一种通过对象状态预测进行自我中心关键对象识别的两阶段框架
A Two-Stage Framework for Ego-Centric Key Object Identification via Object State Prediction
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中文总结 AI 辅助
提出一种两阶段框架，通过虚拟自我车辆表示和对象状态预测，结合时空推理，在自动驾驶中提升关键对象识别的准确性。
中文摘要 AI 辅助
本文提出了一种新颖的框架，旨在增强自动驾驶中的关键对象识别。现有方法主要侧重于独立检测对象或利用视觉关系，但未明确考虑自我车辆在确定对象重要性时的视角。为弥补这一不足，我们提出了一种结构化方法，该方法整合了虚拟自我车辆表示和模块化对象状态预测器，从而能够更准确地估计对象相对于自我车辆的行为。随后，我们的框架采用时空推理来优化关键对象识别，基于对象状态和相对空间信息而非仅依赖视觉关系来确定优先级。在真实世界驾驶数据集上的实验结果表明，我们的方法在复杂交通环境中准确检测关键对象方面具有有效性。
英文摘要
This paper presents a novel framework designed to enhance key object identification in autonomous driving. Existing methods primarily focus on either detecting objects independently or leveraging visual relationships, but they do not explicitly consider the ego vehicle's perspective in determining object importance. To address this gap, we propose a structured approach that integrates a virtual ego-vehicle representation and a modular object state predictor, enabling a more accurate estimation of object behaviors relative to the ego-vehicle. Subsequently, our framework employs spatial-temporal reasoning to refine key object identification, prioritizing objects based on their states and relative spatial information rather than relying solely on visual relationships. Experimental results on real-world driving datasets demonstrate the effectiveness of our approach in accurately detecting critical objects in complex traffic environments.
发表机构
- School of Computing and Information, University of Pittsburgh（匹兹堡大学计算与信息学院）
机构由 AI 辅助整理，请以论文原文为准。
