Name
Abstract | ||
|---|---|---|
This paper proposes a framework to recognize driving intentions and to predict driving behaviors of lane changing on the highway by using externally sensable traffic data from the host-vehicle. The framework consists of a driving characteristic estimator and a driving behavior predictor. A driver's implicit driving characteristic information is uniquely determined and detected by proposed the online-estimator. Neural-network based behavior predictor is developed and validated by testing with the real naturalistic traffic data from Next Generation Simulation (NGSIM), which demonstrates the effectiveness in identifying the driving characteristics and transforming into accurate behavior prediction in real-world traffic situations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IVS.2019.8813987 | 2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19) |
Field | DocType | ISSN |
Computer science,Artificial intelligence,Change prediction,Machine learning,Estimator | Conference | 1931-0587 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Teawon Han | 1 | 0 | 0.34 |
| Junbo Jing | 2 | 0 | 0.68 |
| Ümit Özgüner | 3 | 1014 | 166.59 |