Hierarchical Spatio-Temporal Attention Network with Adaptive Risk-Aware Decision for Forward Collision Warning in Complex Scenarios

2025-11-25

AI Summary

To address the inherent trade-off among modeling accuracy, real-time performance, and alert reliability in forward collision warning (FCW) systems under complex traffic scenarios with multi-agent interactions, this paper proposes an integrated framework combining a hierarchical spatiotemporal attention network with dynamic risk-threshold adaptation. Methodologically, we design a decoupled architecture integrating graph attention and cascaded GRU–self-attention modules; quantify prediction uncertainty via conformal quantile regression; and incorporate a physics-inspired risk potential function coupled with a statistical process control–driven adaptive thresholding mechanism. Evaluated on multi-scenario datasets including NGSIM, our approach achieves a 91.3% prediction interval coverage rate, an 8.2% false alarm rate, an F1 score of 0.912, and a mean lead time of 2.8 seconds, while maintaining an inference latency of only 12.3 ms—substantially outperforming state-of-the-art methods.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Forward Collision Warning systems are crucial for vehicle safety and autonomous driving, yet current methods often fail to balance precise multi-agent interaction modeling with real-time decision adaptability, evidenced by the high computational cost for edge deployment and the unreliability stemming from simplified interaction models.To overcome these dual challenges-computational complexity and modeling insufficiency-along with the high false alarm rates of traditional static-threshold warnings, this paper introduces an integrated FCW framework that pairs a Hierarchical Spatio-Temporal Attention Network with a Dynamic Risk Threshold Adjustment algorithm. HSTAN employs a decoupled architecture (Graph Attention Network for spatial, cascaded GRU with self-attention for temporal) to achieve superior performance and efficiency, requiring only 12.3 ms inference time (73% faster than Transformer methods) and reducing the Average Displacement Error (ADE) to 0.73m (42.2% better than Social_LSTM) on the NGSIM dataset. Furthermore, Conformalized Quantile Regression enhances reliability by generating prediction intervals (91.3% coverage at 90% confidence), which the DTRA module then converts into timely warnings via a physics-informed risk potential function and an adaptive threshold mechanism inspired by statistical process control.Tested across multi-scenario datasets, the complete system demonstrates high efficacy, achieving an F1 score of 0.912, a low false alarm rate of 8.2%, and an ample warning lead time of 2.8 seconds, validating the framework's superior performance and practical deployment feasibility in complex environments.

Problem

• Balancing multi-agent interaction modeling with real-time decision adaptability
• Reducing computational complexity and false alarm rates in collision warnings
• Enhancing prediction reliability and warning timeliness in complex scenarios

Innovation

• Hierarchical Spatio-Temporal Attention Network for interaction modeling
• Dynamic Risk Threshold Adjustment algorithm for warnings
• Conformalized Quantile Regression for prediction intervals