  This paper presents an adaptive lookahead pure-pursuit lateral controller for
optimizing racing metrics such as lap time, average lap speed, and deviation
from a reference trajectory in an autonomous racing scenario. We propose a
greedy algorithm to compute and assign optimal lookahead distances for the
pure-pursuit controller for each waypoint on a reference trajectory for
improving the race metrics. We use a ROS based autonomous racing simulator to
evaluate the adaptive pure-pursuit algorithm and compare our method with
several other pure-pursuit based lateral controllers. We also demonstrate our
approach on a scaled real testbed using a F1/10 autonomous racecar. Our method
results in a significant improvement (20%) in the racing metrics for an
autonomous racecar.
