CARLA MAP Leaderboard
1개 벤치마크 · 논문 8편 · 이 태스크의 논문 보기 →
Benchmarks
CARLA
Most implemented
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving
Hidden Biases of End-to-End Driving Models
MMFN: Multi-Modal-Fusion-Net for End-to-End Driving
MMFN: Multi-Modal Fusion Net for End-to-End Autonomous Driving
Papers
Hidden Biases of End-to-End Driving Models
End-to-end driving systems have recently made rapid progress, in particular on CARLA. Independent of their major contribution, they introduce changes to minor system components. Consequently, the source of improvements i…
Autonomous DrivingBench2DriveCARLA Leaderboard 2.0CARLA longest6+2DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving
Safely navigating through an urban environment without violating any traffic rules is a crucial performance target for reliable autonomous driving. In this paper, we present a Reinforcement Learning (RL) based methodolog…
Autonomous DrivingCARLA MAP LeaderboardGeneral Reinforcement LearningImitation Learning+1MMFN: Multi-Modal-Fusion-Net for End-to-End Driving
Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene. In previous works, cam…
CARLA MAP LeaderboardMMFN: Multi-Modal Fusion Net for End-to-End Autonomous Driving
Under review
Autonomous DrivingCARLA MAP LeaderboardGRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving
Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications such as autonomous driving and robotics. However, DRL is notoriously limited by its high sample com…
Autonomous DrivingCARLA MAP Leaderboardcontinuous-controlContinuous Control+3Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehicles
We present Pylot, a platform for autonomous vehicle (AV) research and development, built with the goal to allow researchers to study the effects of the latency and accuracy of their models and algorithms on the end-to-en…
Autonomous DrivingAutonomous VehiclesCARLA MAP Leaderboard