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Datasets/CARLA

CARLA

Car Learning to Act

EnvironmentCC-BY / MITIntroduced 2017-11-10

CARLA (CAR Learning to Act) is an open simulator for urban driving, developed as an open-source layer over Unreal Engine 4. Technically, it operates similarly to, as an open source layer over Unreal Engine 4 that provides sensors in the form of RGB cameras (with customizable positions), ground truth depth maps, ground truth semantic segmentation maps with 12 semantic classes designed for driving (road, lane marking, traffic sign, sidewalk and so on), bounding boxes for dynamic objects in the environment, and measurements of the agent itself (vehicle location and orientation).

Source: Synthetic Data for Deep Learning

Benchmarks

Autonomous Driving/Driving scoreAutonomous Driving/Route completionAutonomous Driving/Infraction penaltyAutonomous Vehicles/Driving scoreAutonomous Vehicles/Route completionAutonomous Vehicles/Infraction penaltyAutonomous Vehicles/Driving ScoreAutonomous Vehicles/Route CompletionAutonomous Vehicles/Infraction ScoreCARLA longest6/Driving ScoreCARLA longest6/Route CompletionCARLA longest6/Infraction Score

Related Benchmarks

CARLA Leaderboard/Autonomous Driving/Driving ScoreCARLA Leaderboard/Autonomous Driving/Infraction penaltyCARLA Leaderboard/Autonomous Driving/Route CompletionCARLA Leaderboard/Autonomous Vehicles/Driving ScoreCARLA Leaderboard/Autonomous Vehicles/Infraction penaltyCARLA Leaderboard/Autonomous Vehicles/Route Completion

Statistics

Papers
1,345
Benchmarks
12

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Tasks

Autonomous DrivingAutonomous VehiclesCARLA Leaderboard 2.0CARLA MAP LeaderboardCARLA longest6Imitation Learning