Bench2Drive-VL: Benchmarks for Closed-Loop Autonomous Driving with Vision-Language Models

• URL: http://arxiv.org/abs/2604.01259v1
• Date: Wed, 01 Apr 2026 11:38:46 GMT
• Title: Bench2Drive-VL: Benchmarks for Closed-Loop Autonomous Driving with Vision-Language Models
• Abstract summary: In autonomous driving, closed-loop evaluation has become widely recognized as a more reliable validation method than open-loop evaluation.<n>We present Bench2Drive-VL, an extension of Bench2Drive that brings closed-loop evaluation to VLM-based driving.
• Score: 50.22099309218635
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the rise of vision-language models (VLM), their application for autonomous driving (VLM4AD) has gained significant attention. Meanwhile, in autonomous driving, closed-loop evaluation has become widely recognized as a more reliable validation method than open-loop evaluation, as it can evaluate the performance of the model under cumulative errors and out-of-distribution inputs. However, existing VLM4AD benchmarks evaluate the model`s scene understanding ability under open-loop, i.e., via static question-answer (QA) dataset. This kind of evaluation fails to assess the VLMs performance under out-of-distribution states rarely appeared in the human collected datasets.To this end, we present Bench2Drive-VL, an extension of Bench2Drive that brings closed-loop evaluation to VLM-based driving, which introduces: (1) DriveCommenter, a closed-loop generator that automatically generates diverse, behavior-grounded question-answer pairs for all driving situations in CARLA,including severe off-route and off-road deviations previously unassessable in simulation. (2) A unified protocol and interface that allows modern VLMs to be directly plugged into the Bench2Drive closed-loop environment to compare with traditional agents. (3) A flexible reasoning and control framework, supporting multi-format visual inputs and configurable graph-based chain-of-thought execution. (4) A complete development ecosystem. Together, these components form a comprehensive closed-loop benchmark for VLM4AD. All codes and annotated datasets are open sourced.

Related papers

• Roadside-Cooperative Autonomous Driving: From Data Platform to Vision-Language End-to-End Reasoning [19.188066393763407]
  AURORA is an end-to-end cooperative driving framework.<n>AURORA mitigates spatial and semantic discrepancies across ego and roadside viewpoints.<n>LoRA-adapted VLM bridges semantic reasoning and generative trajectory planning.
  arXiv  Detail & Related papers  (2026-08-21T12:29:27Z)
• MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving [62.43744546817599]
  Vision-language--action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings.<n>We propose MAPLE, a novel framework for reactive, multi-agent rollout of a dynamic driving scenario in the latent space of the VLA model.<n> MAPLE achieves state-of-the-art driving performance on Bench2Drive and demonstrates scalable, closed-loop multi-agent play for robust E2E autonomous driving systems.
  arXiv  Detail & Related papers  (2026-05-13T23:35:14Z)
• MindVLA-U1: VLA Beats VA with Unified Streaming Architecture for Autonomous Driving [54.57163800903507]
  We present MindVLA-U1, the first unified streaming VLA architecture for autonomous driving.<n>A unified VLM backbone produces AR language tokens and flow-matching continuous action trajectories in a single forward pass over one shared representation.<n>On the long-tail WOD-E2E benchmark, MindVLA-U1 surpasses experienced human drivers for the first time.
  arXiv  Detail & Related papers  (2026-05-12T18:09:42Z)
• OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models [69.2503510410147]
  We present a unified autonomous driving framework built upon a pretrained VLM.<n>We demonstrate that pretrained VLM attention exhibits strong transferability beyond pure language modeling.<n>Experiments on end-to-end autonomous driving benchmarks demonstrate state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-04-20T07:50:00Z)
• Collision-Aware Vision-Language Learning for End-to-End Driving with Multimodal Infraction Datasets [7.932761533792761]
  High infraction rates remain the primary bottleneck for end-to-end (E2E) autonomous driving.<n>We develop a Video-Language-Augmented Anomaly Detector (VLAAD)<n>VLAAD is a collision-aware plug-in module that can be seamlessly integrated into existing E2E driving models.
  arXiv  Detail & Related papers  (2026-03-26T22:32:52Z)
• dVLM-AD: Enhance Diffusion Vision-Language-Model for Driving via Controllable Reasoning [69.36145467833498]
  We introduce dVLM-AD, a diffusion-based vision-language model that unifies perception, structured reasoning, and low-level planning for end-to-end driving.<n> evaluated on nuScenes and WOD-E2E, dVLM-AD yields more consistent reasoning-action pairs and achieves planning performance comparable to existing driving VLM/VLA systems.
  arXiv  Detail & Related papers  (2025-12-04T05:05:41Z)
• nuCarla: A nuScenes-Style Bird's-Eye View Perception Dataset for CARLA Simulation [10.12033488279778]
  nuCarla is a large-scale, nuScenes-style BEV perception dataset built within the CARLA simulator.<n>By providing both data and models as open benchmarks, nuCarla substantially accelerates closed-loop E2E development.
  arXiv  Detail & Related papers  (2025-11-12T22:45:36Z)
• CoReVLA: A Dual-Stage End-to-End Autonomous Driving Framework for Long-Tail Scenarios via Collect-and-Refine [73.74077186298523]
  CoReVLA is a continual learning framework for autonomous driving.<n>It improves the performance in long-tail scenarios through a dual-stage process of data Collection and behavior Refinement.<n>CoReVLA achieves a Driving Score (DS) of 72.18 and a Success Rate (SR) of 50%, outperforming state-of-the-art methods by 7.96 DS and 15% SR under long-tail, safety-critical scenarios.
  arXiv  Detail & Related papers  (2025-09-19T13:25:56Z)
• Bench2ADVLM: A Closed-Loop Benchmark for Vision-language Models in Autonomous Driving [28.224158941451087]
  Vision-Language Models (VLMs) have emerged as a promising paradigm in autonomous driving (AD)<n>Current performance evaluation protocols for VLM-based AD systems (ADVLMs) are predominantly confined to open-loop settings with static inputs.<n>We introduce Bench2ADVLM, a unified hierarchical closed-loop evaluation framework for real-time, interactive assessment of ADVLMs across both simulation and physical platforms.
  arXiv  Detail & Related papers  (2025-08-04T03:43:23Z)
• DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving [69.82743399946371]
  DriveMLM is a framework that can perform close-loop autonomous driving in realistic simulators. We employ a multi-modal LLM (MLLM) to model the behavior planning module of a module AD system. This model can plug-and-play in existing AD systems such as Apollo for close-loop driving.
  arXiv  Detail & Related papers  (2023-12-14T18:59:05Z)
• DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model [84.29836263441136]
  This study introduces DriveGPT4, a novel interpretable end-to-end autonomous driving system based on multimodal large language models (MLLMs) DriveGPT4 facilitates the interpretation of vehicle actions, offers pertinent reasoning, and effectively addresses a diverse range of questions posed by users.
  arXiv  Detail & Related papers  (2023-10-02T17:59:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.