A pedestrian trajectory prediction method based on social force model and Kalman filter

Abstract

The invention discloses a pedestrian trajectory prediction method which integrates a social force model and a Kalman filter. The Kalman filter is divided into two parts: a time update part and a measurement update part. Adaptive mutation particle swarm optimization algorithm is used to identify the parameters of social force model by setting fitness function. The predicted pedestrian trajectory issimulated in the step 2, the pedestrian position value at the next time is calculated according to the Kalman time update formula in the step 1, and the prior estimate value shown in the descriptionis finally obtained; According to the updated formula of Kalman measurement, the measurement value Zk of pedestrian's current position is calculated, and the optimal estimation value is obtained by combining the prior estimation value shown in the description. The error threshold psi is set to judge the error between the predicted position of the social force model and the optimal estimated value,and the error is corrected to complete the trajectory prediction. It can predict the trajectory accurately when pedestrians take the initiative to avoid turning and walking in a straight line and effectively reduce the error between the actual trajectory and the predicted trajectory so as to meet the required prediction requirements.

Description

technical field [0001] The invention relates to a pedestrian trajectory prediction method, in particular to a pedestrian trajectory prediction method integrating a social force model and a Kalman filter. Background technique [0002] Pedestrian trajectory prediction is to estimate the pedestrian's position at the next moment or the trajectory of the pedestrian in the future based on the current movement information and historical data of pedestrians. Although human motion has great randomness, in a structured environment such as a station, human long-term motion is usually regular, manifested as a continuous trajectory connecting various entrances and exits. Pedestrian trajectory prediction has great practical value in unmanned driving, robot obstacle avoidance planning, and urban traffic management. [0003] Existing methods for pedestrian trajectory prediction are mainly divided into two categories: one is data-based modeling methods, such as human motion prediction algor...

AI Technical Summary

Problems solved by technology

[0004] Although there have been many achievements in pedestrian trajectory prediction, the main problem in the prediction process of existing methods is that pedestrians are regarded as general moving objects for trajectory tracking and prediction, without considering the relationship between pedestrians and pedestrians and pedestrians and obstacles. The role of the relationship

In a multi-pedestrian environment, due to the active perception of people, pedestrians will actively change the direction of movement to avoid collisions with other people or obstacles when moving towards the destination, and this sudden change in the direction of movement will lead to trajectory prediction results. big error

At present, there is no prediction method that considers the active obstacle avoidance of pedestrians

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