This report describes a map construction and evaluation method based on lane-marker information for autonomous driving. Autonomous driving systems typically require digital high-definition (HD) maps to correct the current position of autonomous vehicles by using localization techniques. However, an HD map is usually costly to generate because it requires a special-purpose vehicle and mapping system with precise and expensive sensors. This report presents a map construction method that uses cost-efficient on-board cameras. We implement two types of map construction methods with two different cameras in terms of range and field of view and test their performances to determine the minimum sensor specification required for autonomous driving. This report also presents a constructed map evaluation method to determine the “usability” of the map for autonomous driving. Given that the system cannot obtain “true” positions of landmarks, the method judges whether the constructed map contains sufficient information for localization via the presented indices “lateral-distance error.” The methods are verified based on mapping and localization errors determined via manual driving tests. Furthermore, the smoothness of steering maneuvers is determined by conducting autonomous driving tests on a proving ground. The results reveal the necessary conditions of sensor requirements, i.e., the constant visibility of landmarks is one of the key factors for ego-localization to conduct autonomous driving.

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