Which Traffic Light Should You Look at? Automatically Associating Traffic Lights with Roads in High-Definition Map (Industrial Paper)

High-definition (HD) maps play an essential role in autonomous driving. However, producing HD map needs huge amount of manual annotations and is thus labor intensive and costly, which limits the widespread use of HD map. To improve the productivity and reduce cost, extensive studies have explored automation of HD map production. Existing studies primarily focus on constructing vectorized map elements from vehicle sensing images and point clouds. However, the follow-up procedure of extracting traffic semantics based on vectorized map elements is lack of study, though it is laborious and costly as well. In this paper, we focus on the automation of inferring traffic light controls for HD map production. To be specific, we aim at associating traffic lights with their controlled roads based on vectorized map data. This problem is not trivial in that: 1) the placement of traffic light contrastive to road varies considerably from scene to scene; 2) even if the placement of traffic lights and roads are similar, the road network layout has great influence on the traffic light controls. To tackle the above challenges, we propose a Heterogeneous Interaction model with Stacked Transformers (HIST) that learns representation from vectorized map elements and encodes contextual information via heterogeneous interactions among different types of map elements. We conduct extensive experiments in major cities of China to validate the efficacy of HIST. Results show HIST achieves accuracy ranging from 96.03% to 98.85% in different cities. We further deploy HIST on the HD map production line at AMAP. By incorporating a rule-based confidence system, the whole system achieves the performance with accuracy > 99.9% and automation rate > 85%, which meets the industrial level quality requirement and saves vast amount of human labor. © 2023 ACM.