Accurate address resolution plays a vital role in UAV delivery systems. Existing address resolution systems heavily rely on user-provided Point of Interest (POI) information. However, such information often lacks precision, making it challenging to obtain fine-grained details for further processing. In this paper, we present an end-to-end Language Model-based fine-grained Address Resolution framework (LMAR). Instead of solely relying on POI information, we introduce a language model to process the user input text information. Specifically, we start by collecting data and constructing two datasets, which are then used to fine-tune a pre-trained language model. Additionally, our pipeline incorporates pre-processing and post-processing modules to handle data processing and regularization. We combine the output of the language model with the POI information to perform a database match and derive the final outcome. To evaluate our proposed LMAR, we conduct offline and online experiments. In both offline and online testing, our proposed model achieves an overall performance of over 90% accuracy, while in the online pressure test, it achieves satisfactory performance, demonstrating its effectiveness and practicality. The proposed LMAR has passed the internal test and will be deployed into the Meituan UAV delivery system in the near future.
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A Language Model-Based Fine-Grained Address Resolution Framework in UAV Delivery System
Semantic Scholar · Computer Science · 2024
Abstract
Accurate address resolution plays a vital role in UAV delivery systems. Existing address resolution systems heavily rely on user-provided Point of Interest (POI) information. However, such information often lacks precision, making it challenging to obtain fine-grained details for further processing. In this paper, we present an end-to-end Language Model-based fine-grained Address Resolution framework (LMAR). Instead of solely relying on POI information, we introduce a language model to process the user input text information. Specifically, we start by collecting data and constructing two datasets, which are then used to fine-tune a pre-trained language model. Additionally, our pipeline incorporates pre-processing and post-processing modules to handle data processing and regularization. We combine the output of the language model with the POI information to perform a database match and derive the final outcome. To evaluate our proposed LMAR, we conduct offline and online experiments. In both offline and online testing, our proposed model achieves an overall performance of over 90% accuracy, while in the online pressure test, it achieves satisfactory performance, demonstrating its effectiveness and practicality. The proposed LMAR has passed the internal test and will be deployed into the Meituan UAV delivery system in the near future.