Motion forecasting is crucial in autonomous driving systems to anticipate the
future trajectories of surrounding agents such as pedestrians, vehicles, and
traffic signals. In end-to-end forecasting, the model must jointly detect from
sensor data (cameras or LiDARs) the position and past trajectories of the
different elements of the scene and predict their future location. We depart
from the current trend of tackling this task via end-to-end training from
perception to forecasting and we use a modular approach instead. Following a
recent study, we individually build and train detection, tracking, and
forecasting modules. We then only use consecutive finetuning steps to integrate
the modules better and alleviate compounding errors. Our study reveals that
this simple yet effective approach significantly improves performance on the
end-to-end forecasting benchmark. Consequently, our solution ranks first in the
Argoverse 2 end-to-end Forecasting Challenge held at CVPR 2024 Workshop on
Autonomous Driving (WAD), with 63.82 mAPf. We surpass forecasting results by
+17.1 points over last year's winner and by +13.3 points over this year's
runner-up. This remarkable performance in forecasting can be explained by our
modular paradigm, which integrates finetuning strategies and significantly
outperforms the end-to-end-trained counterparts.

使用模块化方法，将感知、跟踪和预测模块单独建立并训练，然后通过连续的微调步骤整合这些模块以改善性能，在 Argoverse 2 的终端预测挑战中取得了显著的性能提升。

Valeo4Cast：一种模块化的端到端预测方法

Valeo4Cast: A Modular Approach to End-to-End Forecasting

Time series forecasting is a challenging task due to the existence of complex
and dynamic temporal dependencies. This can lead to incorrect predictions by
even the best forecasting models. Using more training data is one way to
improve the accuracy, but this source is often limited. In contrast, we are
building on successful denoising approaches for image generation by advocating
for an end-to-end forecasting and denoising paradigm.
We propose an end-to-end forecast-blur-denoise forecasting framework by
encouraging a division of labors between the forecasting and the denoising
models. The initial forecasting model is directed to focus on accurately
predicting the coarse-grained behavior, while the denoiser model focuses on
capturing the fine-grained behavior that is locally blurred by integrating a
Gaussian Process model. All three parts are interacting for the best end-to-end
performance. Our extensive experiments demonstrate that our proposed approach
is able to improve the forecasting accuracy of several state-of-the-art
forecasting models as well as several other denoising approaches.

通过在时间序列预测中引入端对端的预测和去噪范式，我们提出了一种端对端预测 - 模糊 - 去噪的预测框架，通过鼓励预测模型和去噪模型之间的分工，以提高预测的准确性。经过大量实验证明，我们的方法能够改善多种先进预测模型和去噪方法的预测准确性。