Are VLMs Ready for Autonomous Driving? An Empirical Study from the
  Reliability, Data, and Metric Perspectives
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.
6780c27b7a8cd9c2c506a074
Shaoyuan Xie
admin_assigned
2025-01-10T08:34:11.760Z
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Shaoyuan Xie
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shaoyuanxie
6780c27b7a8cd9c2c506a075
Lingdong Kong
claimed_verified
2025-01-10T08:20:30.948Z
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Lingdong Kong
user
ldkong
6780c27b7a8cd9c2c506a076
Yuhao Dong
admin_assigned
2025-01-10T08:34:17.783Z
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Yuhao Dong
user
THUdyh
6780c27b7a8cd9c2c506a077
Chonghao Sima
admin_assigned
2025-01-10T08:34:27.775Z
65bcaa3f88b6228542e9caa3
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Sima
user
Chonghao
6780c27b7a8cd9c2c506a078
Wenwei Zhang
admin_assigned
2025-01-10T08:34:33.723Z
64e8505321540e1da3226b54
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Wenwei Zhang
user
ZwwWayne
6780c27b7a8cd9c2c506a079
Qi Alfred Chen
6780c27b7a8cd9c2c506a07a
Ziwei Liu
admin_assigned
2025-01-10T08:35:28.051Z
62ab1ac1d48b4d8b048a3473
https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
Ziwei Liu
user
liuziwei7
6780c27b7a8cd9c2c506a07b
Liang Pan
Are VLMs Ready for Autonomous Driving? An Empirical Study from the
  Reliability, Data, and Metric Perspectives
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.
DriveBench evaluates VLMs for autonomous driving, revealing limitations in visual grounding and multi-modal reasoning, and proposing improved metrics for reliability and interpretability.
Vision-Language Models DriveBench benchmark dataset visually grounded multi-modal reasoning evaluation metrics corruption awareness autonomous driving decision-making systems