{'fraction_non_alphanumeric': 0.009472259810554804, 'fraction_numerical': 0.0, 'mean_word_length': 5.166666666666667, '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}}
Probabilistic modeling of sensor properties in generic fusion systems for modern driver assistance systems

Modern driver assistance and safety systems need a reliable and precise description of the environment. Fusing the measurement data of two or more sensors can improve the performance of the perception system. A generic fusion system which is independent of the attached sensors could be reused in multiple fusion systems and sensor combinations. This could be very helpful because sensor data fusion is a demanding and complex task. In this contribution, we present the algorithmic basics for a generic fusion system, detailed ways on how to model sensor specific properties and which benefits we can achieve by using these models.