Publications
2011
Urun Dogan; Johann Edelbrunner; Ioannis Iossifidis
Autonomous Driving: A Comparison of Machine Learning Techniques by Measns of the Prediction of Lane Change Behavior Inproceedings
In: Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011), 2011.
BibTeX | Tags: driver information systems, feed forward neural network, lane change maneuvers, Machine Learning, recurrent neural network, support vector machines, traffic simulator
@inproceedings{Dogan2011b,
title = {Autonomous Driving: A Comparison of Machine Learning Techniques by Measns of the Prediction of Lane Change Behavior},
author = {Urun Dogan and Johann Edelbrunner and Ioannis Iossifidis},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011)},
keywords = {driver information systems, feed forward neural network, lane change maneuvers, Machine Learning, recurrent neural network, support vector machines, traffic simulator},
pubstate = {published},
tppubtype = {inproceedings}
}
Urun Dogan; Johann Edelbrunner; Ioannis Iossifidis
Autonomous Driving: A Comparison of Machine Learning Techniques by Measns of the Prediction of Lane Change Behavior Inproceedings
In: Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011), 2011.
BibTeX | Tags: driver information systems, feed forward neural network, lane change maneuvers, Machine Learning, recurrent neural network, support vector machines, traffic simulator
@inproceedings{Dogan2011bb,
title = {Autonomous Driving: A Comparison of Machine Learning Techniques by Measns of the Prediction of Lane Change Behavior},
author = {Urun Dogan and Johann Edelbrunner and Ioannis Iossifidis},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011)},
keywords = {driver information systems, feed forward neural network, lane change maneuvers, Machine Learning, recurrent neural network, support vector machines, traffic simulator},
pubstate = {published},
tppubtype = {inproceedings}
}
2008
Ueruen Dogan; Hannes Edelbrunner; Ioannis Iossifidis
Towards a Driver Model: Preliminary Study of Lane Change Behavior Inproceedings
In: 2008 11th International IEEE Conference on Intelligent Transportation Systems, pp. 931–937, IEEE, 2008, ISBN: 978-1-4244-2111-4.
Abstract | Links | BibTeX | Tags: driver information systems, driver model, drivers lane change behavior prediction, feed forward neural network, feedforward neural nets, lane change maneuvers, Machine Learning, recurrent neural nets, recurrent neural network, support vector machines, traffic simulator
@inproceedings{Dogan2008b,
title = {Towards a Driver Model: Preliminary Study of Lane Change Behavior},
author = {Ueruen Dogan and Hannes Edelbrunner and Ioannis Iossifidis},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4732700},
doi = {10.1109/ITSC.2008.4732700},
isbn = {978-1-4244-2111-4},
year = {2008},
date = {2008-10-01},
booktitle = {2008 11th International IEEE Conference on Intelligent Transportation Systems},
pages = {931--937},
publisher = {IEEE},
abstract = {The presented work formulates an framework in which early prediction of drivers lane change behavior is realized. We aim to build a representation of drivers lane change behavior in order to recognize and to predict driver's intentions as a first step towards a realistic driver model. In the test bed of the Institute of Neuroinformatik, based on the traffic simulator NISYS TRS 1, 10 individuals have driven in the experiments and they performed more then 150 lane change maneuvers. Lane-offset, distance to the front car and time to contact, were recorded. The acquired data was used to train - in parallel- 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 of performing a lane change prediction time of 1.5 sec beforehand. The proposed approach describes a framework for lane-change detection and prediction, which will serve as a prerequisite for a successful driver model.},
keywords = {driver information systems, driver model, drivers lane change behavior prediction, feed forward neural network, feedforward neural nets, lane change maneuvers, Machine Learning, recurrent neural nets, recurrent neural network, support vector machines, traffic simulator},
pubstate = {published},
tppubtype = {inproceedings}
}
Ueruen Dogan; Hannes Edelbrunner; Ioannis Iossifidis
Towards a Driver Model: Preliminary Study of Lane Change Behavior Inproceedings
In: 2008 11th International IEEE Conference on Intelligent Transportation Systems, pp. 931–937, IEEE, 2008, ISBN: 978-1-4244-2111-4.
Abstract | Links | BibTeX | Tags: driver information systems, driver model, drivers lane change behavior prediction, feed forward neural network, feedforward neural nets, lane change maneuvers, Machine Learning, recurrent neural nets, recurrent neural network, support vector machines, traffic simulator
@inproceedings{Dogan2008bb,
title = {Towards a Driver Model: Preliminary Study of Lane Change Behavior},
author = {Ueruen Dogan and Hannes Edelbrunner and Ioannis Iossifidis},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4732700},
doi = {10.1109/ITSC.2008.4732700},
isbn = {978-1-4244-2111-4},
year = {2008},
date = {2008-10-01},
booktitle = {2008 11th International IEEE Conference on Intelligent Transportation Systems},
pages = {931--937},
publisher = {IEEE},
abstract = {The presented work formulates an framework in which early prediction of drivers lane change behavior is realized. We aim to build a representation of drivers lane change behavior in order to recognize and to predict driver's intentions as a first step towards a realistic driver model. In the test bed of the Institute of Neuroinformatik, based on the traffic simulator NISYS TRS 1, 10 individuals have driven in the experiments and they performed more then 150 lane change maneuvers. Lane-offset, distance to the front car and time to contact, were recorded. The acquired data was used to train - in parallel- 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 of performing a lane change prediction time of 1.5 sec beforehand. The proposed approach describes a framework for lane-change detection and prediction, which will serve as a prerequisite for a successful driver model.},
keywords = {driver information systems, driver model, drivers lane change behavior prediction, feed forward neural network, feedforward neural nets, lane change maneuvers, Machine Learning, recurrent neural nets, recurrent neural network, support vector machines, traffic simulator},
pubstate = {published},
tppubtype = {inproceedings}
}
Urun Dogan; Johann Edelbrunner; Ioannis Iossifidis
Towards a Driver Model: Preliminary Study of Lane Change Behavior Inproceedings
In: Proc. 11th International IEEE Conference on Intelligent Transportation Systems ITSC 2008, pp. 931–937, Beijing, China, 2008.
Abstract | Links | BibTeX | Tags: driver information systems, driver model, drivers lane change behavior prediction, feed forward neural network, feedforward neural nets, lane change maneuvers, Machine Learning, recurrent neural nets, recurrent neural network, support vector machines, traffic simulator
@inproceedings{Dogan2008,
title = {Towards a Driver Model: Preliminary Study of Lane Change Behavior},
author = {Urun Dogan and Johann Edelbrunner and Ioannis Iossifidis},
doi = {10.1109/ITSC.2008.4732700},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. 11th International IEEE Conference on Intelligent Transportation Systems ITSC 2008},
pages = {931--937},
address = {Beijing, China},
abstract = {The presented work formulates an framework in which early prediction of drivers lane change behavior is realized. We aim to build a representation of drivers lane change behavior in order to recognize and to predict driver's intentions as a first step towards a realistic driver model. In the test bed of the Institute of Neuroinformatik, based on the traffic simulator NISYS TRS textlesssuptextgreater1textless/suptextgreater, 10 individuals have driven in the experiments and they performed more then 150 lane change maneuvers. Lane-offset, distance to the front car and time to contact, were recorded. The acquired data was used to train - in parallel- 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 of performing a lane change prediction time of 1.5 sec beforehand. The proposed approach describes a framework for lane-change detection and prediction, which will serve as a prerequisite for a successful driver model.},
keywords = {driver information systems, driver model, drivers lane change behavior prediction, feed forward neural network, feedforward neural nets, lane change maneuvers, Machine Learning, recurrent neural nets, recurrent neural network, support vector machines, traffic simulator},
pubstate = {published},
tppubtype = {inproceedings}
}