  This paper proposes a perception and path planning pipeline for autonomous
racing in an unknown bounded course. The pipeline was initially created for the
2021 evGrandPrix autonomous division and was further improved for the 2022
event, both of which resulting in first place finishes. Using a simple
LiDAR-based perception pipeline feeding into an occupancy grid based expansion
algorithm, we determine a goal point to drive. This pipeline successfully
achieved reliable and consistent laps in addition with occupancy grid algorithm
to know the ways around a cone-defined track with an averaging speeds of 6.85
m/s over a distance 434.2 meters for a total lap time of 63.4 seconds.
