Name
Abstract | ||
|---|---|---|
Advanced Driver Assistance Systems require a tremendous amount of sensor information to support the driver's comfort and safety. In particular, systems that provide (good) route options to a vehicle rely on information, such as traffic jams and road blockages, which is sensed by other (possibly distant) vehicles and distributed by a central server. This information is clearly dynamic and may be invalid by the time the vehicle arrives at the affected location. In this work, we develop an innovative approach to determine optimal routes (minimizing the costs like travel-time to their destination) for vehicles whose original route is adversely impacted by a (severe) road event. A set of recursive equations is developed that yields the optimal decision for each vehicle at each decision-point. Simulations show that our approach adapts to the considered event and finds routes of similar quality as a full-knowledge approach with limited communication overhead. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LCN44214.2019.8990691 | 2019 IEEE 44th Conference on Local Computer Networks (LCN) |
Keywords | DocType | ISSN |
demand-driven,vehicular networks | Conference | 0742-1303 |
ISBN | Citations | PageRank |
978-1-7281-1029-5 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tobias Meuser | 1 | 0 | 0.34 |
| I. Stavrakakis | 2 | 56 | 8.94 |
| Antonio Fernández Anta | 3 | 1 | 1.37 |