{'fraction_non_alphanumeric': 0.014787430683918669, 'fraction_numerical': 0.0, 'mean_word_length': 4.983425414364641, '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}}
Estimating the reliability of georeferenced lane markings for map-aided localization

Maps can greatly improve vehicle localization using perception sensors that detect features georeferenced in the map. This relies on two assumptions. Firstly, the detected features and the elements of the map have to be correctly associated. Secondly, the features of the map have to be accurately referenced. In this paper, solutions regarding these issues are presented. The case study of localization using a camera detecting road markings is considered. A Kalman smoothing process is used to obtain the best possible estimate of the trajectory that enables to evaluate the reliability of markings stored in the map. A likelihood maximization technique is used to best associate the observed markings to those referenced in the map. By using these two methods, map errors are detected after a first passage in an area and can be mitigated in later passes. Experimental results are reported to evaluate the performance of this approach. It is shown that mapping errors can be correctly handled.