Comprehensive Evaluation of Large Vision-Language Models on Real-World Self-Driving Corner Cases

Core Concepts

Large Vision-Language Models (LVLMs) have significant potential to enhance interpretable autonomous driving, but current evaluations primarily focus on common scenarios, lacking quantifiable assessment in severe road corner cases.

Abstract

The paper introduces CODA-LM, a novel benchmark for systematically evaluating LVLMs on real-world self-driving corner cases. CODA-LM comprises three key tasks: general perception, regional perception, and driving suggestions. For general perception, the benchmark requires LVLMs to describe all salient road objects and explain their impact on driving behavior. The regional perception task focuses on location-aware understanding, where LVLMs need to describe and explain the effect of specific corner case objects. The driving suggestions task evaluates the models' ability to provide optimal driving advice based on the perceived environment. The authors demonstrate that using powerful language models (LLMs) as judges can effectively evaluate LVLMs, showing stronger consistency with human preferences compared to using the LVLM itself as the judge. Extensive experiments on both open-sourced and commercial LVLMs reveal that even the state-of-the-art GPT-4V struggles to handle road corner cases well, suggesting we are still far from a robust LVLM-powered intelligent driving agent. The authors hope CODA-LM can serve as a catalyst to promote the future development of reliable and interpretable autonomous driving systems.

Stats

Large commercial construction vehicles are typically heavier than ordinary vehicles, with longer braking distances and potential to block the line of sight. Concrete mixer trucks are much larger and heavier, affecting maneuverability and stopping distance, and may occupy more of the lane, creating blind spots. Vulnerable road users like cyclists represent a safety risk that requires the ego car to be extra cautious.

Quotes

"The presence of this vehicle indicates that there may be construction nearby or on the route. Due to its large size, it is heavier than ordinary vehicles, and the braking distance is longer and may block the line of sight." "Concrete mixer trucks are typically much larger and heavier, affecting maneuverability and stopping distance. Due to their large size, these trucks may occupy more of the lane, increasing the risk of collision." "Cyclists represent vulnerable road users, requiring the ego car to be careful and ensure their safety as well as that of the car."

Key Insights Distilled From

Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases

by Yanze Li,Wen... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10595.pdf

Deeper Inquiries

How can LVLMs be further improved to handle a wider range of corner cases in autonomous driving, beyond the scenarios covered in CODA-LM?

LVLMs can be enhanced to handle a broader spectrum of corner cases in autonomous driving by incorporating several key strategies: Diverse Training Data: LVLMs can benefit from training on a more extensive and diverse dataset that includes a wider range of corner cases. This can help the models learn to generalize better and adapt to novel scenarios. Fine-tuning on Corner Cases: Specific fine-tuning on corner case scenarios can help LVLMs improve their understanding and decision-making in challenging situations. By focusing on these cases during training, the models can learn to navigate complex driving scenarios more effectively. Multi-Modal Fusion: Integrating multiple modalities such as lidar, radar, and sensor data along with vision and language inputs can provide a more comprehensive understanding of the environment. This multi-modal fusion can enhance the models' perception and reasoning capabilities in corner cases. Contextual Understanding: LVLMs can be improved by enhancing their contextual understanding of the driving environment. This includes considering temporal information, spatial relationships, and dynamic changes in the scene to make more informed decisions in corner cases. Robustness and Safety: Ensuring that LVLMs prioritize safety and robustness in their decision-making processes is crucial. Models should be designed to handle uncertainty, unexpected events, and edge cases with a focus on safety-critical scenarios.

What are the potential limitations of using text-only LLMs as judges for evaluating LVLMs, and how can this approach be further refined?

Using text-only LLMs as judges for evaluating LVLMs may have some limitations, including: Lack of Visual Understanding: Text-only LLMs may not have the ability to comprehend visual information, which is essential for evaluating vision-language models in tasks like autonomous driving. This limitation can impact the accuracy and relevance of their judgments. Semantic Misinterpretation: Text-only LLMs may struggle with nuanced semantic understanding, leading to potential misinterpretation of complex scenarios. This can result in inaccurate evaluations of LVLMs' performance in real-world applications. Limited Multimodal Assessment: Text-only LLMs may not capture the full spectrum of multimodal interactions present in autonomous driving tasks. Evaluating LVLMs solely based on text inputs may overlook crucial visual cues and context that impact decision-making. To refine this approach, several strategies can be implemented: Hybrid Models: Incorporating multimodal models that combine text and visual inputs can provide a more comprehensive evaluation framework. Hybrid models can leverage the strengths of both text and vision models for a more holistic assessment. Fine-tuning on Evaluation Tasks: Fine-tuning text-only LLMs on specific evaluation tasks related to autonomous driving can enhance their understanding and judgment capabilities in this domain. Task-specific training can improve the accuracy and relevance of their assessments. Ensemble Approaches: Utilizing ensemble methods that combine judgments from multiple text-only LLMs can help mitigate individual model biases and errors. Aggregating judgments from diverse models can lead to more robust and reliable evaluations.

How can the insights from CODA-LM be leveraged to develop more robust and interpretable autonomous driving systems that can reliably operate in complex real-world environments?

The insights from CODA-LM can be instrumental in enhancing the development of autonomous driving systems in the following ways: Model Improvement: By analyzing the performance of LVLMs on corner cases, developers can identify weaknesses and areas for improvement in existing models. This feedback can guide the refinement of models to better handle complex real-world scenarios. Safety and Reliability: Understanding how LVLMs perform in challenging driving situations can help prioritize safety and reliability in autonomous systems. Insights from CODA-LM can inform the design of robust systems that can operate safely in diverse environments. Interpretability: By evaluating LVLMs on tasks like general perception, regional perception, and driving suggestions, developers can enhance the interpretability of autonomous driving systems. Clear explanations and reasoning provided by LVLMs can improve trust and transparency in their decision-making processes. Adaptation to Edge Cases: Insights from CODA-LM can help autonomous systems adapt to edge cases and unexpected scenarios that are not commonly encountered. By training models on a diverse range of corner cases, systems can be better prepared for real-world challenges. Continuous Learning: Leveraging the insights from CODA-LM, developers can implement continuous learning strategies to update and refine autonomous driving systems over time. This iterative approach ensures that systems remain adaptive and responsive to evolving road conditions and challenges.