Harald Schäfer
CTO at Comma AI, where he works on OpenPilot and open source vehicle autonomy. Specializes in self-driving systems, end-to-end learning, simulation, and robotics.
Top 3 podcasts with Harald Schäfer
Ranked by the Snipd community
99 snips
Apr 16, 2026 • 46min
Open Source Self-Driving with Comma AI
Autonomous driving is not just a big tech or closed-source game, it's becoming accessible through open innovation and real-world deployment. Dan and Chris sit down with Harald Schäfer, CTO at Comma AI, to explore how OpenPilot is bringing self-driving to everyday vehicles using open source AI. We dive into the intersection of machine learning, robotics, and simulation, including how world models are enabling training at scale and shaping the future of autonomy.Featuring:Harald Schäfer – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Comma
9 snips
Sep 18, 2024 • 46min
No hype, Just works: How Comma reached 100M miles in autonomous driving | E2011
Harald Schäfer, CTO of Comma.ai, shares insights on reshaping autonomous driving with open-source technology. He discusses how camera-based systems compare to lidar, emphasizing their advantages over human drivers. The conversation also highlights the rollout timeline for autonomous vehicles and the differing approaches of major players like Tesla and Waymo. Schäfer critiques the industry’s sustainability challenges and explores the potential for DIY solutions in self-driving technology. It's a fascinating look at the future of our roads!
Jul 13, 2025 • 51min
Ep#19 Learning to Drive from a World Model
In this engaging discussion, Harald Schäfer leads the autonomy team at Comma AI, sharing insights from his eight-year journey in robotics. He dives into groundbreaking advancements in self-driving technology, emphasizing data-driven learning and world models. The conversation covers the challenges of developing versatile systems for various car models and innovative simulation strategies. Harald also explores the trade-offs in world model training, the importance of harnessing human-driven data, and the commitment to open-source innovations in automotive AI that could revolutionize user experiences.
