NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment
Yu Zhao, Jiangyu Pan, Tao Hu, Ming Yin, Fan Yang, Jiangfan Liu, Xiubo Liang
Why It Matters
What makes this one worth your time
This research addresses critical challenges in real-time risk assessment for autonomous systems, which is essential for improving safety in real-world driving scenarios.
NSF-HRPT combines neural perception and structured reasoning for enhanced risk assessment in autonomous driving.
Summary
The paper presents NSF-HRPT, a framework that integrates a Neural Semantic Field for scene understanding and a Hierarchical Risk Perception Tree for efficient risk assessment in autonomous driving, addressing challenges in risk quantification from monocular vision inputs.
Key contributions
- Development of the Neural Semantic Field for modeling scene semantics and trajectory predictions.
- Introduction of the Hierarchical Risk Perception Tree for efficient risk assessment.
- Demonstration of state-of-the-art performance on synthetic benchmarks and competitive results on real-world datasets.
Notable insights
- The integration of simulation data for training the Neural Semantic Field allows for better modeling of complex multi-agent interactions.
- The use of a Sim2Real enhancement strategy without retraining is a clever approach to improve real-world applicability.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2608.04776v1 Announce Type: new Abstract: 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.