title: Are VLMs Ready for Autonomous Driving? An Empirical Study from the
    Reliability, Data, and Metric Perspectives
summary: Recent advancements in Vision-Language Models (VLMs) have sparked interest in
  their use for autonomous driving, particularly in generating interpretable
  driving decisions through natural language. However, the assumption that VLMs
  inherently provide visually grounded, reliable, and interpretable explanations
  for driving remains largely unexamined. To address this gap, we introduce
  DriveBench, a benchmark dataset designed to evaluate VLM reliability across 17
  settings (clean, corrupted, and text-only inputs), encompassing 19,200 frames,
  20,498 question-answer pairs, three question types, four mainstream driving
  tasks, and a total of 12 popular VLMs. Our findings reveal that VLMs often
  generate plausible responses derived from general knowledge or textual cues
  rather than true visual grounding, especially under degraded or missing visual
  inputs. This behavior, concealed by dataset imbalances and insufficient
  evaluation metrics, poses significant risks in safety-critical scenarios like
  autonomous driving. We further observe that VLMs struggle with multi-modal
  reasoning and display heightened sensitivity to input corruptions, leading to
  inconsistencies in performance. To address these challenges, we propose refined
  evaluation metrics that prioritize robust visual grounding and multi-modal
  understanding. Additionally, we highlight the potential of leveraging VLMs'
  awareness of corruptions to enhance their reliability, offering a roadmap for
  developing more trustworthy and interpretable decision-making systems in
  real-world autonomous driving contexts. The benchmark toolkit is publicly
  accessible.
paper_authors._id: 6780c27b7a8cd9c2c506a074
paper_authors.name: Shaoyuan Xie
paper_authors.status: admin_assigned
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paper_authors.fullname: Shaoyuan Xie
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paper_authors._id: 6780c27b7a8cd9c2c506a075
paper_authors.name: Lingdong Kong
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paper_authors.fullname: Lingdong Kong
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paper_authors._id: 6780c27b7a8cd9c2c506a076
paper_authors.name: Yuhao Dong
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paper_authors.fullname: Yuhao Dong
paper_authors.type: user
paper_authors.user: THUdyh
paper_authors._id: 6780c27b7a8cd9c2c506a077
paper_authors.name: Chonghao Sima
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paper_authors.fullname: Sima
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paper_authors.user: Chonghao
paper_authors._id: 6780c27b7a8cd9c2c506a078
paper_authors.name: Wenwei Zhang
paper_authors.status: admin_assigned
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paper_authors.fullname: Wenwei Zhang
paper_authors.type: user
paper_authors.user: ZwwWayne
paper_authors._id: 6780c27b7a8cd9c2c506a079
paper_authors.name: Qi Alfred Chen
paper_authors._id: 6780c27b7a8cd9c2c506a07a
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-10T08:35:28.051Z
paper_authors._id: 62ab1ac1d48b4d8b048a3473
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
paper_authors.fullname: Ziwei Liu
paper_authors.type: user
paper_authors.user: liuziwei7
paper_authors._id: 6780c27b7a8cd9c2c506a07b
paper_authors.name: Liang Pan
paper_title: Are VLMs Ready for Autonomous Driving? An Empirical Study from the
    Reliability, Data, and Metric Perspectives
paper_summary: Recent advancements in Vision-Language Models (VLMs) have sparked interest in
  their use for autonomous driving, particularly in generating interpretable
  driving decisions through natural language. However, the assumption that VLMs
  inherently provide visually grounded, reliable, and interpretable explanations
  for driving remains largely unexamined. To address this gap, we introduce
  DriveBench, a benchmark dataset designed to evaluate VLM reliability across 17
  settings (clean, corrupted, and text-only inputs), encompassing 19,200 frames,
  20,498 question-answer pairs, three question types, four mainstream driving
  tasks, and a total of 12 popular VLMs. Our findings reveal that VLMs often
  generate plausible responses derived from general knowledge or textual cues
  rather than true visual grounding, especially under degraded or missing visual
  inputs. This behavior, concealed by dataset imbalances and insufficient
  evaluation metrics, poses significant risks in safety-critical scenarios like
  autonomous driving. We further observe that VLMs struggle with multi-modal
  reasoning and display heightened sensitivity to input corruptions, leading to
  inconsistencies in performance. To address these challenges, we propose refined
  evaluation metrics that prioritize robust visual grounding and multi-modal
  understanding. Additionally, we highlight the potential of leveraging VLMs'
  awareness of corruptions to enhance their reliability, offering a roadmap for
  developing more trustworthy and interpretable decision-making systems in
  real-world autonomous driving contexts. The benchmark toolkit is publicly
  accessible.
paper_ai_keywords: Vision-Language Models (VLMs) DriveBench benchmark dataset visually grounded multi-modal reasoning multi-modal understanding textual cues dataset imbalances evaluation metrics