Eyes on the Road, Mind Beyond Vision: Context-Aware Multi-modal Enhanced Risk Anticipation

2025-07-08

AI Summary

Traffic accident prediction remains challenging due to the difficulty of modeling driver cognition and dynamic road environments. To address this, we propose a human-centered dynamic risk perception model that integrates driving videos, textual context, and driver attention maps for fine-grained, early accident预警. Methodologically, we design an adaptive risk thresholding mechanism that jointly considers scene complexity and gaze entropy; construct a hierarchical multimodal fusion architecture incorporating geospatial contextual visual-language modules; employ a Bi-GRU to capture spatiotemporal dependencies; and introduce 3D spatial relation encoding with context-aware cross-modal alignment. Evaluated on benchmarks including DADA-2000, our approach achieves significant improvements: +1.8 seconds in average early warning lead time, a 23.6% reduction in false positive rate, and enhanced prediction accuracy and model interpretability.

Technology Category

• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Accurate accident anticipation remains challenging when driver cognition and dynamic road conditions are underrepresented in predictive models. In this paper, we propose CAMERA (Context-Aware Multi-modal Enhanced Risk Anticipation), a multi-modal framework integrating dashcam video, textual annotations, and driver attention maps for robust accident anticipation. Unlike existing methods that rely on static or environment-centric thresholds, CAMERA employs an adaptive mechanism guided by scene complexity and gaze entropy, reducing false alarms while maintaining high recall in dynamic, multi-agent traffic scenarios. A hierarchical fusion pipeline with Bi-GRU (Bidirectional GRU) captures spatio-temporal dependencies, while a Geo-Context Vision-Language module translates 3D spatial relationships into interpretable, human-centric alerts. Evaluations on the DADA-2000 and benchmarks show that CAMERA achieves state-of-the-art performance, improving accuracy and lead time. These results demonstrate the effectiveness of modeling driver attention, contextual description, and adaptive risk thresholds to enable more reliable accident anticipation.

Problem

• Improving accident anticipation with multi-modal data integration
• Reducing false alarms using adaptive scene complexity mechanisms
• Enhancing spatio-temporal dependency capture for reliable risk prediction

Innovation

• Multi-modal fusion with video, text, and attention maps
• Adaptive risk thresholds based on scene complexity
• Hierarchical Bi-GRU for spatio-temporal dependency capture