Intention-Driven Trajectory Prediction for Autonomous Driving

Trajectory prediction has received much attention recently, especially in autonomous driving. Many Proposed models generate multi-modal trajectories using a wide variety of frameworks for context representation and dynamic interaction modeling. But they can not estimate the intention of the target vehicle, and the predicted trajectories are not explicable. Towards this end, we propose a model to estimate the vehicle intention and predict the possible trajectories corresponding to the intention. We separate intentions to long-term intention indicating the future path of vehicle and short-term intention indicating the behavior of vehicle. We use rasterized map to represent context information and differentiate the long-term intentions into different channels. A multi encoder-decoder module generates forecasting trajectories based on the context feature of specific intention and learns various behaviors considering the interaction of surrounding obstacles. We demonstrate the performance of our model on the nuScenes prediction dataset, which outperforms the state-of-the-art methods.