This AI Research from China Provides an Exhaustive Evaluation of the Latest SOTA Visual Language Model GPT-4V(ision) and Its Application in Autonomous Driving Scenarios

A team of researchers from Shanghai Artificial Intelligence Laboratory, GigaAI, East China Normal University, The Chinese University of Hong Kong, WeRide.ai evaluates the applicability of GPT-4V(ision), a Visual Language Model, in autonomous driving scenarios. GPT-4V demonstrates superior performance in scene understanding and causal reasoning, showcasing potential in handling diverse scenarios and recognizing intentions. Challenges persist in direction discernment and traffic light recognition, emphasizing the need for further research and development. The study reveals GPT-4V’s promising capabilities in real driving contexts while identifying specific areas for improvement.

The research assesses GPT-4V(ision) in autonomous driving contexts, examining its scene understanding, decision-making, and driving capabilities. Comprehensive tests demonstrate GPT-4V’s superior performance in scene understanding and causal reasoning compared to existing systems. Despite strengths, challenges persist in tasks like direction discernment and traffic light recognition, urging further research and development to enhance autonomous driving capabilities. The findings underscore GPT-4V’s potential while emphasizing the necessity for addressing specific limitations through continued exploration and improvement efforts.

Traditional approaches to autonomous vehicles face challenges in accurately perceiving objects and understanding the intentions of other traffic participants. LLMs show promise in addressing these issues, but their application in autonomous driving is limited by their inability to process visual data. The emergence of GPT-4V presents an opportunity to enhance scene understanding and causal reasoning in autonomous driving. The study aims to comprehensively evaluate GPT-4V’s capabilities in recognizing various conditions and making decisions in real driving situations, providing foundational insights for future research in autonomous driving.

The approach provides an exhaustive evaluation of the GPT-4V(ision) in the context of autonomous driving scenarios. Comprehensive tests assess GPT-4V’s capabilities in understanding driving scenes, making decisions, and acting as drivers. Tasks include basic scene recognition, complex causal reasoning, and real-time decision-making under various conditions. The evaluation employs a curated selection of images and videos from open-source datasets, CARLA simulation, and the internet.

GPT-4V performs better scene understanding and causal reasoning than current autonomous systems, demonstrating its potential in handling out-of-distribution scenarios, recognizing intentions, and making informed decisions in real driving contexts. Despite these strengths, challenges persist in direction discernment, traffic light recognition, vision grounding, and spatial reasoning. The evaluation suggests that GPT-4V’s capabilities surpass those of existing systems, providing foundational insights for future research in autonomous driving. 

The study thoroughly evaluates GPT-4V(ision) in autonomous driving scenarios, revealing its superior performance in scene understanding and causal reasoning compared to existing systems. GPT-4V demonstrates potential in handling out-of-distribution procedures, recognizing intentions, and making informed decisions in real driving contexts. Despite these strengths, challenges persist in direction discernment, traffic light recognition, vision grounding, and spatial reasoning. 

The research recognizes the necessity for additional research and development, specifically in addressing challenges related to direction discernment, traffic light recognition, vision grounding, and spatial reasoning tasks. It notes that the most recent version of GPT-4V may yield different responses compared to the test results presented in the current study.

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