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.

翻译：运动预测在自动驾驶系统中至关重要，用于预测周围智能体（如行人、车辆和交通信号）的未来轨迹。在端到端预测中，模型必须从传感器数据（摄像头或激光雷达）中联合检测场景中不同元素的位置与历史轨迹，并预测其未来位置。我们并未遵循当前通过从感知到预测的端到端训练来处理此任务的趋势，而是采用了模块化方法。基于近期的一项研究，我们分别构建并训练了检测、跟踪与预测模块。随后，我们仅通过连续的微调步骤来更好地整合这些模块，并减轻误差累积。我们的研究表明，这种简单而有效的方法显著提升了端到端预测基准的性能。因此，我们的解决方案在CVPR 2024自动驾驶研讨会（WAD）举办的Argoverse 2端到端预测挑战赛中排名第一，获得了63.82 mAPf的分数。我们的预测结果较去年的优胜者提升了+17.1分，较今年的亚军提升了+13.3分。这一卓越的预测性能可归因于我们的模块化范式，该范式整合了微调策略，并显著超越了端到端训练的对应方法。