This book describes an effective decision-making and planning architecture for enhancing the navigation capabilities of automated
vehicles in the presence of non-detailed, open-source maps. The system involves dynamically obtaining road corridors from
map information and utilizing a camera-based lane detection system to update and enhance the navigable space in order to address
the issues of intrinsic uncertainty and low-fidelity. An efficient and human-like local planner then determines, within a
probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal
and lateral accelerations. LiDAR-based perception is then used to identify the driving scenario, and subsequently re-plan
the trajectory, leading in some cases to adjustment of the high-level route to reach the given destination. The method has
been validated through extensive theoretical and experimental analyses, which are reported here in detail.