FutureNet-LOF：结合未来上下文编码的联合轨迹预测与车道占用场预测
FutureNet-LOF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding
AI总结：
针对自动驾驶运动预测中未来场景编码不足、各智能体预测独立的问题，提出带未来上下文编码的FutureNet及车道语义表示LOF，实现轨迹与占用场联合预测，在两大基准上登顶。
摘要：
自动驾驶领域中，大多数现有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。为解决这一问题，我们提出FutureNet，该模型将初始预测轨迹显式整合到未来场景中，并进一步编码这些未来上下文以增强后续预测。此外，以往大多数运动预测工作都聚焦于为每个智能体预测独立的未来。然而，安全顺畅的自动驾驶需要在复杂动态环境中联合准确预测众多周围智能体的多样化未来行为。考虑到所有智能体都会占据一定的潜在行驶空间且具有车道通行优先级，我们提出车道占用场（Lane Occupancy Field，LOF），这是一种用于自动驾驶运动预测的、具备车道语义的新型表示方法。LOF能够同时捕捉所有道路参与者未来时空位置的联合概率分布。由于车道占用场预测与轨迹预测之间具有高度兼容性，我们提出了一种具备未来上下文编码的新型网络，用于这两项任务的联合预测。我们的方法在两个大规模运动预测基准Argoverse 1和Argoverse 2上均排名第一。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
