Wayve LINGO Driving Language Models Quiz

Answers and explanations appear as you take the quiz.

1. What is the core capability LINGO-1 added to Wayve's driving system?
   • Natural-language commentary explaining what the car sees and does
   • A high-definition map of every city
   • A backup mechanical braking system
   • Faster lidar sensors
2. How does LINGO-2 go beyond LINGO-1?
   • It removes cameras entirely
   • It links language to driving actions, so it can both explain and be instructed
   • It only works on highways
   • It runs without any neural network
3. What does Wayve's 'end-to-end' approach mean?
   • The car drives from one end of the country to the other
   • Two separate cars drive in tandem
   • A neural network learns to drive directly from sensor data rather than hand-coded rules
   • Driving rules are written by lawyers
4. Why does LINGO-2 generating both language and driving behavior from one model matter?
   • It guarantees that every explanation is causally complete
   • It removes the need to test the driving policy
   • It proves the vehicle is safe in all conditions
   • It links the commentary to the driving model’s outputs, while still requiring separate checks of explanation faithfulness
5. What is Wayve's stated business strategy for its AI Driver?
   • Licensing its foundation-model 'AI Driver' to automakers
   • Building and selling its own branded cars
   • Operating a taxi app only
   • Selling lidar hardware
6. Where is Wayve headquartered?
   • Detroit, USA
   • London, UK
   • Shenzhen, China
   • Munich, Germany
7. How does Wayve describe LINGO-2’s driving evaluation?
   • As a text-only chatbot tested only on word puzzles
   • As a simulator with no driving model
   • As a closed-loop vision-language-action model tested on public roads
   • As a lidar sensor tested in a laboratory
8. What does the LingoQA benchmark evaluate?
   • Speech-quality scoring for telephone codecs
   • Music source separation
   • Dependency parsing across languages
   • Video question answering for autonomous-driving scenes