Autonomous driving for motorcycles has received comparatively little attention, despite the extensive research on four-wheeled vehicles. Motorcycles pose unique challenges due to their inherently wide ranges of roll and pitch dynamics, which can significantly influence accurate vehicle localization. In this work, we investigate the necessity of incorporating roll and pitch angles in motorcycle localization models, analyzing how Global Navigation Satellite System (GNSS) output frequency and antenna position influence their performance within Kalman Filter based algorithms. First, we present a formal analysis of the impact of roll and pitch angles on both model integration and output computation as a function of GNSS output frequency and antenna location. Then, we provide a quantitative evaluation using real experimental data to validate the theoretical findings. The results provide both methodological and practical insights into vehicle modeling for achieving high-accuracy, high-rate localization for two-wheeled vehicles.
Radrizzani et al. (Mon,) studied this question.