  Despite rapid developments in visual image-based road detection, robustly
identifying road areas in visual images remains challenging due to issues like
illumination changes and blurry images. To this end, LiDAR sensor data can be
incorporated to improve the visual image-based road detection, because LiDAR
data is less susceptible to visual noises. However, the main difficulty in
introducing LiDAR information into visual image-based road detection is that
LiDAR data and its extracted features do not share the same space with the
visual data and visual features. Such gaps in spaces may limit the benefits of
LiDAR information for road detection. To overcome this issue, we introduce a
novel Progressive LiDAR Adaptation-aided Road Detection (PLARD) approach to
adapt LiDAR information into visual image-based road detection and improve
detection performance. In PLARD, progressive LiDAR adaptation consists of two
subsequent modules: 1) data space adaptation, which transforms the LiDAR data
to the visual data space to align with the perspective view by applying
altitude difference-based transformation; and 2) feature space adaptation,
which adapts LiDAR features to visual features through a cascaded fusion
structure. Comprehensive empirical studies on the well-known KITTI road
detection benchmark demonstrate that PLARD takes advantage of both the visual
and LiDAR information, achieving much more robust road detection even in
challenging urban scenes. In particular, PLARD outperforms other
state-of-the-art road detection models and is currently top of the publicly
accessible benchmark leader-board.
