Automatic generation of intersection models from digital maps for vision-based driving on inner city intersections

Heimes, F.; Fleischer, K.; Nagel, Hans-Hellmut

doi:10.1109/ivs.2000.898392

Cited by 15 publications

(3 citation statements)

References 5 publications

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“…In contrast to our approach, [16], [17], [13] exploit digital road maps to generate road models which are mapped The task of detecting intersections is aggravated by the diversity in scene appearance, the tilted installation angle of the camera and large occlusions, which mainly caused by other traffic participants. Approaches solely based on lane markings will fail in such situations.…”

Section: Introductionmentioning

confidence: 99%

Object flow: A descriptor for classifying traffic motion

Geiger

Kitt

2010

2010 IEEE Intelligent Vehicles Symposium

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Abstract-We present and evaluate a novel scene descriptor for classifying urban traffic by object motion. Atomic 3D flow vectors are extracted and compensated for the vehicle's egomotion, using stereo video sequences. Votes cast by each flow vector are accumulated in a bird's eye view histogram grid. Since we are directly using low-level object flow, no prior object detection or tracking is needed. We demonstrate the effectiveness of the proposed descriptor by comparing it to two simpler baselines on the task of classifying more than 100 challenging video sequences into intersection and non-intersection scenarios. Our experiments reveal good classification performance in busy traffic situations, making our method a valuable complement to traditional approaches based on lane markings.

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Section: Introductionmentioning

confidence: 99%

Object flow: A descriptor for classifying traffic motion

Geiger

Kitt

2010

2010 IEEE Intelligent Vehicles Symposium

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“…Expert knowledge is then used to refine the information and arrive at a set of intersection hypotheses which are then compared to the image. [5] proposes an approach which also uses a digital map and derives intersection geometry hypotheses, though the approach is not as flexible and powerful as [4]. [6] proposes an approach to detect the presence of intersections and the lanes connected to them.…”

Section: Introductionmentioning

confidence: 99%

Aggregating lane markings into lanes for intersection assistance

Duchow

Kortner

2007

2007 IEEE Intelligent Vehicles Symposium

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Abstract-Driver assistance functions on marked innercity intersections require a reliable detection of intersection lanes. Due to the high complexity of intersection scenarios and the large amount of clutter that is usually encountered in urban areas, existing highway lane detectors are not applicable for this task. In order to detect the lanes on marked innercity intersections, we adopt a two step process of first detecting individual lane marking segments and then aggregating these segments to appropriate lanes. We assume that a lane detector for innercity scenarios should be capable of handling lanes with arbitrary orientation and curvature and thus may not rely on simple geometric models. In order to achieve this, in the paper we first derive a set of features from the lane marking segment data. These features are fed into a support vector machine. The support vector machine determines whether the lane marking segments belong to the same lane or not.

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“…In the past few years, attention has moved from lane detection on highways to more complex scenarios that may include intersections (see [2], [3]). The capability of sensing intersection geometries offers great possibilities for application in driver assistance systems.…”

Section: Introductionmentioning

confidence: 99%

A marking-based, Flexible approach to intersection detection

Duchow

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops

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Abstract-This paper describes a flexible, marking-based approach to intersection detection. Edge contours are extracted from an image acquired with an onboard camera. The contours are mapped to the flat earth model so as to remove the effect of the perspective mapping. Using properties like spatial context, orientation and length, the individual markings are grouped to contiguous straight lines and finally to lanes. The result is a description of an intersection's geometry without any assumptions about the number, width and orientation of lanes.

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Automatic generation of intersection models from digital maps for vision-based driving on inner city intersections

Cited by 15 publications

References 5 publications

Object flow: A descriptor for classifying traffic motion

Object flow: A descriptor for classifying traffic motion

Aggregating lane markings into lanes for intersection assistance

A marking-based, Flexible approach to intersection detection

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