Dolphins: Multimodal Language Model for Driving
Yingzi Ma Yulong Cao Jiachen Sun Marco Pavone Chaowei Xiao
The quest for fully autonomous vehicles (AVs) capable of navigating complex
real-world scenarios with human-like understanding and responsiveness. In this
paper, we introduce Dolphins, a novel vision-language model architected to
imbibe human-like abilities as a conversational driving assistant. Dolphins is
adept at processing multimodal inputs comprising video (or image) data, text
instructions, and historical control signals to generate informed outputs
corresponding to the provided instructions. Building upon the open-sourced
pretrained Vision-Language Model, OpenFlamingo, we first enhance Dolphins's
reasoning capabilities through an innovative Grounded Chain of Thought (GCoT)
process. Then we tailored Dolphins to the driving domain by constructing
driving-specific instruction data and conducting instruction tuning. Through
the utilization of the BDD-X dataset, we designed and consolidated four
distinct AV tasks into Dolphins to foster a holistic understanding of intricate
driving scenarios. As a result, the distinctive features of Dolphins are
characterized into two dimensions: (1) the ability to provide a comprehensive
understanding of complex and long-tailed open-world driving scenarios and solve
a spectrum of AV tasks, and (2) the emergence of human-like capabilities
including gradient-free instant adaptation via in-context learning and error
recovery via reflection.