SOTAVerified

Bench2Drive

Bench2Drive is an autonomous driving benchmark based on the CARLA leaderboard 2.0. It consists of 220 short routes featuring safety critical scenarios. The evaluation is performed closed-loop in the CARLA simulator. The performance of an entire driving stack is being evaluated.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 35 papers

TitleStatusHype
Hidden Biases of End-to-End Driving DatasetsCode4
Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous DrivingCode4
SparseDrive: End-to-End Autonomous Driving via Sparse Scene RepresentationCode4
Planning-oriented Autonomous DrivingCode4
End-to-End Driving with Online Trajectory Evaluation via BEV World ModelCode3
SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action AlignmentCode3
CarLLaVA: Vision language models for camera-only closed-loop drivingCode3
GenAD: Generative End-to-End Autonomous DrivingCode3
VAD: Vectorized Scene Representation for Efficient Autonomous DrivingCode3
iPad: Iterative Proposal-centric End-to-End Autonomous DrivingCode2
HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single DecoderCode2
DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous DrivingCode2
DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous DrivingCode2
Hidden Biases of End-to-End Driving ModelsCode2
Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous DrivingCode2
ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous DrivingCode1
Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop TrainingCode1
DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-DrivingCode1
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong BaselineCode1
GEMINUS: Dual-aware Global and Scene-Adaptive Mixture-of-Experts for End-to-End Autonomous DrivingCode0
FocalAD: Local Motion Planning for End-to-End Autonomous Driving—0
From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving—0
GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous DrivingCode0
CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving—0
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)—0
Show:102550
← PrevPage 1 of 2Next →

No leaderboard results yet.