Green Light Optimized Speed Advisory (GLOSA) with Traffic Preview
- 提供方法
- 版元よりダウンロードリンクを連絡
- 形態
- 価格
- 一般価格(税込)：￥6,600 会員価格(税込)：￥5,280
- 文献・情報種別
- SAE Paper
No.2022-01-0152
- 発行年月
- 2022年 3月
- 出版社
- SAE International
- 言語
- 英語
- イベント
- WCX SAE World Congress Experience 2022
書誌事項
| 著者（英） | 1) Mustafa Ridvan Cantas, 2) Gopichandra Surnilla, 3) Martin Sommer |
|---|---|
| 勤務先（英） | 1) Ohio State University, 2) Ford Motor Company, 3) Ford-Werke GmbH |
| 抄録（英） | By utilizing the vehicle to infrastructure communication, the conventional Green Light Optimized Speed Advisory (GLOSA) applications give speed advisory range for drivers to travel to pass at the green light. However, these systems do not consider the traffic between the ego vehicle and the traffic light location, resulting in inaccurate speed advisories. Therefore, the driver needs to intuitively adjust the vehicle's speed to pass at the green light and avoid traffic in these scenarios. Furthermore, inaccurate speed advisories may result in unnecessary acceleration and deceleration, resulting in poor fuel efficiency and comfort. To address these shortcomings of conventional GLOSA, in this study, we proposed the utilization of collaborative perception messages shared by smart infrastructures to create an enhanced speed advisory for the connected vehicle drivers and automated vehicles. Two different algorithms were designed by utilizing the available traffic preview (Signal Phase and Timing (SPAT), MAP, and Collaborative Perception Messages), predicted traffic preview from these messages, and measurements from onboard range sensors. While in the first algorithm, the vehicle is controlled with a rule-based approach, a reinforcement learning-based approach is used in the second algorithm. The designed algorithms are then simulated in a simulation environment created in a MATLAB Simulink. Our simulation results demonstrated the effectiveness of the developed algorithms with better fuel efficiency performance and more comfortable ride performance. 翻訳 |
