{'fraction_non_alphanumeric': 0.011025358324145534, 'fraction_numerical': 0.006615214994487321, 'mean_word_length': 5.053333333333334, '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}}
Human Behavior Based Predictive Brake Assistance

Driver assistance systems have both the potential to alert the driver to critical situations and distract or annoy the driver if the driver is already aware of the situation. As systems attempt to preemptively warn drivers more and more in advance, this problem becomes exacerbated. We present a predictive braking assistance system that identifies not only the need for braking action, but also whether or not a braking action is being planned by the driver. Our system uses a Bayesian framework to determine the criticality of the situation by assessing (1) the probability that braking should be performed given observations of the vehicle and surround and (2) the probability that the driver intends to perform a braking action. We train and evaluate our system using over 22 hours of data collected from real driving scenarios with 28 different drivers