{'fraction_non_alphanumeric': 0.015116279069767442, 'fraction_numerical': 0.0, 'mean_word_length': 5.888, '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}}
Performance bounds on change detection with application to manoeuvre recognition for advanced driver assistance systems

Recognising the intended manoeuvres of other traffic participants is a crucial task for situation interpretation in driver assistance and autonomous driving. While many works propose algorithms for (computationally feasible) inference, much less attention is paid to finding analytic upper performance bounds for these problems. This work studies the statistical properties of the optimal detector in a binary change detection problem, i.e. the Generalised Likelihood Ratio test. With analytic models of the best attainable receiver operating characteristic, the influence of system design parameters can be investigated without the need for empirical evaluation. Moreover, these bounds can be used to derive objective performance metrics.