This paper presents a robust hybrid approach to Predictive Lane Detection - PLD, which utilizes information from digital map to improve efficiency and accuracy to vision-based lane detector. Traditional approaches are mostly designed for well maintained and simple road conditions like motorway or interstate road with clear lane markers, to solve out the estimation problems of coming road shape as well as vehicle's position and ego-state, which however becomes ambiguous or unavailable in the complicated road environment and under difficult weather or illumination conditions. In this paper, the proposed approach refers to vehicle localization on digital map for road geometry estimation, which gives strong cues for vision-based detector to limit the search region of road candidates and suppress noise. In addition, other information from digital map like lane marker painting color and categories is utilized in the high level of data fusion on road geometry estimation. Real and synthesized road experiment results verified the effectiveness and efficiency of our approach.