Viewpoint
Complementary camera, LiDAR, and radar with active sensors provide redundant, superhuman safety.
Complementary camera, LiDAR, and radar with active sensors provide redundant, superhuman safety.
Dmitri Dolgov says Waymo uses complementary camera, LiDAR, and radar sensing modalities, with active sensors providing redundancy and enabling detection in darkness, dust, and glare for superhuman safety.
- Speaker
- Dmitri Dolgov
- Topic
- Sensing and Hardware
- Source timestamp
- 16:04
More from this interview
- Physical AI requires moving fast while shipping safely
- Physical AI faces life-cost errors, extreme latency, no digitized internet, and pre-deployment safety validation.
- An 18-month demo versus a 15-year scalable service with over 200 million miles.
- Reliability follows an exponential ladder of nines; at fleet scale rare events become daily.
- Do not anchor to today's component costs
- Waymo rebuilt its stack around AI breakthroughs; production without regressions is harder than prototyping.
- Waymo's foundation model is a multimodal world-action language model using encoder-decoder and fast/slow reasoning.
- Structured approaches should channel scale, not fight it
- Waymo's structure-augmented models improve safety validation
- Waymo's simulator is a large AI model for closed-loop evaluation and training.
- Generative models enable training on rare edge-case scenarios
- Agent, simulator, and critic AIs share a foundation world model and data flywheel.
- Metrics and evaluation build the strategic moat
- Autonomy trust is earned through safety frameworks and data
- Physical AI mirrors digital AI's earlier stage; next decade unfolds in physical world.