RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understanding
This addresses the challenge of robust perception for autonomous vehicles by improving generalization across domains, though it is incremental as it builds on existing graph learning approaches.
The paper tackles the problem of capturing interactions among road users for autonomous vehicles by proposing RS2G, a data-driven graph extraction and modeling framework that dynamically captures diverse relations, outperforming SOTA rule-based methods by 4.47% and deep learning models by 22.19% in risk assessment and showing better transfer to real-world scenarios.
Effectively capturing intricate interactions among road users is of critical importance to achieving safe navigation for autonomous vehicles. While graph learning (GL) has emerged as a promising approach to tackle this challenge, existing GL models rely on predefined domain-specific graph extraction rules that often fail in real-world drastically changing scenarios. Additionally, these graph extraction rules severely impede the capability of existing GL methods to generalize knowledge across domains. To address this issue, we propose RoadScene2Graph (RS2G), an innovative autonomous scenario understanding framework with a novel data-driven graph extraction and modeling approach that dynamically captures the diverse relations among road users. Our evaluations demonstrate that on average RS2G outperforms the state-of-the-art (SOTA) rule-based graph extraction method by 4.47% and the SOTA deep learning model by 22.19% in subjective risk assessment. More importantly, RS2G delivers notably better performance in transferring knowledge gained from simulation environments to unseen real-world scenarios.