*1.2. Contributions*

The current study aims to develop an occlusion-aware path planner for enhancing the indoor infrared positioning accuracy of an autonomous vehicle system. The planner is expected to be optimal and fast without violating fundamental restrictions, such as collision-avoidance and kinematic constraints. In particular, we adopt the first-search-thenoptimize framework to combine search-based and optimization-based planners to find the global optimum. Both planners work within the Frenet frame, and thus time efficiency is enhanced. The optimizer is designed through trial and error; hence, the finally derived path is kinematically feasible within a real-world Cartesian frame.
