Dogun Kim

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논문/E2E Autonomous Driving (MBRL) 1

논문 리뷰) Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving in CARLA-v2 ((ECCV 2024))

Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving in CARLA-v2https://arxiv.org/abs/2402.16720 Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLAReal-world autonomous driving (AD) especially urban driving involves many corner cases. The lately released..

논문/E2E Autonomous Driving (MBRL) 2025.02.05
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Dogun Kim

Dept of AI, University of Seoul

  • Studying
    • 논문
      • CV
      • NLP
      • E2E Autonomous Driving (VLM)
      • E2E Autonomous Driving (MBRL)
    • Autonomous Driving
      • 1. Perception
      • 2. LiDAR Perception
      • 3. Sensor Fusion
      • 4. Motion Planning & Control
    • Reinforcement learning
      • 0. Intro
      • 1. Tabular Solution Methods
      • 2. Approximate Solution Methods
      • 3. Frontiers
    • Deep Learning
      • 0. Intro
      • 1. PyTorch
      • 2. Natural language processing
    • 학부 수업
      • 데이터마이닝
      • 확률 및 랜덤 프로세스
      • 학부 수업
      • 정보이론
      • 컴퓨터비전
      • Reliable AI
    • Data Science
      • Data analysis
    • Robotics
      • ROS

Tag

reinforcement learning, 3d 객체 검출, retinaface, kitti 데이터셋, giou, pointnet++, 3d bounding box, planning algorithm, waymo 데이터셋, #computervision #cameraprojection #projectivetransformation #weakperspective #orthographicprojection #affineprojection #homocoordinates #calibrationmatrix #vanishingpoint, 자율주행, think2drive, 강화학습, 자율주행 데이터셋, 자율주행 평가 지표, nuscenes 데이터셋, PointNet, world model, model-based rl, carla v2,

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