WEKO3
アイテム
Vehicle Trajectory Data Reduction in Maneuver Coordination Message by Polynomial Approximation
https://ipsj.ixsq.nii.ac.jp/records/220449
https://ipsj.ixsq.nii.ac.jp/records/220449d1ce2959-e74a-4d74-ada3-574ffe973299
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-DPSWS2022017.pdf (769.1 kB)
|
©2022 Information Processing Society Japan
|
|
| オープンアクセス | ||
| Item type | Symposium(1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2022-10-17 | |||||||||
| タイトル | ||||||||||
| タイトル | Vehicle Trajectory Data Reduction in Maneuver Coordination Message by Polynomial Approximation | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | manuever cordination, trajectory data reduction | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||||||
| 資源タイプ | conference paper | |||||||||
| 著者所属 | ||||||||||
| 慶應義塾大学理工学研究科情報工学専修 | ||||||||||
| 著者所属 | ||||||||||
| 慶應義塾大学理工学部情報工学科 | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Dept. of Information and Computer Science, Keio University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Dept. of Information and Computer Science, Keio University | ||||||||||
| 著者名 |
國部, 匡志 重野 寛
× 國部, 匡志 重野 寛
|
|||||||||
| 著者名(英) |
Masashi, Kunibe
× Masashi, Kunibe
× Hiroshi, Shigeno
|
|||||||||
| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | In this paper, we propose the vehicle trajectory data reduction in Maneuver Coordination Messages (MCMs) by polynomial approximation. For the traffic safety and efficiency, it is assumed that Connected and Automated Vehicles (CAVs) exchange their planned trajectories and desired trajectories among them with MCMs by vehicle-to-everything transmission. However, the size of MCM becomes large as the number of waypoints in the trajectory increases. Therefore, the MCM transmission has the potential to cause wireless congestion. To mitigate the wireless congestion, in the proposed method, the CAVs reduce the MCM size by approximating their trajectories by polynomials. Specifically, they include the polynomial coefficients into MCMs instead of their trajectories. They approximate their trajectories by the polynomial approximation such that the position errors between the original trajectories and approximated ones become less than the acceptable error. In the simulation, we have evaluated the MCM size by changing the trajectory length, number of polynomial coefficients, and vehicle heading based on the kinematic bicycle model. The simulation result shows that the proposed method reduces the MCM size by 59.47% compared with the original MCM size even when the trajectory is frequently curved. |
|||||||||
| 書誌情報 |
第30回マルチメディア通信と分散処理ワークショップ論文集 p. 119-125, 発行日 2022-10-17 |
|||||||||
| 出版者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
