Back to AI/MLLanes, roads, and paths are represented as vectors Objects and their trajectories are vectorized Makes planning and decision-making more efficient Enables end-to-end learning More efficient than pixel-level processing Easier to reason about spatial relationships Better for planning algorithms Enables neural networks to learn driving policies directly Neural networks learn to convert camera inputs to vector space Planning happens in this vector space Control outputs are generated from vector space representations
Vector Space
Vector Space is Tesla's representation of the driving environment. Instead of working with raw pixels or object detections, the system converts the environment into a vector space representation.
Key Concepts
Benefits
Implementation
This approach is central to Tesla's end-to-end learning strategy, allowing the system to learn optimal driving behaviors directly from data.