Researchers from multiple universities and NVIDIA have developed Dolphins, a vision-language model for autonomous vehicles. Dolphins excel in providing driving instructions by combining language reasoning with visual understanding, exhibiting human-like features such as rapid learning and interpretability. The model addresses challenges in achieving full autonomy in vehicular systems and emphasizes the importance of computational efficiency.
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Introducing Dolphins: A Vision-Language Model for Autonomous Driving
Overview
A team of researchers from the University of Wisconsin-Madison, NVIDIA, the University of Michigan, and Stanford University have developed a new vision-language model (VLM) called Dolphins. Dolphins is a conversational driving assistant designed to address the complex driving scenarios faced by autonomous vehicles (AVs) and exhibit human-like features such as rapid learning, adaptation, error recovery, and interpretability during interactive conversations.
Practical Solutions and Value
Dolphins combine language model reasoning with visual understanding, excelling in in-context learning and handling varied video inputs. The model addresses the challenge of achieving full autonomy in vehicular systems, aiming to design AVs with human-like understanding and responsiveness in complex scenarios. Dolphins demonstrate advanced understanding, instant learning, and error recovery, emphasizing interpretability for trust and transparency. They excel in solving diverse autonomous vehicle tasks with human-like capabilities such as instant adaptation and error recovery.
Dolphins use OpenFlamingo and GCoT to enhance reasoning and ground VLMs in the AV context. They also create a multimodal in-context instruction tuning dataset for detailed conversation tasks. The model showcases impressive holistic understanding and human-like reasoning in intricate driving scenarios, excelling in interpretability and rapid adaptation.
Emphasizing the critical role of VLMs in enabling autonomous driving and unlocking full AI potential in AVs, Dolphins propose the development of customized and distilled versions of VLMs to balance computational demands with power efficiency. Continuous exploration and innovation are deemed essential for unlocking the full potential of AVs empowered by advanced AI capabilities like Dolphins.
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