  Prevalent solutions for Connected and Autonomous vehicle (CAV) mapping
include high definition map (HD map) or real-time Simultaneous Localization and
Mapping (SLAM). Both methods only rely on vehicle itself (onboard sensors or
embedded maps) and can not adapt well to temporarily changed drivable areas
such as work zones. Navigating CAVs in such areas heavily relies on how the
vehicle defines drivable areas based on perception information. Difficulties in
improving perception accuracy and ensuring the correct interpretation of
perception results are challenging to the vehicle in these situations. This
paper presents a prototype that introduces crowdsourcing trajectories
information into the mapping process to enhance CAV's understanding on the
drivable area and traffic rules. A Gaussian Mixture Model (GMM) is applied to
construct the temporarily changed drivable area and occupancy grid map (OGM)
based on crowdsourcing trajectories. The proposed method is compared with SLAM
without any human driving information. Our method has adapted well with the
downstream path planning and vehicle control module, and the CAV did not
violate driving rule, which a pure SLAM method did not achieve.
