Intelligent Driving Data Recorder in Smartphone Using Deep Neural Network-Based Speedometer and Scene Understanding

This paper proposes a smartphone-based Driving Data Recorder (DDR). The proposed DDR has the functions of accurate speed estimation and intelligent traffic scene understanding. DDRs are used to store the relevant driving data to provide feedback on driver behavior for accident analysis, insurance issue, and so on. The conventional DDRs are standalone devices with multiple sensors. The current DDR products record many useless data or lose important information. On the other hand, the widely used smartphones already have the hardware conditions to replace the conventional DDR products. This paper proposes to develop the intelligent DDR in the smartphones. Considering the requirements of the DDRs, two functions are developed in this paper: motion sensor-based speedometer and vision sensor-based scene understanding. The proposed speedometer function adopts double-layered Long Short-Term Memory (LSTM) network as the model, which can estimate the vehicle speed directly from gyroscope and accelerometer of a smartphone. The scene understanding function can detect road facilities such as traffic lights, crosswalks, and stop lines. The driving data recorded in those areas are very important for analyzing driver behaviors. In the development of the scene understanding function, maintaining high detection accuracy with reduced computation cost is significant due to the limitation of smartphones' processing resources. This paper uses a lightweight architecture deep learning network to achieve the goal. The proposed system has been evaluated using the real traffic data. Speed estimation function only has 1.8 km/h of speed mean error. In addition, there is no accumulated error even for a long time driving. The evaluation of the scene understanding function indicates that the proposed method can provide a high-accuracy detection at 2 FPS, which is faster than the state-of-the-art method.