open scenario map
open scenario map
open scenario map
open scenario map
Open scenario
map
unseen scenarios at scale
unseen scenarios at scale
Scaling the test of generalization for ADAS systems beyond the development fleet.
YEAR
Cities
Experts
Incidents
Severe incidents
KM
scenarios that matter
We partnered with driving school instructors to annotate scenarios where human drivers make unsafe driving decisions, or critical errors. The labels are categorized by the type of driving mistake; e.g. 'Right of Way' for not following the appropriate priority order at an intersection, or 'Placement' for incorrect placement of the vehicle between lane markings. Driving intructors additionally annotate severe driving mistakes by using dual-control pedals. These scenarios are labeled as 'Instructor Pedal'. See the current cohort of scenarios annotated by driving instructors in the interactive map on the right.
scenarios that matter
We partnered with driving school instructors to annotate scenarios where human drivers make unsafe driving decisions, or critical errors. The labels are categorized by the type of driving mistake; e.g. 'Right of Way' for not following the appropriate priority order at an intersection, or 'Placement' for incorrect placement of the vehicle between lane markings. Driving intructors additionally annotate severe driving mistakes by using dual-control pedals. These scenarios are labeled as 'Instructor Pedal'. See the current cohort of scenarios annotated by driving instructors in the interactive map on the right.
scenarios that matter
We partnered with driving school instructors to annotate scenarios where human drivers make unsafe driving decision or critical errors. The labels are categorized by type of driving mistake. F.ex Right of Way for not following the appropriate priority order at intersection or Placement for incorrect placement of vehicle between lane markings. Driving intructor additionally annotate severe driving mistakes by using dual control pedals. These scenario are labeled as Instructor Pedal.
scenarios that matter
We partnered with driving school instructors to annotate scenarios where human drivers make unsafe driving decisions, or critical errors. The labels are categorized by the type of driving mistake; e.g. 'Right of Way' for not following the appropriate priority order at an intersection, or 'Placement' for incorrect placement of the vehicle between lane markings. Driving intructors additionally annotate severe driving mistakes by using dual-control pedals. These scenarios are labeled as 'Instructor Pedal'. See the current cohort of scenarios annotated by driving instructors in the interactive map on the right.
scenarios that matter
We partnered with driving school instructors to annotate scenarios where human drivers make unsafe driving decisions, or critical errors. The labels are categorized by the type of driving mistake; e.g. 'Right of Way' for not following the appropriate priority order at an intersection, or 'Placement' for incorrect placement of the vehicle between lane markings. Driving intructors additionally annotate severe driving mistakes by using dual-control pedals. These scenarios are labeled as 'Instructor Pedal'. See the current cohort of scenarios annotated by driving instructors in the interactive map on the right.
scenarios that matter
We partnered with driving school instructors to annotate scenarios where human drivers make unsafe driving decisions, or critical errors. The labels are categorized by the type of driving mistake; e.g. 'Right of Way' for not following the appropriate priority order at an intersection, or 'Placement' for incorrect placement of the vehicle between lane markings. Driving intructors additionally annotate severe driving mistakes by using dual-control pedals. These scenarios are labeled as 'Instructor Pedal'. See the current cohort of scenarios annotated by driving instructors in the interactive map on the right.
Vektorized
'Vektorized' scenarios are first captured with eight HDR cameras, and then morphed by Vektor into the OpenScenario format for an unbiased, unseen test of generalization for ADAS systems in simulation.
Vektorized
'Vektorized' scenarios are first captured with eight HDR cameras, and then morphed by Vektor into the OpenScenario format for an unbiased, unseen test of generalization for ADAS systems in simulation.
Vektorized
'Vektorized' scenarios are first captured with eight HDR cameras, and then morphed by Vektor into the OpenScenario format for an unbiased, unseen test of generalization for ADAS systems in simulation.
Vektorized
'Vektorized' scenarios are first captured with eight HDR cameras, and then morphed by Vektor into the OpenScenario format for an unbiased, unseen test of generalization for ADAS systems in simulation.
Vektorized
'Vektorized' scenarios are first captured with eight HDR cameras, and then morphed by Vektor into the OpenScenario format for an unbiased, unseen test of generalization for ADAS systems in simulation.
Vektorized
'Vektorized' scenarios are first captured with eight HDR cameras, and then morphed by Vektor into the OpenScenario format for an unbiased, unseen generalization test for ADAS systems in simulation.
Advancing autonomy through test for generalization
fleet & hardware
Expand coverage by sponsoring a driving school
Bridge data gaps by completing drive missions
Computing
Donate cloud credits for hosting Open Scenario Map
Donate GPUs for scenario extraction with Vektor
data & MAPS
Contribute lane-level maps to expand Open Scenario Map
Add natural language commentary to scenarios
research & Development
Partner with Yaak to advance the development of Vektor
Contribute to Open Scenario Map by reviewing scenarios
Join the waitlist
Get notified when Open Scenario Map is public.
Join the waitlist
Get notified when Open Scenario Map is public.
Join the waitlist
Get notified when Open Scenario Map is public.
join the waitlist
Get notified when Open Scenario Map is public
Join the waitlist
Get notified when Open Scenario Map is public.
Join the waitlist
Get notified when Open Scenario Map is public.