Name
Abstract | ||
|---|---|---|
Human drivers are endowed with an inborn ability to put themselves in the position of other drivers and reason about their behaviors and intended actions. State-of-the-art driving assistance systems, on the other hand, are generally limited to physical models and ad-hoc safety rules. In order to drive safely amongst humans, autonomous vehicles require a high-level description of the state of traffic participants. This paper presents a probabilistic model for estimating and predicting the behavior of drivers immersed in traffic. The model is defined within a stochastic filtering framework and estimation and prediction are carried out with statistical inference techniques. The approach is validated with real data from a fleet of mining vehicles. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IVS.2011.5940407 | Intelligent Vehicles Symposium |
Keywords | Field | DocType |
Bayes methods,ad hoc networks,behavioural sciences computing,driver information systems,filtering theory,inference mechanisms,probability,Bayesian approach,ad-hoc safety rules,autonomous vehicles,drivers behavior prediction,driving assistance systems,driving behavior inference,high level description,probabilistic model,statistical inference techniques,stochastic filtering framework,Driver behavior,anticipatory driving,intelligent transportation systems,road safety,situational awareness,vehicle interaction | Situation awareness,Inference,Vehicle dynamics,Statistical model,Statistical inference,Artificial intelligence,Probabilistic logic,Intelligent transportation system,Engineering,Machine learning,Bayesian probability | Conference |
ISSN | ISBN | Citations |
1931-0587 | 978-1-4577-0890-9 | 6 |
PageRank | References | Authors |
0.48 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Agamennoni | 1 | 194 | 16.42 |
| Juan I. Nieto | 2 | 939 | 88.52 |
| Eduardo Mario Nebot | 3 | 171 | 16.84 |