Vehicle track prediction method of two-dimensional intelligent driving model based on virtual boundary

Abstract

The invention discloses a vehicle trajectory prediction method of a two-dimensional intelligent driving model based on a virtual boundary, which is a method for describing complex traffic behaviors at ramps and accurately predicting vehicle trajectories in an environment of multi-lane road mixed traffic flow. According to the method, a topological structure of a road is considered to generate a virtual boundary line, and then state information (transverse and longitudinal coordinate positions, speeds and accelerated speeds) of a target vehicle (a vehicle needing track prediction) and surrounding vehicles at the current moment is acquired; then calculating relative effective distances and relative average speeds between the target vehicle and surrounding vehicles (in front, back, left and right directions), and distances between the target vehicle and the virtual boundary line; and then calculating the state information of the target vehicle at the next moment according to the two-dimensional intelligent driving model based on the virtual boundary, repeating the previous steps, continuously calculating the state information of the target vehicle at the next moment, and finally forming a complete vehicle track.

Description

technical field [0001] The present invention relates to a vehicle trajectory prediction method based on a two-dimensional intelligent driving model of a virtual boundary, which is a method for predicting vehicle trajectory in the environment of multi-lane road mixed traffic flow (consisting of ordinary manual driving vehicles and automatic driving vehicles). A method for describing complex traffic behavior on ramps and accurately predicting vehicle trajectories. Background technique [0002] In order to safely and efficiently navigate complex traffic composed of human drivers, self-driving cars need to have active capabilities, such as deciding when to change lanes, overtake, slow down or merge, which requires self-driving cars to be able to predict the future movement of surrounding vehicles make inferences. Predicting vehicle trajectories is beneficial to shorten the time for smart cars to make appropriate decisions. Accurate and reliable vehicle trajectory prediction can...

AI Technical Summary

Problems solved by technology

However, the current two-dimensional driving model is only suitable for homogeneous roads and homogeneous traffic flow, and cannot describe the complex heterogeneous traffic behavior at the ramp, and it is difficult to predict vehicle trajectories

Images