不重训模型，用加权平均法提升轨迹预测精度10%。
Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
- 直接组合多个预训练模型，用置信度加权平均融合输出。
- 在NuScenes和Argoverse上整体性能提升10%，长尾场景更显著。
- 无需微调，开源代码，适合追求高效部署的自动驾驶研究者。
本文研究了集成学习在城市环境中车辆多维轨迹预测这一回归问题中的应用。随着自动驾驶领域不断出现更大、更先进的预测模型，如何在无需昂贵重新训练的情况下融合这些大模型的优势成为一个重要开放挑战。我们发现，出乎意料地，仅通过简单的置信度加权平均方法，即可在不进行再训练或微调的前提下，有效结合多个最先进的深度学习模型，从而显著提升整体预测性能。实验表明，该方法相较最优单个模型，在长尾指标上性能提升达10%，且在NuScenes和Argoverse两个数据集上均实现全域分布上的改进。相关代码已开源。
原文摘要 · Abstract (English)
This work explores the application of ensemble modeling to the multidimensional regression problem of trajectory prediction for vehicles in urban environments. As newer and bigger state-of-the-art prediction models for autonomous driving continue to emerge, an important open challenge is the problem of how to combine the strengths of these big models without the need for costly re-training. We show how, perhaps surprisingly, combining state-of-the-art deep learning models out-of-the-box (without retraining or fine-tuning) with a simple confidence-weighted average method can enhance the overall prediction. Indeed, while combining trajectory prediction models is not straightforward, this simple approach enhances performance by 10% over the best prediction model, especially in the long-tailed metrics. We show that this performance improvement holds on both the NuScenes and Argoverse datasets, and that these improvements are made across the dataset distribution. The code for our work is open source.
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