{'fraction_non_alphanumeric': 0.010514018691588784, 'fraction_numerical': 0.0, 'mean_word_length': 5.6953125, '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}}
An Hybrid Approach to Improve the Performance of Encoder-Decoder Architectures for Traversability Analysis in Urban Environments

Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This paper proposes a hybrid approach that combines geometric and appearance features for training Deep Encoder-Decoder architectures to detect the traversability score in real urban contexts. The proposed approach has been tested with two Deep Learning architectures on a public dataset of outdoor driving scenarios. Thanks to our approach, we are able to reach high levels of accuracy in detecting the correct traversability score in environments of highly variable complexity. This demonstrates the effectiveness and robustness of the proposed method.