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Bench2Drive
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
FocalAD: Local Motion Planning for End-to-End Autonomous Driving
From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving
CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving
GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous Driving
ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous Driving
DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)
iPad: Iterative Proposal-centric End-to-End Autonomous Driving
Safety2Drive: Safety-Critical Scenario Benchmark for the Evaluation of Autonomous Driving
X-Driver: Explainable Autonomous Driving with Vision-Language Models
Two Tasks, One Goal: Uniting Motion and Planning for Excellent End To End Autonomous Driving Performance
End-to-End Driving with Online Trajectory Evaluation via BEV World Model
ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation
Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training
DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving
SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment
HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decoder
DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving
Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning
Hidden Biases of End-to-End Driving Datasets
Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model
DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving
CarLLaVA: Vision language models for camera-only closed-loop driving
Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning
SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation
GenAD: Generative End-to-End Autonomous Driving
DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving
Hidden Biases of End-to-End Driving Models
Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
Planning-oriented Autonomous Driving
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
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