Architecture of a Two-wheeler Mobile Sensing Platform for Riding Data Collection

This paper presents the architecture of a two-wheeler mobile sensing platform aimed at collecting compre-hensive two-wheeler riding data. Two-wheelers, prevalent in many countries for their agility and efficiency lack extensive study in autonomous vehicle research. Recognizing this gap, our work introduces a scalable and modular platform equipped with multiple sensors and high-performance embedded systems. The platform captures essential data points such as GPS, acceleration, gyroscope, speed, and 360-degree image and depth data, 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 platform's potential in improving two-wheeler safety and contributing to the advancement of autonomous vehicle technologies.