On the Road with GPT-4V(ision): Early Explorations of Visual-Language
  Model on Autonomous Driving
The pursuit of autonomous driving technology hinges on the sophisticated
integration of perception, decision-making, and control systems. Traditional
approaches, both data-driven and rule-based, have been hindered by their
inability to grasp the nuance of complex driving environments and the
intentions of other road users. This has been a significant bottleneck,
particularly in the development of common sense reasoning and nuanced scene
understanding necessary for safe and reliable autonomous driving. The advent of
Visual Language Models (VLM) represents a novel frontier in realizing fully
autonomous vehicle driving. This report provides an exhaustive evaluation of
the latest state-of-the-art VLM, \modelnamefull, and its application in
autonomous driving scenarios. We explore the model's abilities to understand
and reason about driving scenes, make decisions, and ultimately act in the
capacity of a driver. Our comprehensive tests span from basic scene recognition
to complex causal reasoning and real-time decision-making under varying
conditions. Our findings reveal that \modelname demonstrates superior
performance in scene understanding and causal reasoning compared to existing
autonomous systems. It showcases the potential to handle out-of-distribution
scenarios, recognize intentions, and make informed decisions in real driving
contexts. However, challenges remain, particularly in direction discernment,
traffic light recognition, vision grounding, and spatial reasoning tasks. These
limitations underscore the need for further research and development. Project
is now available on GitHub for interested parties to access and utilize:
https://github.com/PJLab-ADG/GPT4V-AD-Exploration
654dbabb0011607c31213778
Licheng Wen
admin_assigned
2023-11-10T10:44:06.784Z
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/avatars/441ccadd62e039fb8cb112f138ed917d.svg
Licheng Wen
user
Wayne-lc
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Xuemeng Yang
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Daocheng Fu
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Xiaofeng Wang
claimed_verified
2024-01-22T10:32:24.336Z
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JeffWang
user
Jeff-Wang
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Pinlong Cai
654dbabb0011607c3121377d
Xin Li
654dbabb0011607c3121377e
Tao Ma
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Yingxuan Li
654dbabb0011607c31213780
Linran Xu
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Dengke Shang
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Zheng Zhu
extracted_confirmed
2023-12-05T07:00:29.237Z
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Zheng Zhu
user
ZhengZhu
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Shaoyan Sun
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Yeqi Bai
admin_assigned
2023-11-10T10:46:32.780Z
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/avatars/d121a9599ed05016c251e67b280fc882.svg
Skura
user
baiyeqi
654dbabb0011607c31213785
Xinyu Cai
admin_assigned
2023-11-10T10:46:16.994Z
64521a2a7ba5cb58fabefcae
/avatars/caff912e95348c36b34dfc4f1509d42b.svg
XinyuCai
user
XinyuCai
654dbabb0011607c31213786
Min Dou
654dbabb0011607c31213787
Shuanglu Hu
654dbabb0011607c31213788
Botian Shi
admin_assigned
2023-11-10T10:45:52.609Z
643df87f7cd64d872cb9fabd
/avatars/c53bfabcee08de448dde973915e8b31d.svg
Botian Shi
user
friskit
On the Road with GPT-4V(ision): Early Explorations of Visual-Language
  Model on Autonomous Driving
The pursuit of autonomous driving technology hinges on the sophisticated
integration of perception, decision-making, and control systems. Traditional
approaches, both data-driven and rule-based, have been hindered by their
inability to grasp the nuance of complex driving environments and the
intentions of other road users. This has been a significant bottleneck,
particularly in the development of common sense reasoning and nuanced scene
understanding necessary for safe and reliable autonomous driving. The advent of
Visual Language Models (VLM) represents a novel frontier in realizing fully
autonomous vehicle driving. This report provides an exhaustive evaluation of
the latest state-of-the-art VLM, \modelnamefull, and its application in
autonomous driving scenarios. We explore the model's abilities to understand
and reason about driving scenes, make decisions, and ultimately act in the
capacity of a driver. Our comprehensive tests span from basic scene recognition
to complex causal reasoning and real-time decision-making under varying
conditions. Our findings reveal that \modelname demonstrates superior
performance in scene understanding and causal reasoning compared to existing
autonomous systems. It showcases the potential to handle out-of-distribution
scenarios, recognize intentions, and make informed decisions in real driving
contexts. However, challenges remain, particularly in direction discernment,
traffic light recognition, vision grounding, and spatial reasoning tasks. These
limitations underscore the need for further research and development. Project
is now available on GitHub for interested parties to access and utilize:
https://github.com/PJLab-ADG/GPT4V-AD-Exploration
Visual Language Models (VLMs) demonstrate superior scene understanding and causal reasoning in autonomous driving but face challenges in specific vision tasks like direction discernment and traffic light recognition.
Visual Language Models VLMs scene understanding causal reasoning autonomous driving real-time decision-making out-of-distribution scenarios intention recognition spatial reasoning vision grounding