Name
Abstract | ||
|---|---|---|
A signal processing approach for modeling vehicle trajectory during lane changing driving is discussed. Because individual driving habits are not a deterministic process, we developed a stochastic method. The proposed model consists of two parts: a dynamic system represented by a hidden Markov model and a cognitive distance space derived from the range distance distribution. The first part models the local dynamics of vehicular movements and generates a set of probable trajectories. The second part selects an optimal trajectory by stochastically evaluating the distances from surrounding vehicles. From experimental evaluation, we show that the model can predict the vehicle trajectory at given traffic conditions with 17.6 m prediction error for two different drivers. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICASSP.2009.4959849 | Taipei |
Keywords | Field | DocType |
behavioural sciences,hidden Markov models,signal processing,stochastic processes,cognitive distance space,driving behavior,hidden Markov model,lane changing driving,signal processing approach,stochastic method,stochastic modeling,vehicle trajectory,Driving Behavior,Dynamic System,Sampling,hidden Markov model | Signal processing,Mean squared prediction error,Mathematical optimization,Control theory,Computer science,Simulation,Stochastic process,Sampling (statistics),Deterministic system,Hidden Markov model,Probability density function,Trajectory | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4244-2354-5 | 978-1-4244-2354-5 | 4 |
PageRank | References | Authors |
0.48 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshihiro Nishiwaki | 1 | 4 | 0.48 |
| Chiyomi Miyajima | 2 | 345 | 45.71 |
| Hidenori Kitaoka | 3 | 4 | 0.48 |
| Kazuya Takeda | 4 | 1301 | 195.60 |