Deep Learning for Accident Detection in Micro-Mobility Systems : Autoencoder-Based Anomaly Detection Using IMU Data and Temporal Features

Sammanfattning

Micro-mobility services, such as shared electric scooters and bikes, have revolutionized urban transportation systems worldwide. While these services offer convenient and sustainable mobility, their rapid adoption has raised safety concerns, particularly regarding user accidents. Reliable accident detection is crucial to address this concern and improve user safety. This thesis examines the use of deep learning–based anomaly detection techniques to identify accidents in micro-mobility data, with a particular emphasis on autoencoders. It explores whether incorporating the temporal characteristics of data collected from Inertial Measurement Units (IMUs), which monitor motion metrics such as acceleration and angular velocity, can enhance the performance of accident detection methods. Furthermore, the thesis compares the effectiveness of these advanced methods to a traditional threshold-based approach. The research findings demonstrate that autoencoder-based methods outperform the simpler threshold-based method, highlighting the potential of deep learning methods over traditional, rule-based methods. However, the study found no significant improvement in accident detection when considering the temporal aspect of the data. This may be due to the limited testing of different hyperparameters and the small size of the accident data set, which constrained the analysis. The results suggest that further exploration of the architecture of the LSTM (Long Short-Term Memory) autoencoderbased method and the TCN (Temporal Convolutional Network) autoencoderbased method is required to determine whether these methods fail to capture temporal dependencies or if the data inherently lack such dependencies.