Most prior motion prediction endeavors in autonomous driving have inadequately encoded future scenarios, leading to predictions that may fail to accurately capture the diverse movements of agents (e.g., vehicles or pedestrians). To address this, we propose FutureNet, which explicitly integrates initially predicted trajectories into the future scenario and further encodes these future contexts to enhance subsequent forecasting. Additionally, most previous motion forecasting works have focused on predicting independent futures for each agent. However, safe and smooth autonomous driving requires accurately predicting the diverse future behaviors of numerous surrounding agents jointly in complex dynamic environments. Given that all agents occupy certain potential travel spaces and possess lane driving priority, we propose Lane Occupancy Field (LOF), a new representation with lane semantics for motion forecasting in autonomous driving. LOF can simultaneously capture the joint probability distribution of all road participants' future spatial-temporal positions. Due to the high compatibility between lane occupancy field prediction and trajectory prediction, we propose a novel network with future context encoding for the joint prediction of these two tasks. Our approach ranks 1st on two large-scale motion forecasting benchmarks: Argoverse 1 and Argoverse 2.

翻译：先前大多数自动驾驶运动预测研究未能充分编码未来场景，导致预测结果可能无法准确捕捉智能体（如车辆或行人）的多样化运动。为解决此问题，我们提出FutureNet，该模型将初始预测轨迹显式整合至未来场景中，并进一步编码这些未来上下文以增强后续预测。此外，既往多数运动预测研究侧重于为每个智能体预测独立的未来状态。然而，安全流畅的自动驾驶需要在复杂动态环境中准确联合预测众多周围智能体的多样化未来行为。鉴于所有智能体均占据特定潜在行驶空间且具有车道行驶优先权，我们提出车道占用场——一种用于自动驾驶运动预测的、具有车道语义的新型表征方法。LOF能够同步捕捉所有道路参与者未来时空位置的联合概率分布。基于车道占用场预测与轨迹预测之间的高度兼容性，我们提出一种具有未来上下文编码功能的新型网络，用于联合完成这两项任务的预测。我们的方法在Argoverse 1和Argoverse 2两大运动预测基准测试中均位列榜首。