Challenge of Millions of Driving Experiences
Tesla has more than 3 million vehicles on the road, Waymo is testing thousands of autonomous cars, and Mercedes is developing driving assistance systems – each of these vehicles is a mobile laboratory generating gigabytes of sensory data every day. The problem is that 99% of this experience is wasted.
When a vehicle encounters an unusual situation, such as children running out into the roadway, an unexpected detour, or a traffic signal failure, its experience should immediately become a lesson for the entire fleet. Unfortunately, traditional data formats make it too expensive and time-consuming to upload and process in such scenarios.
VectorDiff as a Road Experience Language
VectorDiff allows semantic coding of road scenarios. Instead of transmitting gigabytes of raw data from cameras and sensors, a vehicle can transmit a „situation history.” „Child object appeared at point (X,Y) at time T, moved at speed V towards roadway, vehicle applied ‘emergency_brake’ with intensity I, situation resolved successfully at time T+5s.”
This semantic description occupies only a few kilobytes instead of gigabytes of raw data. Yet, it contains all the information necessary for other vehicles to learn from the experience.
Road Safety Transformation
Real-time learning: When a Tesla vehicle in California encounters a rare scenario, all the brand’s cars worldwide can update their models in minutes, not months.
Predicting rare events: VectorDiff enables the analysis of patterns preceding hazardous situations. The system can learn to recognize warning signals – subtle changes in the behavior of pedestrians or other drivers – that precede potential dangers.
Reducing infrastructure requirements: Instead of storing and analyzing petabytes of raw data, manufacturers can work with compact semantic histories, drastically reducing cloud infrastructure costs.
A real-world example: imagine an autonomous vehicle in Helsinki learning how to deal with snowy roads. Its experience, coded in VectorDiff, can immediately improve vehicle performance in Oslo, Moscow, or Calgary – wherever winter conditions present challenges.
