Driving Everywhere with Large Language Model Policy Adaptation: A Powerful Tool for Seamless Traffic Rule Navigation
Основные понятия
LLaDA, a simple yet powerful tool, enables human drivers and autonomous vehicles to adapt their driving behavior to traffic rules in new locations by leveraging the zero-shot generalizability of large language models.
Аннотация
The paper presents LLaDA, a framework that adapts nominal motion plans by a human driver or an autonomous vehicle (AV) to local traffic rules of a given region. LLaDA consists of three key components:
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Traffic Rule Extractor (TRE): TRE uses the nominal execution plan and the description of the unexpected scenario to extract keywords from the traffic code of the current location, which are then used to extract the most relevant paragraphs.
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LLM Planner: The extracted traffic rule information from TRE is fed into a pre-trained large language model (GPT-4 in this case) to adapt the nominal plan accordingly.
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Modular Design: LLaDA's modular design allows it to be used for both human driver assistance and AV plan adaptation. For human drivers, LLaDA can provide natural language instructions on how to resolve unexpected scenarios based on local traffic rules. For AVs, LLaDA can interface with any motion planner capable of generating high-level semantic descriptions of its plan and a vision language model to adapt the plan to the rules of a new geographical location.
The paper demonstrates LLaDA's effectiveness through user studies, experiments on the nuScenes dataset, and extensive ablation studies. LLaDA is shown to significantly improve the performance of baseline planning approaches and provide useful instructions to human drivers in challenging traffic scenarios.
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arxiv.org
Driving Everywhere with Large Language Model Policy Adaptation
Статистика
Tourists are more susceptible to accidents that can sometimes result in injury or death.
Adapting to new driving rules and customs is difficult for both humans and AVs, and failure to do so can lead to unpredictable and unsafe behaviors.
LLMs have recently shown impressive zero- or few-shot adaptation capabilities in various fields, including vision and robotics.
Цитаты
"Adapting driving behavior to new environments, customs, and laws is a long-standing problem in autonomous driving, precluding the widespread deployment of autonomous vehicles (AVs)."
"Studies have shown that tourists are more susceptible to accidents [25, 26] that can sometimes result in injury or death [28]."
Дополнительные вопросы
How can LLaDA be extended to automatically provide plan corrections without requiring the human driver to explicitly query the system?
To enable LLaDA to automatically provide plan corrections, an unexpected scenario detector and translator can be developed. This detector would continuously monitor the driving environment and the execution of the nominal plan. If it detects any deviations or unexpected situations, it can trigger LLaDA to generate new instructions or corrections without the need for explicit human queries. By integrating this detector into the system, LLaDA can proactively adapt plans in real-time, enhancing the overall adaptability and safety of the driving system.
What are the potential challenges and limitations in developing an AV-specific foundation model that can provide more accurate scene descriptions compared to GPT-4V?
One of the main challenges in developing an AV-specific foundation model for scene descriptions is the need for extensive and diverse training data that accurately represents real-world driving scenarios. Gathering such data can be time-consuming and costly. Additionally, ensuring the model's generalizability across different driving environments and conditions poses a significant challenge. Moreover, designing a model that can effectively capture the nuances and complexities of driving scenes while maintaining interpretability and reliability is a non-trivial task. Furthermore, the computational complexity of training and deploying an AV-specific foundation model may present limitations in terms of real-time processing and system integration.
How can the safety and reliability of LLM-based systems like LLaDA be further improved through techniques like uncertainty quantification and formal verification?
To enhance the safety and reliability of LLM-based systems like LLaDA, techniques such as uncertainty quantification and formal verification can be employed. Uncertainty quantification methods can help assess the confidence and reliability of LLaDA's outputs, enabling the system to provide probabilistic predictions and identify areas of uncertainty. By incorporating formal verification techniques, the system can undergo rigorous testing and validation to ensure that it meets specified safety requirements and standards. Formal verification can help detect and mitigate potential vulnerabilities or errors in the system's decision-making processes, enhancing overall robustness and trustworthiness. By combining these techniques, LLaDA can achieve higher levels of safety and reliability in autonomous driving scenarios.
