Deep Learning for Enhancing Autonomous Driving Systems: Technological Innovations, Strategic Implementations, and Business Implications

Abstract

This thesis investigates the transformative role of deep learning and its strategic adoption within the autonomous driving industry. A dual research approach was employed, combining a technical case study and a survey of industry experts from APAC, Europe, and North America. In the first phase, an innovative architecture for real-time, automated HD map creation was developed. The system integrates data from cameras, LiDAR, and standard-definition maps to generate vectorized HD maps, enhancing accuracy and scalability over existing methods. By leveraging advanced techniques such as Bird’s Eye View (BEV) encoding, transformers, and Graph Convolutional Networks (GCNs), the architecture dynamically updates crucial road features like lane boundaries and pedestrian crossings, resulting in a 5.9% improvement in mean Average Precision (mAP) and significantly enhancing real-time map generation, critical for autonomous navigation. In the second phase, a survey gathered insights into the regional and organizational challenges of deep learning adoption. While North America and Europe prioritize technological advancements, APAC and China are driven by competitive pressures and cost concerns. Common challenges across regions include data quality, talent shortages, and regulatory compliance. Organizations are adopting varied strategies, such as upskilling teams or hiring externally, to address these issues. This research not only proposes a scalable, real-time solution for HD map generation but also offers strategic insights into the successful adoption of deep learning technologies in autonomous driving, highlighting future trends like End-to-End Learning, Simulation and Virtual Training, and Edge Computing.