Lane Changing Behavior Recognition of Other Vehicles Considering Driving Tendency

Abstract

Autonomous driving technology includes four links: perception, cognition, decision-making, and behaviour. The cognition plays a vital role as a bridge between perception and decision-making. In the cognition link, accurate identification of the intention and behaviour of surrounding vehicles is a necessary prerequisite for autonomous vehicles to make correct behaviour decisions, and it is conducive to reducing the driving risk of own vehicle. Different from driving style, driving tendency has the characteristics of dynamic and behavioural decision-making preference. Being able to identify the driving tendency of surrounding vehicles in real time will be more conducive to making reasonable behavioural decisions for own vehicle. Therefore, this paper takes the lane change trajectory data of passenger cars in the NGSIM open-source database as the research sample. Firstly, the problem of data imbalance is considered, and the imbalanced samples are sampled using the method of uniformly dividing the data set. Secondly, from the perspective of own vehicle, the characteristic indicators that can represent the tendency of the surrounding vehicles to change lanes are mined. Finally, the GRU deep learning algorithm is used to establish a real-time identification model for aggressive lane change behaviour. Compared with the traditional method, the method proposed in this paper can effectively identify the tendency of lane change behaviour of surrounding vehicles in the early stage of lane-changing behaviour and has higher recognition accuracy. The research results show that the real-time identification of the tendency of the lane change behaviour of the surrounding vehicles is beneficial to the assessment and quantification of the driving risk of own vehicle, and also provides a theoretical basis for the decision-making of own vehicle, which is of great significance to the safe driving of own vehicle.

Keywords: traffic safety, driving tendency, aggressive lane change, driving behaviour, behaviour recognition, deep learning

DOI: 10.54941/ahfe1003422