L Lakshmanan supervised by Dr. Aftab M Hussain received his  Master of Science –  Dual Degree in Electronics and Communication Engineering  (ECD). Here’s a summary of his research work on Multi-Modal Time-Series Data Collection for Predictive Analytics in Two-Wheeler Safety:

 With major advances in the field of Machine Learning, fully autonomous vehicles are becoming a reality. However, most research towards ML based approaches for autonomous driving and safety measures so far solely focus on four-wheelers. Two-wheelers are a primary mode of transport in many developing countries and are also very vulnerable in road accidents due to their open nature. Hence, effective safety measures for two-wheelers can potentially save millions of lives. In this thesis, we first look at the problem of driving event recognition which is the primary step towards incorporating prediction-based models in safety features. We note that there is no public dataset that we can use as a standard for this task, and hence design a hardware system to collect data. The hardware system is modular and can be attached to any vehicle to collect six-axis acceleration and velocity data at a high polling rate. We tested traditional Machine Learning (ML) models with the annotated dataset for accuracy and found them to be insufficient due to the temporal nature of the data. So, we propose employing Deep Learning (DL) models that are better suited to processing time-series data, such as LSTMs. We also integrate the attention mechanism with the LSTM models to improve their accuracy and performance on long sequences. A comprehensive evaluation of the proposed models reveal that the Bi-LSTM model with attention provides the best accuracy. However, we also investigated the viability of these models by quantizing and deploying them on a Raspberry Pi. Here, the regular LSTMmodel provides the best efficiency and inference time. However, all the models are viable and successfully run on a resource-constrained platform, thus indicating that there is scope for deployment of predictive models with inference on edge. Next, we introduce a scalable and modular platform for comprehensive two-wheeler riding data collection. The platform is equipped with multiple sensors and high-performance embedded systems, and captures essential data points such as GPS, acceleration, gyroscope, speed, and 360-degree image and depth data which are crucial for developing deep learning models for autonomous navigation and accident prevention. We detail the hardware architecture, consisting of an Nvidia Jetson TX2 NX platform, Raspberry Pi 4 Model B, and various sensors, all tailored to operate efficiently on a two-wheeler. The software methodology is designed to synchronize and process the data effectively, hence allowing us to generate accurate and thorough datasets. Our results demonstrate the potential of the platform towards improving two-wheeler safety and contributing to the advancement of autonomous vehicle technologies. Finally, we provide an analysis of the shortcomings of the platform architecture that we observe after the first few data collection runs and we detail the fixes that we implement. We also discuss the scope of  this work and future work that can be done in the domain of two-wheeler data collection as well as the deployment of predictive models on two-wheelers for various purposes. Overall, this thesis aims to advance the integration of machine learning and predictive models for two-wheeler safety by addressing critical gaps in data availability, model design, and edge deployment. We demonstrate the potential of lightweight model inference on edge and also develop a novel architecture of comprehensive riding data collection, which is essential for large scale model training. 

July 2025