Wayve and End-to-End Driving Models Quiz

Answers and explanations appear as you take the quiz.

1. What does Wayve's end-to-end driving model map to driving outputs?
   • Raw sensor inputs
   • A prebuilt high-definition map only
   • A human operator's verbal instructions only
   • A fixed route table without vehicle observations
2. What mapping strategy does Wayve’s AV2.0 approach emphasize?
   • A hand-built HD map for every street
   • Mapless autonomy that does not rely on high-definition maps
   • LiDAR-only navigation with no learned model
   • A fixed route table programmed for each trip
3. What is GAIA-2 in Wayve's stack?
   • A LiDAR sensor model
   • A physical robotaxi platform
   • A generative world model that simulates driving video
   • A regulatory certification standard
4. How does Wayve primarily plan to commercialize its technology?
   • Selling LiDAR units to suppliers
   • Operating its own city-wide robotaxi fleet exclusively
   • Building consumer mapping apps
   • Licensing its driving software to automakers
5. What is a key drawback of a single end-to-end driving network?
   • It is harder to interpret and debug than modular pipelines
   • It cannot use cameras
   • It requires no training data
   • It only works with LiDAR
6. What learning approach does Wayve describe for its AV2.0 system?
   • Training only from hand-coded driving rules
   • Self-supervised learning from raw, unlabeled driving data
   • A fixed route table for every city
   • A speech-recognition model trained on transcripts
7. What driving factors can GAIA-2 control when generating a scenario?
   • Only the color of the vehicle logo
   • Only the final steering angle
   • Ego-vehicle actions, dynamic agents, and scene features
   • The legal approval status of a car
8. What does Wayve mean by a sensor-agnostic AI Driver platform?
   • Every vehicle must use the same camera hardware
   • The system can drive without any sensor input
   • It requires an HD map for every road
   • Automakers can integrate it with sensor and compute choices suited to their vehicles