A Vehicle Trajectory Prediction Method Based on Residual Attention Mechanism

Abstract

The invention discloses a vehicle trajectory prediction method based on the residual attention mechanism, which preprocesses the trajectory of the target vehicle and the surrounding vehicles as a model input, and calculates the relative distance between each surrounding vehicle and the target vehicle through the residual connected attention module. Reconfigure the interaction tensor according to the weight coefficient of the weight coefficient; combine the new interaction tensor and the encoding of the target car’s historical trajectory to extract the complete interaction feature, as the input of the decoder, and finally output the probability distribution of the future predicted trajectory through the fully connected layer ;Using the mean value of the probability distribution as the predicted value of the actual trajectory coordinates, calculate the root mean square error loss value of the model, backpropagate the error, update the parameters in the model through the Adam optimizer, train until the model loss value is the smallest, and test in the verification set and test It has good generalization ability on the set.

Description

technical field [0001] The invention belongs to the field of driving assistance systems, and specifically refers to a vehicle trajectory prediction method based on a residual attention mechanism. Background technique [0002] In recent years, with the public's attention to car safety issues, driver assistance systems have become more and more widely used in vehicles, and the lane departure warning system has been effective in reducing lane departure accidents caused by driving fatigue. However, the existing problem of false positives of lane departure warning has always existed, and the main problem is concentrated on the wrong prediction of the future trajectory of the vehicle. Therefore, improving the accuracy of the predicted trajectory of the vehicle in the future is directly related to solving the problem of false positives of lane departure warning. [0003] The current vehicle trajectory prediction technologies can be divided into trajectory prediction methods based o...

AI Technical Summary

Problems solved by technology

However, the existing problems about false alarms of lane departure warnings have always existed. The main problems focus on wrongly predicting the future trajectory of vehicles. Therefore, improving the accuracy of future vehicle trajectory predictions is directly related to solving the problem of false alarms of lane departure warnings

[0004] The current trajectory prediction method based on data learning not only uses the historical trajectory of the vehicle as the basis for the model to predict the trajectory, but also considers the impact of the interaction between vehicles on the future trajectory of the vehicle. For example, when the driver changes lanes to the left, he will observe and predict the left Whether the driving conditions of the coming vehicle behind the lane will affect the lane change of the own vehicle; construct the interaction tensor according to the road structure and vehicle trajectory, and extract the interaction features in the interaction tensor through the convolutional layer or the fully connected layer; but consider the interaction between vehicles When the trajectory prediction method extracts the interaction features between vehicles, it basically assigns the same weight to each vehicle without bias. In the real driving environment, the driver of the vehicle will only pay attention to some vehicles on the road, and the driver assigns different weights to different vehicles. The degree of attention is different; therefore, there will be a large difference between the unbiased interaction features extracted by assigning the same weight to each vehicle and the real interaction features in the road situation

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