Deep Learning-Based Vehicle Speed Estimation Using Smartphone Sensors in GNSS-Denied Environment

This study presents a deep learning-based framework for vehicle speed estimation in GNSS-denied environments, such as underground parking lots, using only smartphone sensors. Accurate speed estimation in such environments is critical for enabling infrastructure-free indoor navigation, yet remains challenging due to sensor noise, orientation variability, and the lack of GNSS signals. The proposed model leverages accelerometer and gyroscope data without requiring transformation from the smartphone’s body frame to a global navigation frame, thereby simplifying the preprocessing pipeline. An LSTM network combined with an attention mechanism is employed to capture temporal dependencies in sequential sensor data. To improve estimation accuracy, statistical features are also incorporated. Training data were collected over a distance of 41.8 km using four smartphones in real-world parking lot environments. The model was further validated through controlled experiments under three test scenarios, including circular driving and repeated turning maneuvers. Across all scenarios, the proposed method achieved a mean RMSE of 0.38 m/s with consistent performance across different device orientations. These results demonstrate that the proposed approach achieves high speed estimation accuracy and robustness across various phone orientations, without the need for additional infrastructure. This highlights the potential of combining deep learning and smartphone sensing for reliable indoor navigation in challenging environments.