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Can LVLMs Obtain a Driver's License? A Benchmark Towards Reliable AGI for Autonomous Driving (2409.02914v1)

Published 4 Sep 2024 in cs.CV

Abstract: Large Vision-LLMs (LVLMs) have recently garnered significant attention, with many efforts aimed at harnessing their general knowledge to enhance the interpretability and robustness of autonomous driving models. However, LVLMs typically rely on large, general-purpose datasets and lack the specialized expertise required for professional and safe driving. Existing vision-language driving datasets focus primarily on scene understanding and decision-making, without providing explicit guidance on traffic rules and driving skills, which are critical aspects directly related to driving safety. To bridge this gap, we propose IDKB, a large-scale dataset containing over one million data items collected from various countries, including driving handbooks, theory test data, and simulated road test data. Much like the process of obtaining a driver's license, IDKB encompasses nearly all the explicit knowledge needed for driving from theory to practice. In particular, we conducted comprehensive tests on 15 LVLMs using IDKB to assess their reliability in the context of autonomous driving and provided extensive analysis. We also fine-tuned popular models, achieving notable performance improvements, which further validate the significance of our dataset. The project page can be found at: \url{https://4dvlab.github.io/project_page/idkb.html}

Citations (1)

Summary

  • The paper presents IDKB, a comprehensive dataset combining real-world driving manuals, test data, and simulated scenarios to fill LVLMs' knowledge gaps.
  • It evaluates 15 LVLMs using MCQ and QA tasks, revealing that proprietary models excel and that fine-tuning open-source models significantly boosts performance.
  • The findings underscore that explicit driving laws, techniques, and crisis management are crucial for improving autonomous driving safety and reliability.

An Expert Overview of "Can LVLMs Obtain a Driver's License? A Benchmark Towards Reliable AGI for Autonomous Driving"

The paper "Can LVLMs Obtain a Driver's License? A Benchmark Towards Reliable AGI for Autonomous Driving" presents an in-depth paper on enhancing the applicability of Large Vision-LLMs (LVLMs) in the domain of autonomous driving. The authors introduce the Intelligent Driving Knowledge Base (IDKB), a comprehensive dataset designed to address the gap in domain-specific driving knowledge within LVLMs. This dataset includes over one million data entries from 15 countries and encompasses driving laws, traffic rules, driving techniques, and crisis management skills.

Driving Knowledge Dataset

The authors highlight the limitations of existing vision-language driving datasets, which primarily focus on scene understanding and decision-making. These datasets fail to incorporate explicit guidance on traffic rules and driving skills. To bridge this gap, IDKB combines real-world data (driving handbooks and test questions) and synthetic data (simulated driving scenarios using the CARLA simulator).

Dataset Composition and Construction

The IDKB dataset is notable for its diverse data sources, covering:

  1. Driving Handbooks: Text data from driving manuals, which provide foundational driving knowledge.
  2. Driving Test Data: A collection of multiple-choice and short-answer questions from driving tests, which reinforce and assess understanding.
  3. Driving Road Data: Synthetic data from the CARLA simulator, representing practical driving scenarios with varied weather, lighting, and traffic conditions.

The authors also employed data augmentation techniques using GPT-4o to enhance the diversity and scale of the dataset, ensuring high-quality entries through a two-step verification process.

Evaluation of LVLMs

The paper evaluates 15 representative LVLMs using the IDKB dataset. These models vary in parameter size, visual encoders, and underlying LLMs. The evaluation consists of multiple-choice questions (MCQ) and question-and-answer (QA) tasks, derived from both driving test data and driving road data.

Key Findings

  1. Overall Performance: Proprietary LVLMs like GPT-4o and Gemini-1.5-flash show superior performance across most metrics. Among open-source models, XComposer2 performs best.
  2. Data Type Analysis: LVLMs generally perform better on driving road data (traffic sign recognition) compared to driving test data (broader driving knowledge and regulations).
  3. Instruction Following: Proprietary models exhibit higher adherence to instruction-following tasks, indicating better alignment with specified formats.
  4. Improvement Through Fine-Tuning: Fine-tuning open-source LVLMs with the IDKB dataset leads to significant performance improvements, demonstrating the dataset's value in enhancing driving knowledge.

Implications and Future Developments

The results indicate that explicit, structured driving knowledge is crucial for enhancing the practical application of LVLMs in autonomous driving. The IDKB dataset, by providing a comprehensive foundation of driving regulations and practical scenarios, significantly boosts the performance of fine-tuned models.

Speculative Applications

The incorporation of IDKB in LVLM training promises several advancements in AI-driven autonomous driving:

  1. Enhanced Safety: Better understanding of driving laws and techniques reduces the likelihood of accidents.
  2. Improved Navigation: Comprehensive knowledge of traffic signs and rules allows for more accurate and reliable navigation.
  3. Regulatory Compliance: Models fine-tuned with IDKB will be better equipped to handle region-specific traffic regulations, facilitating wider deployment of autonomous vehicles across different countries.

Conclusion

The paper provides a significant contribution to the autonomous driving research field by addressing the critical gap in domain-specific knowledge within LVLMs. The IDKB dataset proves to be a valuable resource for fine-tuning vision-LLMs, ensuring they can perform more reliably in the highly regulated and safety-critical domain of driving. This work sets a solid foundation for further research and development in the quest for robust and reliable autonomous driving systems.

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