This project, in collaboration with HERE Technologies, addresses the challenge of scaling digital map creation for autonomous driving and location-based services. Current manual and rule-based methods cannot process the volume and complexity of dashcam imagery needed for real-time map updates. Using computer vision and deep learning, we developed a prototype AI pipeline to automatically detect, classify, and localize road features such as lane markings and surface symbols. This proof of concept demonstrates the potential of AI-driven automation to improve mapping efficiency, accuracy, and scalability.
Watch the team present this project at 31:10 in the session recording here.
Keywords: computer vision, deep learning, street-level imagery, lane detection, feature localization, autonomous driving, digital maps
Faculty Advisor
Sanjay Boddhu is the Head of AI/ML Engineering at HERE Technologies, a global leader in location data and platform services. He has over 15 years of experience in leading and mentoring diverse and distributed teams in developing state-of-the-art applications and solutions in the domains of Computer Vision, Image Processing, Natural Language Processing, Predictive Analytics, and Data Science Strategy/Modeling. He holds a Ph.D. in Computer Science and Engineering from Wright State University and is a Senior Member of the IEEE.
At HERE, he is leading the UniMap Automation initiative, which leverages AI and machine learning to transform raw spatial and nonspatial data from various sources, such as imagery, probe data, car camera feeds, lidar, and IoT data, into an actionable, navigable digital map that is updated in near real-time. He is also responsible for designing and deploying algorithms at scale, managing product roadmaps and stakeholder engagements, and driving innovation and excellence in map automation and computer vision. His mission is to revolutionize how maps are created and maintained, and to enable new use cases and opportunities for autonomous and robotic mobility.