Birgit Henke

The end of human drivers? Tried Tesla Robotaxi in the Bay Area for the first time, 8.8 miles from Meta SF to Daly City station for $18. Unlike Waymo, Tesla’s Robotaxi can take the freeway. The ride was smooth, and the decision-making felt far closer to human than “bot-like.” What surprised me most: it’s just a regular Model Y. With the built-in rear screen, any Tesla could potentially be activated as a Robotaxi. That’s scalability at an entirely different level. Waymo might have an early lead, but Tesla’s vision-only approach gives it massive coverage and faster rollout potential. If this scales, Uber, Lyft, and even car ownership itself could be disrupted faster than expected. This is exactly why I quit my job to go all-in on ElasticDash We’re entering a new industrial revolution, the real question is: will you shape it, or be forced to adapt?

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🛣️ What a weekend! Hello from California. I finally had the chance to put Waymo to the test in San Francisco, and it was an absolute incredible experience. After following the Google Self-Driving Car Project since its inception in 2009 and spending most of my Google career in the automotive space, this felt like a true full-circle moment. We really pushed the technology to its limits with a dozen rides, complete with some hilarious pick-up moments and many conversations with the virtual assistant. It's one thing to follow the development and think you know everything about it, but another to experience it firsthand. The exciting news that #Waymo is planning to launch in London is a natural next step, building on the company's strong existing ties to the UK. London and Oxford are already home to Waymo’s first international engineering hubs. Furthermore, Waymo is a proud partner of the iconic British brand Jaguar Land Rover. The potential for self-driving cars to make a difference in road safety and accessible transport is huge. In fact, #Waymo was named to TIME’s list of the Best Inventions of 2025. A massive thank you to my "ride or dies," Dr. Will Dunaway and Dr. Phyliss You, for helping me test everything, for letting me use their US account, and for being up for that "one last spin" again and again. #GoogleAutomotive #Steffigonetesting #Waymo #AutonomousVehicles #SelfDrivingCars #FutureOfMobility #London #Innovation #TIMEBestInventions #Google #JLR

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NTSB has a preliminary description of the Waymo robotaxi colliding with a student pedestrian in Santa Monica. The diagram provides useful insight into how it happened. The student seems to have crossed mid-block instead of using the available marked crosswalk at the end of the block. But then again, this was a 9-year-old student and it is an elementary school, so such activity should not be a complete surprise. We also do not know if that is a customary behavior for other students in this scenario, which would be something accounted for by human drivers participating in the cultural experience that is "drop-off line". The student was crossing from a vehicle in the opposite-direction travel lane as the robotaxi. It is worth paying particular attention to the boundaries of the school speed limit, as well as the proximity to the school. The collision occurred just after the transition from 15 mph to 25 mph, with the robotaxi going 17 mph. Based on this analysis that is below the legal speed limit, but speed appropriate for context is an additional determination that is still pending. One fact of interest is that the robotaxi was there because it had just "completed a passenger drop-off ... at the stop sign in front of an elementary school". (Wondering how old that passenger riding solo was arriving at an elementary school .... but that potential issue is unrelated to the collision.) Thanks as always to NTSB for their rigorous approach to investigations. Worth noting as well that Waymo, as a party to the investigation, would not be expected to comment in an ongoing investigation. So a lack of comment from them is neither a positive nor negative indication. NTSB Investigation web page: https://lnkd.in/epCe6Qmc

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Two recent pieces of Waymo news that are making me very happy: 1) The company just raised another $16B to accelerate its deployment of robotaxis around the world. They’re already serving 6 major metros in the U.S., with an annual volume of ~15 million rides. In 2026, they’re putting their foot on the accelerator (ok, “gas pedal” doesn’t work, their fleet is electric): They’ll add service in 20 more cities, including expansion to Tokyo and London. I’m excited for more people to experience the joys of robotaxis, for larger fleets in existing operating areas, and for faster service. 2) Waymo announced service to SFO. Working with airports has been a huge blocker. And even though you can take a continuous ride from San Francisco to San Jose in a Waymo, you still have to Uber to the airport. That annoyance is no more. Music to my ears. Excited to see Waymo continue to dominate ;)

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Are you team Tesla or Waymo? Two very different philosophies in autonomous driving. ✅ Tesla bets on simplicity — a vision-only approach, trusting end-to-end neural networks to interpret the world. ✅ Waymo, on the other hand, builds redundancy into every step — combining LiDAR, radar, cameras, and on-board compute to achieve reliability through sensor fusion. At SEMICON West, Waymo presented a deep look into their system architecture and mission: “Be the world’s most trusted driver.” Key engineering takeaways: - Complete in-house designed 3D sensor suite, integrating: ▪ LiDAR – long-range 360° and short-range perimeter units ▪ Cameras – full-coverage vision system for object detection and classification ▪ Radar – complementing LiDAR under adverse visibility - Central compute node processing hundreds of millions of sensor readings per second - Designed as a safety-critical system with near-zero fault tolerance Tesla or Waymo? Both have one goal: safe autonomy. 🎥 See the short clip below for a look inside Waymo’s sensor suite and I love all the unexpected situations which Waymo is able to handle successfully!

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I’ve shared a new long-form post summarizing key system-design principles for motion prediction in AVs — grounded in real engineering challenges rather than theory. If you’re into ML and curious about new directions in system design, you might find it valuable. Your feedback is highly appreciated. 👉 https://lnkd.in/dtY7UZnW

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