarity between driving task recognition and speech recognition,a hidden Markov model(HMM)is determined as the recognition model for driving tasks.Secondly,a hidden Markov model for each driving task is established to form the template library for driving task recognition as a matching basis for driving task recognition.According to the template library,the recognition of six driving tasks including turning left,turning right,lane changing of left,lane changing of right,lane keeping,and U-turn is completed.Secondly,a travel process segmentation method based on task characteristics is proposed.Aiming at the problems of signal interception methods using sliding time windows commonly used in current driving task recognition research,this paper proposes a driving process segmentation method based on task characteristics.The curvature information of vehicle motion trajectory reflects the structural information of the road to a certain extent,so this method divides the driving process into multiple driving segments based on the curvature change characteristics of different driving tasks,and each driving segment is an independent driving task to be identified.The recognition of each driving segment is accomplished using the driving task recognition model.This paper conducts a random driving simulator simulation test,collecting test data for model training and system verification.For each individual driving task,the testers complete multiple driving tests.The parameter training of the driving task recognition model is completed using test data,and the accuracy of the HMM recognition model is tested.The results show that the recognition accuracy of the recognition model established in this paper for driving process of each independent driving task can reach 98%.Through the verification of a driving task recognition method based on task characteristics segmentation,this method can segment each driving task with an accuracy rate of more than 88.9%,effectively segment a long time free driving process,and accurately identify the driving tasks of each driving segment.