https://www.youtube.com/watch?v=tbgtGQIygZQ
The Mission
Building and optimizing the entire infrastructure (hardware and software) from ground up with autonomous self driving as the mission
Mission and decision making
Design decisions are made with trade off between functionality and cost to achieve the mission while keeping cost in control
- Lidar is not useful when cameras are available
- driving cars with HD mapping makes the entire operation brittle since actual road conditions can change
Operating structure
- Data Team
- Hardware Team
- Software Team
The Data model
- Cars on roads are constantly collecting new data
- New data is being utilized to train and improve neural network model
- New improved model is constantly being deployed back to the car to improve self driving
- Real world data provides visibility into long tail scenarios that simulated data cannot. Simulating long tail scenario is an intractable problem
- Balancing between data model and software
- Neural network is suitable for problems that are hard to solve by defining functions / heuristics
- Simple heuristics are better handled through coding in software
Future revenue model
Robo-taxi that will disrupt the ride-sharing space.
- Consumer car – USD0.60 / mile
- Ride sharing – USD 2-3 / mile
- Telsa Network – USD 0.18 / mile
Main challenges:
- Legal – need more data and processing time to get approved
- Battery capacity
- Social norms around robo taxi