Object Detection in Driving Datasets Using a High-Performance Computing Platform: A Benchmark Study

Abstract

Nowadays, machine learning methods are increasingly used in different parts of autonomous driving and driving assistance systems. Yet, data and computational requirements can be enormous with these methods. Thus, providing several datasets containing many and diverse cases for the target problem and sufficient hardware for training and application of ML methods are too critical for achieving accurate results when applying them. Hence, we present an object detection benchmark study implementing the knowledge graph-based data integration framework to meet the data requirements and run the implementation on a big data and high-performance computing (HPC) platform, namely the EVOLVE. We applied different object detection methods to widely known open datasets, and compared the results on three different hardware setups, including EVOLVE. We also performed a small-scale transfer learning experiment. The results show that EVOLVE allowed the exploitation of much bigger data leading to a more efficient application of the object detection models with the help of the knowledge graph-based data integration framework. EVOLVE significantly improved the execution times compared to running them on a local laptop and a virtual machine and provided the easy-to-use and ready-to-use means to store large datasets and apply different models with its hardware and software stack.

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review

“…The digital transformation wave is challenging the mobility and transport sector like no other sector, creating a paradigm shift towards electric and automated vehicles enabled by various digital technologies such as the Internet of Things, robotics, big data analytics, and artificial intelligence [25][26][27].…”