{'fraction_non_alphanumeric': 0.01936026936026936, 'fraction_numerical': 0.0016835016835016834, 'mean_word_length': 5.291005291005291, '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}}
Vehicle state prediction for outdoor autonomous high-speed off-road UGVs

This paper describes a method of vehicle state prediction for an autonomous high-speed off-road Unmanned Ground Vehicle (UGV). Effective vehicle state prediction will allow a UGV to plan its navigation such that states (such as vertical acceleration induced by the terrain roughness) never exceed a desired threshold. In this paper a model of an n-wheeled generic vehicle is used determine its dynamics. Using a known terrain input profile the vehicle's output states are predicted using the developed n-wheel model. Simulated results of this vehicle state prediction approach are presented, as well as experimental tests using a UGV platform called Loc8. The experimental results used a 3D point cloud to determine the terrain input profile. Methods from the literature are tested against the developed n-wheeled vehicle state prediction method. Results show this n-wheel technique presents both advantages and disadvantages in comparison with existing techniques. The proposed approach predicts the average absolute acceleration much closer to the measured average absolute acceleration than existing approaches.