Fahrerintentionserkennung für Fahrerassistenzsysteme

Schroven, Frank; Giebel, Tobias

doi:10.1365/s35778-008-0212-0

Cited by 4 publications

(2 citation statements)

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“…The principles and methods of these approaches are similar to other fields of intention detection [6], [7], [8]. Closely linked are approaches dealing with ACC control using information of lane change intentions [9]. Many different algorithms are used for the detection of intentions.…”

Section: A Intention Detection In Generalmentioning

confidence: 99%

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Predicting the driver's turn intentions at urban intersections using context-based indicators

Rodemerk

Winner

Kastner

2015

2015 IEEE Intelligent Vehicles Symposium (IV)

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Many improvements have been made in the area of vehicle safety and pedestrian protection; however, urban intersections are still black spots for Advanced Driver Assistance Systems (ADAS). One of the main reasons is the uncertainty of the future driving direction at intersections. Due to that uncertainty, the early activation of an intersection ADAS will lead to high false positive rates, while, in contrast, a late activation of an intersection ADAS will lead to a low accident-reduction potential. This tradeoff is described as warning dilemma. In order to solve the warning dilemma, an approach to predicting the driver's turn intention at urban intersections is introduced. The novelty of the approach is its context-based prediction of the future driving maneuver several seconds before the driving trajectory changes. To predict maneuvers, indicators are used to encode the context information together with vehicle data. A system setup including prediction results of the system is described.

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Section: A Intention Detection In Generalmentioning

confidence: 99%

“…Many different algorithms are used for the detection of intentions. The most common methods, amongst others, are based on Bayesian networks [6], [9], Hidden Markov Models [7], [10], neural networks or fuzzy logic [9]. Finite state machines are used [11] as well as different types of vector machines [12].…”

Section: A Intention Detection In Generalmentioning

confidence: 99%