发表机构
Zhejiang University; Beihang University（浙江大学; 北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出NSF-HRPT框架，结合神经语义场与分层风险感知树，引入Sim2Real策略，实现单目视觉输入下安全关键场景的高效风险评估，在合成与现实数据集上均取得优异性能。
AI 中文摘要
准确评估和预判安全关键场景中的风险的能力对自动驾驶系统至关重要。现有研究在碰撞预测方面已取得进展，但由于多智能体交互的复杂动态性以及现实环境中固有的不确定性，从单目视觉输入中准确量化风险水平仍然具有挑战性。为解决这些挑战，我们提出了NSF-HRPT，这是一种结合基于学习的感知与结构化推理的定量风险评估新框架。我们的方法包含神经语义场（NSF），该模型从模拟数据中学习建模场景语义、轨迹预测以及概率碰撞时间（TTC）分布。推理阶段，预训练的NSF作为分层风险感知树（HRPT）的先验，实现了多智能体风险的高效并行计算与空间推理。此外，我们引入了Sim2Real增强策略，通过融入基础模型的先验，无需重新训练即可提升现实适用性。大量评估表明，我们的框架在合成基准上达到了最先进的性能，在TTC估计准确率和风险定位精度方面，在现实世界数据集上也取得了具有竞争力的、接近最先进的结果。该方法为从单目相机输入中实现实时风险感知提供了有效解决方案。
英文摘要
The ability to accurately assess and anticipate risks in safety-critical scenarios is crucial for autonomous driving systems. While existing research has made progress in collision prediction, accurately quantifying risk levels from monocular vision inputs remains challenging due to the complex dynamics of multi-agent interactions and the inherent uncertainty in real-world environments. To address these challenges, we present NSF-HRPT, a novel framework that combines learning-based perception with structured reasoning for quantitative risk assessment. Our approach features a Neural Semantic Field (NSF) that learns to model scene semantics, trajectory predictions, and probabilistic Time-to-Collision (TTC) distributions from simulation data. During inference, the pre-trained NSF serves as a prior for our Hierarchical Risk Perception Tree (HRPT), which enables efficient parallel computation and spatial reasoning about multi-agent risks. Additionally, we introduce a Sim2Real enhancement strategy that improves real-world applicability without retraining by incorporating priors from foundation models. Extensive evaluations demonstrate that our framework achieves state-of-the-art performance on synthetic benchmarks and delivers competitive, near-state-of-the-art results on real-world datasets for both TTC estimation accuracy and risk localization precision. The proposed method provides an effective solution for real-time risk awareness from monocular camera inputs.
Comments13 pages, 5 figures
