A Driving Intention Prediction Method Considering Vehicle Interaction

Abstract:

Accurate intention prediction can help intelligent vehicles better understand the environment and make safe decisions， thus improving the safety of automatic driving and promoting cooperative driving. This paper proposes an interactive intention prediction method， which makes a more accurate prediction of the driver's future intention. First， the Hidden Markov Model （HMM） and Gaussian Mixture Model （GMM） are combined to establish a behavior recognition model， which fully considers the surrounding scene information and accurately judges the current driving behavior. Then， an intention-based trajectory prediction method is proposed to plan the best driving trajectory considering the complex and changeable characteristics of the traffic scene， and the maximum expected utility theory is used to infer the future driving behavior. The behavior recognition and intention reasoning models comprehensively consider the evolution process of the traffic situations and the interaction between vehicles， and the final predicted driving intention of vehicles is obtained by combining the results of the above two models. Finally， the proposed method is verified in the NGSIM dataset， and the results show that the proposed behavior recognition model can recognize the lane changing intention of the vehicle 0.2 to 0.3 seconds in advance. Combined with the future intention reasoning model， it can more accurately predict the future driving behavior of the vehicle and improve driving safety.