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<p>Pedestrian crossing intention prediction is a critical task for autonomous driving that requires high accuracy and real-time inference capability. In India, this is particularly challenging in unstructured and crowded road environments where occlusions, vulnerable road users, and non-compliant pedestrian behavior are very common. Existing methods are primarily trained on non-Indian datasets which often struggles to generalize to such conditions and rely on static combinations of visual and non-visual features, leading to a high computational complexity. To address these limitations, we propose a hybrid CNN-GCN based network with a dynamic modality gating module that adaptively prioritizes visual (pose, local and global context) and non-visual (motion, bounding boxes) features depending on the scene context. This enables the model to emphasize reliable modalities under visual degradation while maintaining computational efficiency. In addition, a novel scale-aware HRNet based preprocessing module with adaptive scaling is introduced to robustly extract pedestrians pose key points for distant and partially occluded pedestrians. To support evaluation in unstructured traffic scenarios, a custom Indian Road Dataset (IRD) is collected in Trivandrum city, India covering urban and suburban roads to capture diverse traffic scenarios with dense crowds, non-compliant pedestrian behaviors, adverse weather and frequent occlusions. The proposed model achieves moderate accuracy of 88% on JAAD_All dataset and 82% on the IRD dataset, while maintaining the lower inference latency (1.5ms on IRD and 4.6 ms on JAAD_All) among compared methods achieving a favorable trade-off between predictive performance and computational efficiency. Extensive experiments including ablation studies on modality gating, attention mechanisms, and scale-aware preprocessing, along with cross-dataset generalization analysis(PIE dataset) and failure case evaluation demonstrate the effectiveness of the proposed approach. Further analysis under varying conditions including occlusion, pedestrian scale variations, and modality weight adaptation shows that the model dynamically adjusts feature reliance and retains consistent predictive capability in visually degraded scenarios. Additionally, deployment on the Jetson Xavier NX demonstrates near real-time performance, highlighting the practical feasibility of the proposed method for autonomous driving applications.</p>

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Hybrid CNN–GCN network with dynamic gating and scale-aware preprocessing for pedestrian crossing intention prediction in Indian road conditions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月16日

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  • Rajesh Kannan Megalingam,
  • Naveen Prasaad Selvarajan

摘要

Pedestrian crossing intention prediction is a critical task for autonomous driving that requires high accuracy and real-time inference capability. In India, this is particularly challenging in unstructured and crowded road environments where occlusions, vulnerable road users, and non-compliant pedestrian behavior are very common. Existing methods are primarily trained on non-Indian datasets which often struggles to generalize to such conditions and rely on static combinations of visual and non-visual features, leading to a high computational complexity. To address these limitations, we propose a hybrid CNN-GCN based network with a dynamic modality gating module that adaptively prioritizes visual (pose, local and global context) and non-visual (motion, bounding boxes) features depending on the scene context. This enables the model to emphasize reliable modalities under visual degradation while maintaining computational efficiency. In addition, a novel scale-aware HRNet based preprocessing module with adaptive scaling is introduced to robustly extract pedestrians pose key points for distant and partially occluded pedestrians. To support evaluation in unstructured traffic scenarios, a custom Indian Road Dataset (IRD) is collected in Trivandrum city, India covering urban and suburban roads to capture diverse traffic scenarios with dense crowds, non-compliant pedestrian behaviors, adverse weather and frequent occlusions. The proposed model achieves moderate accuracy of 88% on JAAD_All dataset and 82% on the IRD dataset, while maintaining the lower inference latency (1.5ms on IRD and 4.6 ms on JAAD_All) among compared methods achieving a favorable trade-off between predictive performance and computational efficiency. Extensive experiments including ablation studies on modality gating, attention mechanisms, and scale-aware preprocessing, along with cross-dataset generalization analysis(PIE dataset) and failure case evaluation demonstrate the effectiveness of the proposed approach. Further analysis under varying conditions including occlusion, pedestrian scale variations, and modality weight adaptation shows that the model dynamically adjusts feature reliance and retains consistent predictive capability in visually degraded scenarios. Additionally, deployment on the Jetson Xavier NX demonstrates near real-time performance, highlighting the practical feasibility of the proposed method for autonomous driving applications.