Arihant Jain supervised by Prof. Deepak Gangadharan received his Master of Science – Dual Degree in Electronics and Communication Engineering (ECD). Here’s a summary of his research work on Time-Series Deep Learning for Two-Wheeler Driving Event Recognition

Two-wheelers are among the most widely used modes of transportation in many regions of the world, particularly in developing countries. However, the lack of structural protection for riders makes them highly vulnerable to road accidents. Understanding and recognising driving events from vehicle motion data is therefore an important step toward developing intelligent safety systems, rider assistance technologies, and accident detection mechanisms. This thesis investigates time-series based deep learning approaches for twowheeler driving event recognition using inertial sensor data. A multivariate time-series dataset was collected using an embedded sensing platform consisting of a tri-axial accelerometer, gyroscope, and GPS module mounted on a motorcycle. Unlike many existing datasets, the collected data incorporates diverse riding conditions, including recordings from multiple riders, variations in riding behaviour, and both daytime and nighttime sessions, providing a more realistic representation of real-world riding dynamics. 

Several temporal deep learning architectures are evaluated for driving event classification, including Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), attention-enhanced recurrent networks, Temporal Convolutional Networks (TCN), and transformer-based models. To capture both short-term motion patterns and long-range temporal dependencies, a Time-Series Transformer (TST) architecture is explored and compared against these baseline models. All models are trained and evaluated using a consistent preprocessing pipeline and hyperparameter optimisation framework. Experimental results show that transformer-based temporal modelling achieves competitive or superior performance compared to recurrent and convolutional approaches, particularly for challenging manoeuvre-related events such as turns, stops, and bumps. To evaluate practical feasibility, the trained models are deployed on a Raspberry Pi edge platform, where inference latency, memory usage, and real-time processing capability are analysed. 

The results demonstrate that accurate driving event recognition can be achieved using lightweight temporal models operating directly on raw sensor streams, enabling the development of practical on-device safety monitoring Synopsis 1 systems for two-wheelers. 

June 2026