视觉预测作为避障的中层表征
Visual Forecasting as a Mid-level Representation for Avoidance
AI总结：
本文提出以视觉预测作为中层表征，通过边界框序列和增强路径呈现动态物体未来轨迹，并在 Unity 仿真与真实场景中验证其导航避障效果。
摘要：
在包含动态物体的环境中进行导航，这一挑战仍然是自主智能体研究中的核心问题。尽管预测方法具有良好前景，但它们依赖精确的状态信息，因此在现实世界部署中实用性不足。本研究提出视觉预测，作为一种创新的替代方案。通过引入直观的视觉线索，该方法投影动态物体的未来轨迹，以增强智能体感知并使其能够采取预期性行动。我们的研究探索了通过视觉预测传递预测信息的两种不同策略：（1）边界框序列，以及（2）增强路径。为验证所提出的视觉预测策略，我们首先在使用 Unity 引擎的仿真环境中开展评估，随后将评估扩展到真实世界场景，以检验其实用性和有效性。结果证实，视觉预测是动态环境中导航与避障的一种可行且富有前景的解决方案。
英文摘要：
The challenge of navigation in environments with dynamic objects continues to be a central issue in the study of autonomous agents. While predictive methods hold promise, their reliance on precise state information makes them less practical for real-world implementation. This study presents visual forecasting as an innovative alternative. By introducing intuitive visual cues, this approach projects the future trajectories of dynamic objects to improve agent perception and enable anticipatory actions. Our research explores two distinct strategies for conveying predictive information through visual forecasting: (1) sequences of bounding boxes, and (2) augmented paths. To validate the proposed visual forecasting strategies, we initiate evaluations in simulated environments using the Unity engine and then extend these evaluations to real-world scenarios to assess both practicality and effectiveness. The results confirm the viability of visual forecasting as a promising solution for navigation and obstacle avoidance in dynamic environments.
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。
