360 Degree multi sensor fusion for static and dynamic obstacles

Schueler, Kai; Weiherer, Tobias; Bouzouraa, Essayed; Hofmann, Ulrich

doi:10.1109/ivs.2012.6232253

Cited by 47 publications

(34 citation statements)

References 15 publications

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“…Multi-sensory technology and data fusion is used in autonomous driving [9], [10] to extract information about the surrounding environment and provide guidance. Data mining techniques [11], [12], hierarchical Markov decision processes [13] and neural networks [14], [15] are also often used for this purpose.…”

Section: Discussionmentioning

confidence: 99%

“…Multisensory approaches are also used to provide orientation and mobility to the blind [3], [4], addressing user location, navigation and environment recognition problems in a integrated solution [5]- [8]. The multi-sensory technology and data fusion is used in other contexts for the same purpose, as for instance in autonomous driving [9], [10]. Are often used data mining techniques [11], [12], hierarchical Markov decision processes [13] and neural networks [14], [15] for information extraction from the data gathered.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pervasive Crowd Mapping for Dynamic Environments

Paredes¹,

Fernandes²,

Sousa³

et al. 2016

Proceedings of the 2015 International Conference on Recent Advances in Computer Systems

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Abstract-There is a demand for new models of computation intelligence for the recognition of the environment and the obstacles in each moment and the sharing of this information with other users providing temporary dangers notifications, which can enhance blind navigation experience and autonomy. We identified an opportunity to contribute with an integrated strategy to develop a solution to improve the blind autonomy and quality of life. We are looking for solutions to problems that have emerged from the accumulated experience in blind navigation systems research. The main objective of this paper is to present a conceptual model that works based on data obtained from sensors on passive monitoring, worn by bystanders that can combine and correlate the inference patterns that match the obstacles and/or dangers. The model has retro-feedback mechanisms, allowing the sensors to search and pervasively validate the existence of obstacle, ensuring the temporary basis of these risks.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pervasive Crowd Mapping for Dynamic Environments

Paredes¹,

Fernandes²,

Sousa³

et al. 2016

Proceedings of the 2015 International Conference on Recent Advances in Computer Systems

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“…Therefore, the state of an object is estimated with respect to reference points (RPs) which are located on or inside the contour of the extended object. In this thesis extended objects are represented as oriented bounding boxes (OBBs) as in [Kam07;SWBH12;Wen08]. For this representation the objects' length l and width w have to be estimated in addition to the model dependent states.…”

Section: Extended Object Tracking Using Reference Pointsmentioning

confidence: 99%

Intersection-Based Road User Tracking Using a Classifying Multiple-Model PHD Filter

Meißner

Reuter

Strigel

et al. 2014

IEEE Intell. Transport. Syst. Mag.

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Bibliografische Information der Deutschen NationalbibliothekDie Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über http://dnb.dnb.de abrufbar. AbstractDue to an increasing number of traffic accident fatalities at intersections, the prevention of traffic accidents at intersection attracts more and more attention in public as well as in research. One example therefore, is the joint-project Ko-PER of the research initiative Ko-FAS which aims to increase the preventive traffic safety. This is realized by an preferably exhaustive recognition of the vehicles' environments based on locally gathered and communicated perception data. Therefore, especially at urban intersections, an intersection-based perception system which communicates the current traffic situation to vehicles in the intersection's surrounding is essential. Thus, a busy public intersection in Aschaffenburg, Germany was equipped with an intersection perception system, which contains multiple laserscanners and video cameras as well as data processing units. Beside the planning of the perception system, the core of this thesis is the laserscanner-based detection and classification of road users and the tracking of them by fusing detections of the laserscanners and video cameras.Since the laserscanners at the intersection are mounted at posts of lamps, traffic-lights as well as surrounding buildings, segmentation algorithms are introduced which utilize the knowledge about the consistent environment to segment the laserscanner measurements in background and foreground. Subsequently, the object detection in the foreground data relies on position as well as motion information. The 3D measurement points are clustered with a real-time capable implementation of the density based spatial clustering for applications with noise (DBSCAN) algorithm. Nevertheless, particularly large objects are sometimes not entirely detected due to occlusions, poor reflection characteristics and measurement uncertainties. This may lead to the separation of one object in multiple clusters. To avoid the overclustering, a combination of 2D tracking and 3D clustering methods is proposed. Beside the benefit of the considerably more robust object detection, additionally the determination of the objects' orientations is improved. Finally, the detected objects are classified by means of features of their laserscanner point clusters.The focus of this thesis lays on the tracking of road users at traffic intersections. Thus, a tracking approach is required which is able to handle multiple objects detected by multiple classifying sensors with different field of views. Due to the topdown modeling of the multi-object tracking problem and the availability of efficient implementations, a Gaussian mixture probability hypothesis density (GM-PHD) filter is used. Based on this the classifying multiple-model probability hypothesis density (CMMPHD) filter is developed which facilitates to track road user...

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“…• Methods that filter out measurements supposed to belong to dynamic objects and subsequently use a traditional mapping algorithm suitable for a static world [42,100,141,183,184,193,229,[259][260][261]263].…”

mentioning

confidence: 99%

“…This score is based on newly occupied and free regions, but, in opposition to the proposed approach, the information is not used within the measurement vector generation for an employed RBPF-based tracking. A combination of object-based multi-target tracking and grid mapping can further be found in [42,229], where grid cells are associated with object states and predicted within the map. A subsequent feedback from the map is used to preserve consistency between the map content and the object list.…”

mentioning

confidence: 99%

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Schreier

2017

At - Automatisierungstechnik

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The dissertation approaches the questions i) how to represent the driving environment in an environment model, ii) how to obtain such a representation, and iii) how to predict the traffic scene for criticality assessment. Bayesian inference provides the common framework of all designed methods. First, Parametric Free Space (PFS) maps are introduced, which compactly represent the vehicle environment in form of relevant, drivable free space. They are obtained by a novel method for grid mapping and tracking in dynamic environments. In addition, a maneuver-based, long-term trajectory prediction and criticality assessment system is introduced and the application of all methods within the advanced driver assistance system PRORETA 3 is described.

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360 Degree multi sensor fusion for static and dynamic obstacles

Cited by 47 publications

References 15 publications

Pervasive Crowd Mapping for Dynamic Environments

Pervasive Crowd Mapping for Dynamic Environments

Intersection-Based Road User Tracking Using a Classifying Multiple-Model PHD Filter

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

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