Trajectory Prediction for Autonomous Driving Using a Transformer Network
CoRR(2024)
Abstract
Predicting the trajectories of surrounding agents is still considered one of
the most challenging tasks for autonomous driving. In this paper, we introduce
a multi-modal trajectory prediction framework based on the transformer network.
The semantic maps of each agent are used as inputs to convolutional networks to
automatically derive relevant contextual information. A novel auxiliary loss
that penalizes unfeasible off-road predictions is also proposed in this study.
Experiments on the Lyft l5kit dataset show that the proposed model achieves
state-of-the-art performance, substantially improving the accuracy and
feasibility of the prediction outcomes.
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