@techreport{oai:ipsj.ixsq.nii.ac.jp:00239039,
 author = {齊藤, 充行 and 松本, 宗一郎 and 髙橋, 雄三 and 小作, 敏晴 and 辻, 勝宏 and Mitsuyuki, Saito and Soichiro, Matsumoto and Yuzo, Takahashi and Toshiharu, Kosaku and Katsuhiro, Tsuji},
 issue = {8},
 month = {Sep},
 note = {Model-based control, which has attracted attention in recent years in research on autonomous driving, requires a simple vehicle model that can accurately represent vehicle behavior. In this study, in order to estimate vehicle behavior with high accuracy in driving environments such as snowy roads and unpaved roads, we focus on modeling errors (position error, azimuth angle error, and sideslip angle error) that occur in a geometric two-wheel model, propose a method to learn and estimate these using a threelayer neural network, and demonstrate its usefulness through simulation experiments., Model-based control, which has attracted attention in recent years in research on autonomous driving, requires a simple vehicle model that can accurately represent vehicle behavior. In this study, in order to estimate vehicle behavior with high accuracy in driving environments such as snowy roads and unpaved roads, we focus on modeling errors (position error, azimuth angle error, and sideslip angle error) that occur in a geometric two-wheel model, propose a method to learn and estimate these using a threelayer neural network, and demonstrate its usefulness through simulation experiments.},
 title = {車体横すべり角を考慮した予測車両走行モデリング},
 year = {2024}
}