AB  - 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.
AU  - Saleh, K
AU  - Grigorev, A
AU  - Mihaita, A-S
DA  - 2025/11/21
DO  - 10.1109/itsc60802.2025.11423617
EP  - 3087
JO  - 2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)
PB  - Institute of Electrical and Electronics Engineers (IEEE)
PY  - 2025/11/21
SP  - 3082
TI  - Cyclist Near-Miss Detection Using Lightweight Deep Temporal Neural Networks
VL  - 00
Y1  - 2025/11/21
Y2  - 2026/05/01
ER  -