Accurate lateral positioning from map data and road marking detection

• Accurate ego-localization using visual features from two lateral cameras.
• Digital map integrated as a powerful additional sensor.
• Road marking detection coupled with the map data in a fusion filter.
• Vehicle localization at an ego-lane level of accuracy.

We are witnessing the clash of two industries and the remaking of in-car market order, as the world of digital knowledge recently made a significant move toward the automotive industry. Mobile operating system providers are battling between each other to take over the in-vehicle entertainment and information systems, while car makers either line up behind their technology or try to keep control over the in-car experience. What is at stake is the map content and location-based services, two key enabling technologies of self-driving cars and future automotive safety systems. These content-based augmented geographic information systems (GIS) as well as Advanced Driver Assistance Systems (ADAS) require an accurate, robust, and reliable estimation of road scene attributes. Accurate localization of the vehicle is a challenging and critical task that natural GPS or classical filter (EKF) cannot reach. This paper proposes a new approach allowing us to give a first answer to the issue of accurate lateral positioning. The proposed approach is based on the fusion of 4 types of data: a GPS, a set of INS/odometer sensors, a road marking detection, and an accurate road marking map. The lateral road markings detection is done with the processing of two lateral cameras and provides an assessment of the lateral distance between the vehicle and the road borders. These information coupled with an accurate digital map of the road markings provide an efficient and reliable way to dramatically improve the localization obtained from only classical way (GPS/INS/Odometer). Moreover, the use of the road marking detection can be done only when the confidence is sufficiently high (punctual use). In fact, the vision processing and the map data can be used punctually only in order to update the classical localization algorithm. The temporary lack of vision data does not affect the quality of lateral positioning. In order to evaluate and validate this approach, a real test scenario was performed on Satory’s test track with real embedded sensors. It shows that the lateral estimation of the ego-vehicle positioning is performed with a sub-decimeter accuracy, high enough to be used in autonomous lane keeping, and land-based mobile mapping.