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Data-Driven Non-Reactive Autonomous Vehicle Benchmark.
Evaluates autonomous driving stacks that produce waypoints with a static dataset using the PDM-Score metric. The PDM-score metric performs a pseudo-simulation by rolling out the trajectory and simulating all other actors via log-replay. This results in an open-loop evaluation that correlates with closed-loop performance.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Epona: Autoregressive Diffusion World Model for Autonomous Driving
ReSim: Reliable World Simulation for Autonomous Driving
ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning
Generalized Trajectory Scoring for End-to-end Multimodal Planning
Pseudo-Simulation for Autonomous Driving
GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous Driving
iPad: Iterative Proposal-centric End-to-End Autonomous Driving
TransDiffuser: End-to-end Trajectory Generation with Decorrelated Multi-modal Representation for Autonomous Driving
End-to-End Driving with Online Trajectory Evaluation via BEV World Model
Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training
Centaur: Robust End-to-End Autonomous Driving with Test-Time Training
Finetuning Generative Trajectory Model with Reinforcement Learning from Human Feedback
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving
DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers
Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model
DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving
NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking
Enhancing End-to-End Autonomous Driving with Latent World Model
Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
Planning-oriented Autonomous Driving
0/1 Deep Neural Networks via Block Coordinate Descent
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.