Autonomous vehicles are being developed rapidly in recent years. In advance implementation stages, many particular problems must be solved to bring this technology into the market place. This paper focuses on the problem of driving in snow and wet road surface environments. First, the quality of laser imaging detection and ranging (LIDAR) reflectivity decreases on wet road surfaces. Therefore, an accumulation strategy is designed to increase the density of online LIDAR images. In order to enhance the texture of the accumulated images, principal component analysis is used to understand the geometrical structures and texture patterns in the map images. The LIDAR images are then reconstructed using the leading principal components with respect to the variance distribution accounted by each eigenvector. Second, the appearance of snow lines deforms the expected road context in LIDAR images. Accordingly, the edge profiles of the LIDAR and map images are extracted to encode the lane lines and roadside edges. Edge matching between the two profiles is then calculated to improve localization in the lateral direction. The proposed method has been tested and evaluated using real data that are collected during the winter of 2016-2017 in Suzu and Kanazawa, Japan. The experimental results show that the proposed method increases the robustness of autonomous driving on wet road surfaces, provides a stable performance in laterally localizing the vehicle in the presence of snow lines, and significantly reduces the overall localization error at a speed of 60 km/h.