SIMMF：面向自动驾驶的语义感知交互式多智能体运动预测
SIMMF: Semantics-aware Interactive Multiagent Motion Forecasting for Autonomous Vehicle Driving
AI总结：
针对自动驾驶中多智能体运动预测忽视语义信息且距离剪枝不充分的问题，提出SIMMF方法，结合语义与空间信息，通过语义感知选择和注意力机制提取全局编码，生成运动策略，实现更准确和场景一致的预测。
摘要：
自动驾驶车辆需要对周围的多智能体（行人和车辆）进行运动预测，以做出最优的导航决策。现有方法侧重于利用这些智能体的位置和速度的技术，未能捕捉场景中的语义信息。此外，为了缓解与场景中智能体数量相关的计算复杂度增加，一些工作利用欧氏距离来剪除远距离的智能体。然而，仅基于距离的度量不足以选择相关的智能体并准确执行其预测。为了解决这些问题，我们提出了语义感知交互式多智能体运动预测（SIMMF）方法，以捕捉语义和空间信息，并最优地选择相关智能体进行运动预测。具体而言，我们通过实现从场景中语义感知地选择相关智能体，并将其通过注意力机制提取全局编码来实现这一点。这些编码连同智能体的局部信息，通过编码器获得时间相关的潜在变量，用于预测未来轨迹的运动策略。我们的结果表明，所提出的方法优于最先进的基线，并提供更准确和场景一致的预测。
英文摘要：
Autonomous vehicles require motion forecasting of their surrounding multiagents (pedestrians and vehicles) to make optimal decisions for navigation. The existing methods focus on techniques to utilize the positions and velocities of these agents and fail to capture semantic information from the scene. Moreover, to mitigate the increase in computational complexity associated with the number of agents in the scene, some works leverage Euclidean distance to prune far-away agents. However, distance-based metric alone is insufficient to select relevant agents and accurately perform their predictions. To resolve these issues, we propose the Semantics-aware Interactive Multiagent Motion Forecasting (SIMMF) method to capture semantics along with spatial information and optimally select relevant agents for motion prediction. Specifically, we achieve this by implementing a semantic-aware selection of relevant agents from the scene and passing them through an attention mechanism to extract global encodings. These encodings along with agents' local information, are passed through an encoder to obtain time-dependent latent variables for a motion policy predicting the future trajectories. Our results show that the proposed approach outperforms state-of-the-art baselines and provides more accurate and scene-consistent predictions.
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。
