{'fraction_non_alphanumeric': 0.01098901098901099, 'fraction_numerical': 0.0, 'mean_word_length': 5.6880733944954125, '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}}
Adaptive Kalman Filter with Linear Equality Road Constraints for Autonomous Vehicle Localization

This paper proposes a positioning method using an adaptive Kalman filter (AKF) with linear equality road constraints. The AKF algorithm used in vehicle localization can adaptively adjust the covariance matrices of both the system noise and the measurement noise based on the innovation sequence. Linear equality road constraints incorporated into AKF architecture can restrict the unconstrained position estimates to the constraint set via projection method and then give the final positioning results. Finally., two simulation cases based on segmented straight roads are provided to show the effectiveness of the proposed method.