A Low-Cost Solution for Automatic Lane-Level Map Generation Using Conventional In-Car Sensors

Abstract

Lane-level digital maps are crucial to advanced driver assistance systems (ADAS) and autonomous driving, since they can simplify driving tasks and enhance system performance by providing strong priors about the driving environment. However, the high cost of current map generation systems prevents their benefits to normal commercial cars as they usually depend on specialized sensors and need great manual postprocessing. In this paper, a low-cost solution is proposed for automatic generation of a precise lane-level map by using conventional sensors that have been already installed in contemporary cars. It mainly consists of two modules, i.e., road orthographic image generation and lane graph construction. First, the global map is divided into fixed local segments based on the road network topology. With the reference of the local map segments, the bird's eye view images of the road surface are accumulated by fusing GPS, INS, and visual odometry and subsequently integrated into synthetic orthographic images. Next, the driving lane information is extracted from the road orthographic images and a large amount of vehicle trajectories. Such information is then used to construct a lane graph of the map based on the sophisticated lane models we proposed without manual processing. Experiments show promising results of the automatic map generation of the real-world roads, which substantiated the effectiveness of the proposed approach. Such a system can offer increased value and promote the automation level for today's commercial cars without being supplemented additional sensors.