Bench2Drive On Bench2Drive

Metrics

Driving Score

Results

Performance results of various models on this benchmark

		Paper Title
HiP-AD	86.77	HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decoder
SimLingo-Base (CarLLaVa)	85.94	CarLLaVA: Vision language models for camera-only closed-loop driving
TransFuser++	84.21	Hidden Biases of End-to-End Driving Models
Hydra-NeXt	73.86	Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training
DiffAD	67.92	DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving
DriveAdapter	64.22	DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving
Drivetransformer-Large	63.46	DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
ThinkTwice	62.44	Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving
TCP-traj	59.90	Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
DiFSD	52.02	DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving
TCP-traj w/o distillation	49.30	Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
UniAD-Base	45.81	Planning-oriented Autonomous Driving
GenAD	44.81	GenAD: Generative End-to-End Autonomous Driving
SparseDrive	44.54	SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation
VAD	42.35	VAD: Vectorized Scene Representation for Efficient Autonomous Driving
UniAD-Tiny	40.73	Planning-oriented Autonomous Driving
TCP	40.70	Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
TCP-ctrl	30.47	Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
AD-MLP	18.05	Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving

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Bench2Drive On Bench2Drive

Metrics

Driving Score

Results

Performance results of various models on this benchmark

		Paper Title
HiP-AD	86.77	HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decoder
SimLingo-Base (CarLLaVa)	85.94	CarLLaVA: Vision language models for camera-only closed-loop driving
TransFuser++	84.21	Hidden Biases of End-to-End Driving Models
Hydra-NeXt	73.86	Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training
DiffAD	67.92	DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving
DriveAdapter	64.22	DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving
Drivetransformer-Large	63.46	DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
ThinkTwice	62.44	Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving
TCP-traj	59.90	Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
DiFSD	52.02	DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving
TCP-traj w/o distillation	49.30	Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
UniAD-Base	45.81	Planning-oriented Autonomous Driving
GenAD	44.81	GenAD: Generative End-to-End Autonomous Driving
SparseDrive	44.54	SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation
VAD	42.35	VAD: Vectorized Scene Representation for Efficient Autonomous Driving
UniAD-Tiny	40.73	Planning-oriented Autonomous Driving
TCP	40.70	Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
TCP-ctrl	30.47	Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
AD-MLP	18.05	Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving

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