{'fraction_non_alphanumeric': 0.016170212765957447, 'fraction_numerical': 0.0, 'mean_word_length': 5.157068062827225, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Robust Look-ahead Distance Tuning Strategy for the Geometric Path Tracking Controllers

Geometric path tracking controllers are very popular to be implemented in the autonomous vehicles because of its simple implementation and validation in the practical applications. However, it also suffered from the sensitivity to the selections of the look-ahead distance. Therefore, reasonable tuning strategy of the look-ahead distance is very important to the application of the geometric controllers. This paper proposed a more robust tuning strategy for all the geometric controllers based on the careful analysis of the previous methods. Instead of utilizing as much as possible elements, we just want to make use of only two variables, the distance from the current location to the reference path and the changing rate of it. Fuzzy logic was utilized to combine these two variables to tune the look-ahead distance. We will show that we just use two variables, but we actually consider another two variables’ affections indirectly. Lastly, we will add our strategy to two different classical geometric controllers and compare the relative benefits of previous tuning strategies.