Vehicle trajectory prediction method based on global attention and state sharing

Abstract

The invention discloses a vehicle trajectory prediction method based on global attention and state sharing. The solution method comprises the following steps that a GAS-LED trajectory prediction model of a codec LSTM model with a global attention mechanism and state sharing is used; in the GAS-LED track prediction model, a state sharing mechanism with an encoder and a decoder is adopted to reduce the calculation workload, and meanwhile, two parallel calculation GAS-LED track prediction models are adopted to output prediction of the transverse lane changing behavior and the longitudinal driving distance of the vehicle in parallel; in the track prediction task of the lane level, the lane where the vehicle is located is focused on, and the GAS-LED track prediction model outputs corresponding prediction results for the transverse lane change and the longitudinal driving distance; and historical information of the current vehicle and the surrounding vehicles is used as the input of the GAS-LED trajectory prediction model 2, and then the two GAS-LED trajectory prediction models are used in parallel to obtain more output results convenient to predict. Through the scheme, the purpose of high-precision prediction is achieved, and the method has very high practical value and popularization value.

Description

technical field [0001] The invention belongs to the technical field of automatic driving, and in particular relates to a vehicle trajectory prediction method based on global attention and state sharing. Background technique [0002] Existing research on trajectory prediction can be roughly divided into two categories. They are rule-based and learning-based trajectory prediction algorithms, respectively. Rule-based forecasting algorithms simulate traffic flow models by applying traffic rules, while learning-based forecasting algorithms employ machine learning models (recently deep models) to make predictions based on historical trajectories of moving objects. [0003] Rule-based trajectory prediction: Rule-based trajectory prediction algorithms mainly apply traffic rules to simulate traffic flow models. For example, when there is no vehicle ahead on the right, the vehicle will move to the right lane; if there is no vehicle ahead, the vehicle will accelerate. The simulation...

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