Traditional approaches to autonomous vehicles have faced challenges in accurately perceiving objects and understanding the intentions of other traffic participants. However, recent advancements in Language and Vision Models (LLMs) offer promise in addressing these issues. One such model is GPT-4V(ision), which aims to enhance scene understanding and causal reasoning in autonomous driving scenarios.
A team of researchers from Shanghai Artificial Intelligence Laboratory, GigaAI, East China Normal University, The Chinese University of Hong Kong, and WeRide.ai conducted a comprehensive evaluation of GPT-4V(ision) in the context of autonomous driving. The study focused on examining the model’s scene understanding, decision-making, and driving capabilities.
The evaluation encompassed a wide range of tasks, including basic scene recognition, complex causal reasoning, and real-time decision-making under various conditions. The researchers used a combination of curated images and videos from open-source datasets, CARLA simulation, and internet sources to ensure a comprehensive assessment.
The results of the evaluation demonstrated that GPT-4V outperformed existing autonomous systems in scene understanding and causal reasoning. The model showcased its potential in handling out-of-distribution scenarios, recognizing intentions, and making informed decisions in real driving contexts. However, challenges still persisted in certain areas such as direction discernment, traffic light recognition, vision grounding, and spatial reasoning.
Although GPT-4V showed promising capabilities, the researchers emphasized the need for continued research and development to address these limitations. They highlighted the importance of further exploring direction discernment, traffic light recognition, vision grounding, and spatial reasoning tasks to enhance the model’s performance.
This comprehensive evaluation of GPT-4V in autonomous driving scenarios provides foundational insights for future research in the field. It serves as a starting point for further exploration and improvement efforts to enhance the capabilities of autonomous vehicles.
What is GPT-4V(ision)?
GPT-4V(ision) is a Visual Language Model that aims to enhance scene understanding and causal reasoning in autonomous driving scenarios.
What were the strengths of GPT-4V in the evaluation?
GPT-4V demonstrated superior performance in scene understanding and causal reasoning compared to existing autonomous systems. It showcased potential in handling diverse scenarios, recognizing intentions, and making informed decisions in real driving contexts.
What were the challenges identified in the evaluation?
Challenges identified in the evaluation included direction discernment, traffic light recognition, vision grounding, and spatial reasoning. These areas require further research and development to enhance autonomous driving capabilities.
What sources were used in the evaluation?
The evaluation utilized a curated selection of images and videos from open-source datasets, CARLA simulation, and internet sources.
What is the significance of this evaluation?
This evaluation of GPT-4V(ision) provides foundational insights for future research in autonomous driving. It highlights the potential of GPT-4V while emphasizing the necessity for addressing specific limitations through continued exploration and improvement efforts.