THMA: Tencent HD Map AI System for Creating HD Map Annotations
Kun Tang, Xu Cao, Zhipeng Cao, Tong Zhou, Erlong Li, Ao Liu, Shengtao, Zou, Chang Liu, Shuqi Mei, Elena Sizikova, Chao Zheng

TL;DR
The paper presents THMA, an AI system that automates and accelerates the creation of high-definition maps for autonomous vehicles, significantly reducing manual effort and increasing efficiency.
Contribution
Introduction of THMA, an end-to-end AI-based active learning system for large-scale HD map annotation, achieving high accuracy and efficiency in real-world deployment.
Findings
Automatically labels over 90% of Tencent Map HD data.
Produces more than 30,000 km of HD maps daily.
Speeds up traditional HD map labeling by over ten times.
Abstract
Nowadays, autonomous vehicle technology is becoming more and more mature. Critical to progress and safety, high-definition (HD) maps, a type of centimeter-level map collected using a laser sensor, provide accurate descriptions of the surrounding environment. The key challenge of HD map production is efficient, high-quality collection and annotation of large-volume datasets. Due to the demand for high quality, HD map production requires significant manual human effort to create annotations, a very time-consuming and costly process for the map industry. In order to reduce manual annotation burdens, many artificial intelligence (AI) algorithms have been developed to pre-label the HD maps. However, there still exists a large gap between AI algorithms and the traditional manual HD map production pipelines in accuracy and robustness. Furthermore, it is also very resource-costly to build…
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Taxonomy
TopicsData Management and Algorithms · Context-Aware Activity Recognition Systems · Robotics and Sensor-Based Localization
