Early Recognition of Maneuvers in Highway Traffic
- This paper presents an application of Bayesian networks where early recognition of traffic maneuver intention is achieved using features of lane change, representing the relative dynamics between vehicles on the same lane and the free space to neighbor vehicles back and front on the target lane. The classifiers have been deployed on the automotive target platform, which has severe constraints on time and space performance of the system. The test driving has been performed with encouraging results. Even earlier recognition is possible by considering the trend development of features, characterizing the dynamic driving process. The preliminary test results confirm feasibility.
Author: | Galia WeidlORCiD, Anders L. Madsen, Viacheslav Tereshchenko, Dietmar Kaspar, Gabi Breuel |
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URL: | https://www.researchgate.net/publication/300646375_Early_Recognition_of_Maneuvers_in_Highway_Traffic#fullTextFileContent |
DOI: | https://doi.org/10.1007/978-3-319-20807-7_48 |
Parent Title (English): | European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, July 2015 |
Document Type: | Conference Proceeding |
Language: | English |
Year of Completion: | 2015 |
Release Date: | 2023/12/06 |
GND Keyword: | Fahrerassistenzsystem |
Urheberrecht: | 1 |
Institutes: | Einrichtungen / Kompetenzzentrum Künstliche Intelligenz |
research focus : | Intelligent Systems / Artifical Intelligence and Data Science |
Licence (German): | ![]() |