Cyclist Near-Miss Detection Using Lightweight Deep Temporal Neural Networks
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Conference Proceeding
- Citation:
- 2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), 2025, 00, pp. 3082-3087
- Issue Date:
- 2025-11-21
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Near-miss incidents, where cyclists narrowly avoid collisions, are critical for understanding and improving urban cycling safety but are often underreported in official statistics. This paper introduces XceptionCycle, a lightweight and efficient deep neural network designed to detect cyclist near-miss incidents using time-series data from Inertial Measurement Units (IMUs) and GPS sensors. Building on the XceptionTime architecture, our model incorporates inverted bottlenecks and multi-scale depth-wise separable convolutions to extract rich temporal features while maintaining a low computational footprint. We benchmark XceptionCycle on the large-scale SimRa dataset, demonstrating superior discriminative performance with an 81.99% Area Under the ROC Curve and strong robustness with a 48.33% Matthews correlation coefficient, outperforming state-of-the-art models, while requiring less than half the number of trainable parameters. These results highlight XceptionCycle's potential for real-time near-miss detection and its suitability for resource-constrained environments such as mobile safety applications.
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