Method for generating a predictive model of vulnerable road user trajectories and predictive method

Abstract

The invention discloses a method for generating a vulnerable road user trajectory prediction model and a corresponding prediction method. Generating the trajectory prediction model includes the following steps: Step S1: Obtain a training set, which includes marked N+M frames of consecutive images in time; Step S2: From the continuous N+M frames of images in the training set, according to each target The tracking ID serial number of the true value screens the training samples, and the training samples of the same target with the same tracking ID serial number form a group; Step S3: Extract spatiotemporal multi-cue features to obtain the training input vector X j t+k and the training output vector Y j t+k ; Step S4: generate a model, and input the training vector X j t+k and the training output vector Y j t+k Input to a gated recurrent unit neural network with an adaptive two-parameter activation function, and model training with a pre-designed loss function to generate a trajectory prediction model for vulnerable road users.

Description

technical field [0001] The invention relates to the field of automatic driving, in particular to a method for generating a trajectory prediction model of vulnerable road users and a corresponding prediction method. Background technique [0002] Pedestrians and cyclists (including cyclists, electric vehicles, and motorcycles) in traffic scenes are Vulnerable Road Users (VRU for short). Predicting Trajectory Prediction of Vulnerable Road Users Around Intelligent Vehicles is one of the key technologies of intelligent transportation, and it is the basis for intelligent vehicles to perform trajectory planning and motion obstacle avoidance. [0003] The main research object of existing VRU trajectory prediction methods is pedestrians. Moreover, traditional pedestrian trajectory prediction methods only focus on the motion time clue of position change, ignoring appearance features. That is to say, the existing trajectory prediction models or methods do not take into account the mo...

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Problems solved by technology

Moreover, traditional pedestrian trajectory prediction methods only focus on the motion time clue of position change, ignoring appearance features

That is to say, the existing trajectory prediction models or methods do not consider the movement characteristics of different types of VRUs. For example, because the riding tool of the lower body of the cyclist is a rigid body, the randomness is less than that of the pedestrian, and the movement speed is faster.

In addition, the existing trajectory prediction models or methods do not consider the interconnection between VRU targets, and do not make full use of the temporal motion features and rectangular frame appearance features of VRU targets, as well as the shared contextual image features between different VRU targets.

Therefore, the accuracy of the existing VRU trajectory prediction model or method is poor, and it is not suitable for the trajectory prediction of vulnerable road users in complex and changeable scenarios

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