{'fraction_non_alphanumeric': 0.016233766233766232, 'fraction_numerical': 0.0, 'mean_word_length': 5.292517006802721, '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 driver adaptive lane departure warning system based on image processing and a fuzzy evolutionary technique

By applying fixed threshold values for a single lane departure information such as lateral offset, most lane departure warning systems have a difficulty in practical application. This paper proposes a method that can generate the driver adaptive lane departure warning model that can reduce driver disturbance factors, such as a frequently annoying alarm, etc. After a training period, the lane departure warning model is constructed by a fuzzy-evolutionary algorithm that can fuse the current and near future vehicle state information like the lateral offset and TLC (Time to Lane Crossing). After the departure model has been constructed; the driver merely selects an appropriate hazard level of lane departure warning. The proposed system has been developed and tested in HiLS (hardware in the loop simulation).