A Pedestrian Trajectory Prediction Method Combining Social Force Model and Kalman Filter

Abstract

The invention discloses a pedestrian trajectory prediction method that integrates a social force model and a Kalman filter. The Kalman filter is divided into two parts: time update and measurement update; Identify various parameters of the force model; use the estimated pedestrian trajectory obtained from the simulation in step 2, and calculate the position value of the pedestrian at the next moment according to the Kalman time update formula in step 1, and finally obtain the prior estimate value X (k|k‑1) ; Calculate the pedestrian's current position measurement value Z according to the Kalman measurement update formula k , combined with the prior estimate X (k|k‑1) Calculate the optimal estimated value; set the error threshold ψ, judge the error between the predicted position of the social force model and the optimal estimated value, and make corrections to complete the trajectory prediction work. It can have a more accurate predicted trajectory when pedestrians actively avoid, turn and walk in a straight line, and effectively reduce the error with the actual 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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