Surrounding vehicle behavior adaptive correction prediction method based on driving prediction field

Abstract

The invention discloses a surrounding vehicle behavior adaptive correction prediction method based on a driving prediction field, which comprises the steps of: S1: carrying out surrounding vehicle behavior discretization and data set preprocessing, i.e., partitioning surrounding vehicle behaviors into N typical behaviors according to a transverse direction and a longitudinal direction; S2: acquiring traffic environment participation vehicle time series data, i.e., enabling each traffic environment participation vehicle to acquire a position, a speed and an acceleration of the vehicle at each moment in real time by using a positioning system; S3: establishing the driving prediction field, i.e., establishing the driving prediction field EP based on three elements of safety, efficiency and driving comfort, wherein EP=ES+EE+EC; S4: establishing a surrounding vehicle behavior prediction model on the basis of a maximum likelihood estimation method; and S5: carrying out surrounding vehicle behavior real-time prediction and model adaptive correction. According to the invention, safety, efficiency and driving comfort which influence driver behaviors are comprehensively considered; the driving prediction field is established in a driving region of a target vehicle and qualitative and quantitative analysis is carried out; and a new idea is proposed for surrounding vehicle behavior prediction.

Description

Technical field [0001] The invention belongs to the technical field of intelligent driving, and specifically relates to an adaptive correction prediction method of surrounding vehicle behavior based on a driving prediction field. Background technique [0002] Nowadays, both advanced driver assistance systems and fully self-driving vehicles have aroused extensive research interest from scholars in various fields. There is no doubt that automobile intelligence has become one of the most important trends and trends in the development of the automobile industry. The reason is that smart vehicles not only have more efficient, safer and cleaner performance in the transportation system, but they can also release human manipulation of the vehicle during driving. The real traffic environment is often complex and highly uncertain. In this environment, humans are actually very good drivers, because people can infer the behavior intentions of surrounding traffic participants and predict thei...

AI Technical Summary

Problems solved by technology

Another feasible solution is to directly predict the behavior through the prototype trajectory of the surrounding target vehicle, which can obtain higher computational efficiency, but this type of method regards the predicted target vehicle as an independent individual when performing motion prediction. It is difficult to carry out stable and accurate long-term motion behavior prediction in the traffic environment, because no matter Whether it is a human-driven traffic vehicle or a traffic vehicle with autonomous driving capabilities, it is an intelligent body that responds to the surrounding environment.

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