{'fraction_non_alphanumeric': 0.016973125884016973, 'fraction_numerical': 0.0, 'mean_word_length': 5.802884615384615, '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}}
An Abstracted Radar Measurement Model for Extended Object Tracking

The trend towards highly-automated driver assistance systems strongly depends on the performance of environmental perception systems. Increasingly, greater attention is paid to sensor data modeling, because more measurement information can be gained with a good understanding of the hardware. Radar sensors are cost-efficient and thus often used for kinematic object tracking. Current techniques for the utilization of high-resolution radar sensors for object extent estimation, usually model the spatial occurrence of radar detections using statistical approximations. Although providing fast calculation speeds, the obtainable accuracy is not sufficient, especially when low-cost hardware is used. In this work, we propose a radar model which is based on approximations of every functional abstraction layer of radar sensors, from the electromagnetic wave distribution to the digital signal processing. This approach allows an inherent and generic modeling of the spatial detection probability of the environment, regardless of its exact content and avoiding any complexity issues. Consequently, the interaction between measurements of multiple vehicles can be accurately modeled. Additionally, various details like the antenna diagram and underbody reflections are included. The final radar model is evaluated and compared to measurement data.