Detection of Road Users in Fused Sensor Data Streams for Collision Mitigation

Walchshausl, L.; Lindl, R.; Vogel, K.; Tatschke, T.

doi:10.1007/3-540-33410-6_6

Cited by 17 publications

(12 citation statements)

References 2 publications

(2 reference statements)

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“…The long term goal of the subproject ProFusion2 is the development of innovative, robust and reliable sensor data fusion techniques for advanced driver assistance systems. The shown data screen-shots have been recorded by the BMW demonstrator ( [10]) and are by courtesy of COMPOSE. The presented methods are integrated into the COMPOSE sensor fusion system and use its graphical interface.…”

Section: Acknowledgmentsmentioning

confidence: 99%

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Model-based detection and tracking of objects using a 3D-camera

Schindler

2010

2010 IEEE Intelligent Vehicles Symposium

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In modern driver assistance systems the environment perception especially the detection of vulnerable road users plays an important role. The analysis of such traffic scenes demands reliable and robust information on objects and their position. In this context a 3D-camera, offers a promising concept in providing both lateral resolution and depth information to supply this task. This paper presents models and methods for the detection and the tracking of objects using the range data of a 3D-camera.For that purpose the depth information of a 3D-camera is used for the reconstruction of the traffic scenario. Special and adapted methods of the field of machine learning allow to analyze the re-projected structures in order to extract object measurements from the sensors raw data. Finally stochastic state estimation is applied to propagate object hypotheses taking into account the measurements. The proposed methodology facilitates the integration and support of standard environment perception techniques used in todays advanced driver assistance systems.

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Section: Acknowledgmentsmentioning

confidence: 99%

“…This paper presents an approach on how the data of a 3D-camera can be used for object detection and object tracking. For that purpose a 3D-camera has been integrated into the object detection environment of [10] using early sensor data fusion. The used 3D-camera is the prototype of the UseRCams project [11].…”

Section: Introductionmentioning

confidence: 99%

Model-based detection and tracking of objects using a 3D-camera

Schindler

2010

2010 IEEE Intelligent Vehicles Symposium

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“…Although this might be acceptable for some driver assistance functions, it is not desired for safety critical applications such as pre-crash or emergency braking. In warning systems, valuable time is lost before a reaction of the driver influences the car [4]. Because of this, safety systems preferably would directly interact with the power train and the brake system of the vehicle autonomously.…”

Section: B Generic Roadmap Of Vehicle Functionsmentioning

confidence: 99%

Functional requirements of future automotive radar systems

Rasshofer¹

2007

2007 European Microwave Conference

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Automotive radar systems are starting to divide into two groups: Highly specialized stand alone low-cost sensors targeting high volume markets and high-performance multi purpose sensors used in sophisticated data fusion architectures. While the first group focuses on ultimate cost reduction, the latter provides the basis for future high-performance driver assistance functions which might be deployed in middle-to luxury-class cars. This paper addresses both sensor groups and lists OEM requirements resulting from the specifications of future driver assistance functions and from upcoming perception system architectures. The second part of this contribution addresses recent trends in automotive radar sensing technology.978-2-87487-001-9

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“…Range sensor efficiently narrows the search range, although it is not complete, such that real time implementation becomes possible. Actually, to some extent, sensor redundancy is required to meet the reliability needs in automotive field [19].…”

Section: Introductionmentioning

confidence: 99%

Sensor Fusion Based Obstacle Detection/Classification for Active Pedestrian Protection System

Jung

Lee

Yoon

et al. 2006

Advances in Visual Computing

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Abstract. This paper proposes a sensor fusion based obstacle detection/classification system for active pedestrian protection system. At the frontend of vehicle, one laser scanner and one camera is installed. Clustering and tracking of range data from laser scanner generate obstacle candidates. Vision system classifies the candidates into three categories: pedestrian, vehicle, and other. Gabor filter bank extracts the feature vector of candidate image. The obstacle classification is implemented by combining two classifiers with the same architecture: support vector machine for pedestrian and vehicle. Obstacle detection system recognizing the class can actively protect pedestrian while reducing false positive rate.

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Detection of Road Users in Fused Sensor Data Streams for Collision Mitigation

Cited by 17 publications

References 2 publications

Model-based detection and tracking of objects using a 3D-camera

Model-based detection and tracking of objects using a 3D-camera

Functional requirements of future automotive radar systems

Sensor Fusion Based Obstacle Detection/Classification for Active Pedestrian Protection System

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