用单模型自回归预测轨迹，提升自动驾驶的未来行为预判能力。
DONUT: A Decoder-Only Model for Trajectory Prediction
- 采用自回归解码器模型统一处理历史轨迹编码与未来预测
- 在Argoverse 2数据集上达到新最好性能，优于传统编码器-解码器结构
- 引入超前预测任务增强模型对未来长期轨迹的感知能力
预测场景中其他智能体的运动对自动驾驶至关重要，有助于车辆提前预判。受语言建模中解码器仅模型成功的启发，我们提出DONUT——一种用于轨迹展开的解码器仅网络。与现有编码器-解码器预测模型不同，该模型通过单一自回归模型同时编码历史轨迹并预测未来轨迹，实现一致的迭代预测，并始终基于最新信息进行推断，从而提升性能。此外，借鉴语言建模中的多标记预测思想，我们引入‘超预测’策略，让模型额外学习更长时序范围的轨迹预测任务。这使模型能更好地预判未来，进一步提升表现。实验表明，我们的解码器仅方法超越编码器-解码器基线，在Argoverse 2单智能体轨迹预测基准上取得新最优结果。
原文摘要 · Abstract (English)
Predicting the motion of other agents in a scene is highly relevant for autonomous driving, as it allows a self-driving car to anticipate. Inspired by the success of decoder-only models for language modeling, we propose DONUT, a Decoder-Only Network for Unrolling Trajectories. Unlike existing encoder-decoder forecasting models, we encode historical trajectories and predict future trajectories with a single autoregressive model. This allows the model to make iterative predictions in a consistent manner, and ensures that the model is always provided with up-to-date information, thereby enhancing performance. Furthermore, inspired by multi-token prediction for language modeling, we introduce an 'overprediction' strategy that gives the model the auxiliary task of predicting trajectories at longer temporal horizons. This allows the model to better anticipate the future and further improves performance. Through experiments, we demonstrate that our decoder-only approach outperforms the encoder-decoder baseline, and achieves new state-of-the-art results on the Argoverse 2 single-agent motion forecasting benchmark.
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