2012
S Noth; J Edelbrunner; I Iossifidis
An integrated architecture for the development and assessment of ADAS Proceedings Article
In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2012, ISBN: 9781467330640.
Abstract | Links | BibTeX | Tags: ADAS, Autonomous Driving, Machine Learning
@inproceedings{Noth2012a,
title = {An integrated architecture for the development and assessment of ADAS},
author = {S Noth and J Edelbrunner and I Iossifidis},
doi = {10.1109/ITSC.2012.6338805},
isbn = {9781467330640},
year = {2012},
date = {2012-01-01},
booktitle = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
abstract = {Advanced Driver Assistant Systems act, by definition in natural, often poorly structured, environments and are supposed to closely interact with human operators. Both, natural environments as well as human behaviour have no inherent metric and can not be modelled/measured in the classical way physically plausibly behaving systems are described. textcopyright 2012 IEEE.},
keywords = {ADAS, Autonomous Driving, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Advanced Driver Assistant Systems act, by definition in natural, often poorly structured, environments and are supposed to closely interact with human operators. Both, natural environments as well as human behaviour have no inherent metric and can not be modelled/measured in the classical way physically plausibly behaving systems are described. textcopyright 2012 IEEE.
2011
Ürün Dogan; Johann Edelbrunner; Ioannis Iossifidis
Autonomous driving: A comparison of machine learning techniques by means of the prediction of lane change behavior Proceedings Article
In: 2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011, pp. 1837–1843, 2011, ISSN: 01962892.
Abstract | Links | BibTeX | Tags: ADAS, Autonomous Driving, lane change prediction, Machine Learning
@inproceedings{Dogan2011a,
title = {Autonomous driving: A comparison of machine learning techniques by means of the prediction of lane change behavior},
author = {Ürün Dogan and Johann Edelbrunner and Ioannis Iossifidis},
doi = {10.1109/ROBIO.2011.6181557},
issn = {01962892},
year = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011},
pages = {1837–1843},
abstract = {In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the data acquired from human drivers in the traffic simulator NISYS TRS1, we trained a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able to predict lane changes up to 1.5 sec in beforehand.},
keywords = {ADAS, Autonomous Driving, lane change prediction, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the data acquired from human drivers in the traffic simulator NISYS TRS1, we trained a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able to predict lane changes up to 1.5 sec in beforehand.