  Accuracy and time efficiency are two essential requirements for the
self-localization of autonomous vehicles. While the observation range
considered for simultaneous localization and mapping (SLAM) has a significant
effect on both accuracy and computation time, its effect is not well
investigated in the literature. In this paper, we will answer the question: How
far should a driverless car observe during self-localization? We introduce a
framework to dynamically define the observation range for localization to meet
the accuracy requirement for autonomous driving, while keeping the computation
time low. To model the effect of scanning range on the localization accuracy
for every point on the map, several map factors were employed. The capability
of the proposed framework was verified using field data, demonstrating that it
is able to improve the average matching time from 142.2 ms to 39.3 ms while
keeping the localization accuracy around 8.1 cm.
