Scene understanding
Lane geometry, surrounding vehicles, pedestrians, obstacles, and weather fused into a world model that updates every frame.
Self-driving intelligence.
Real-time perception, prediction, and planning under pressure. This page is dedicated to our autonomous driving research — try the simulator below.
The challenge
Noisy sensors. Unpredictable humans. Fractions of a second to decide.
Driving is one of the hardest tests of machine intelligence we work on. It demands the same perception and object identification systems we build elsewhere, but running continuously, fused across sensors, with safety margins that don't bend when confidence drops.
The stack
Four subsystems, one closed loop.
Behavior forecasting
Anticipating what other road users will do — paths, speeds, intent — before committing to a maneuver.
Motion selection
Ranking lane changes, merges, and speed adjustments against safety buffers, comfort, and traffic rules in milliseconds.
Scenario testing
Thousands of synthetic edge cases — merges, intersections, sudden obstacles — rehearsed before they appear on a real road.
Decision loop
Sense → predict → plan → act.
01Sense
Perception streams, map context, and vehicle state into a unified world model.
02Predict
Trajectory forecasts, conflict detection, and risk scoring across the planning window.
03Plan
Candidate maneuvers ranked by safety margin, progress, and constraint satisfaction.
04Act
Controlled steering and acceleration with continuous outcome monitoring.
InputPerception stream
Lanes, actors, velocities, scene uncertainty.
ModelPrediction field
Trajectory hypotheses and risk per agent.
PolicyPlanning engine
Maneuvers ranked by safety and progress.
OutputControlled action
Steering and acceleration with telemetry feedback.
Self-driving research & partnerships.
Interested in autonomous mobility, simulation infrastructure, or real-time decision systems?
Get in touch