| Title |
Autonomous Refueling Strategies using Vehicle-to-Infrastructure Communication in Smart Cities |
| ID_Doc |
44442 |
| Authors |
Carr, K; Rojas, J; Chen, X |
| Title |
Autonomous Refueling Strategies using Vehicle-to-Infrastructure Communication in Smart Cities |
| Year |
2019 |
| Published |
|
| DOI |
10.1109/MASSW.2019.00022 |
| Abstract |
Recently self-driving vehicles have attracted tremendous attention from all walks of life. A problem facing self-driving vehicles is when to stop for gas. In this paper, we study the autonomous refueling strategies using vehicle-to-infrastructure communication in smart cities. We set three goals for our strategies: not to slop too late, nor too early, and get relatively cheap gas. To satisfy these goals, we relate our problem to the Gusein-Zade's version of the secretary problem and provide a solution framework where we divide the distance a vehicle can travel front a full lank to an empty tank into Density Observation Section, Secretary Section, and the Critical Section. To predict the number of gas stations a vehicle will encounter in the future, we use the Constant Density Approximation (CDA) method and the Machine Learning (ML) method. Simulation mulls comparing the proposed (1)A and ML algorithms with the ground truth algorithm show that both perform nearly as well as the ground truth in satisfying all three goals. |
| Author Keywords |
secretary problem; self-driving vehicle; smart city; stopping rule; V21 |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000768255900014 |
| WoS Category |
Computer Science, Artificial Intelligence; Remote Sensing; Telecommunications |
| Research Area |
Computer Science; Remote Sensing; Telecommunications |
| PDF |
|