Obstacle Detection and Tracking for the Urban Challenge

Darms, Michael; Rybski, Paul E.; Baker, Christopher; Urmson, Chris

doi:10.1109/tits.2009.2018319

Cited by 118 publications

(55 citation statements)

References 15 publications

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“…Für einen Spurwechselassistenten, welcher den optimalen Zeitpunkt für einen Spurwechsel prädizieren soll, sind jedoch Informationen über vorausfahrenden und rückwärtigen Verkehr als auch über sich auf angrenzenden Fahrstreifen befindliche Fahrzeuge erforderlich (Schubert et al, 2010). Für diese Anwendung oder auch für vollständig autonome Fahrzeuge werden in der Regel eine Vielzahl verschiedener Sensortypen eingesetzt (Darms et al, 2009 (Blackman and Popoli, 1999). In den letzten Jahren hat noch eine zweite Gruppe eine verstärkte Aufmerksamkeit erfahren.…”

Section: Einführungunclassified

Objektverfolgung bei einer vollständigen Fahrzeugumfeldüberwachung mittels Radar

et al. 2014

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Section: Einführungunclassified

Objektverfolgung bei einer vollständigen Fahrzeugumfeldüberwachung mittels Radar

et al. 2014

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“…Methods to solve the occlusion problem in multiple interacting objects tracking have been previously presented in [8,12,15,16]. Sensor based occlusion tracking methods [8,18] cannot handle the difference between moving and nonmoving obstacles. In general, solutions to both pedestrian and vehicle occlusion problems extend from spatial to temporal domains can be broadly classified into many types.…”

Section: Related Workmentioning

confidence: 99%

Occlusion and Abandoned Object Detection for Surveillance Applications

Chitra¹,

Geetha²,

Menaka³

2013

IJCATR

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Abstract:Object detection is an important step in any video analysis. Difficulties of the object detection are finding hidden objects and finding unrecognized objects. Although many algorithms have been developed to avoid them as outliers, occlusion boundaries could potentially provide useful information about the scene's structure and composition. A novel framework for blob based occluded object detection is proposed. A technique that can be used to detect occlusion is presented. It detects and tracks the occluded objects in video sequences captured by a fixed camera in crowded environment with occlusions. Initially the background subtraction is modeled by a Mixture of Gaussians technique (MOG). Pedestrians are detected using the pedestrian detector by computing the Histogram of Oriented Gradients descriptors (HOG), using a linear Support Vector Machine (SVM) as the classifier. In this work, a recognition and tracking system is built to detect the abandoned objects in the public transportation area such as train stations, airports etc. Several experiments were conducted to demonstrate the effectiveness of the proposed approach. The results show the robustness and effectiveness of the proposed method.

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“…The following heuristic takes short term artifact into account. The observed moving decision is only kept if the object is classified as moving for a time period t The distinction of static and moving obstacles was a key concept of the perception system of Tartan Racing's autonomous vehicle for the Urban Challenge 2007 [4]. Fig.…”

Section: B Observed Moving Vs Not Observed Movingmentioning

confidence: 99%

“…Analogous to moving obstacle tracking [2], special algorithms exist to estimate the position and shape of static obstacles. To reduce artifacts with these algorithms it is important to distinguish static from dynamic obstacles, too [4].…”

Section: Introductionmentioning

confidence: 99%

Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments

Darms

Rybski

Urmson

2008

2008 IEEE Intelligent Vehicles Symposium

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Abstract-Future driver assistance systems are likely to use a multisensor approach with heterogeneous sensors for tracking dynamic objects around the vehicle. The quality and type of data available for a data fusion algorithm depends heavily on the sensors detecting an object. This article presents a general framework which allows the use sensor specific advantages while abstracting the specific details of a sensor. Different tracking models are used depending on the current set of sensors detecting the object. A sensor independent algorithm for classifying objects regarding their current and past movement state is presented. The described architecture and algorithms have been successfully implemented in Tartan Racing's autonomous vehicle for the Urban Grand Challenge. Results are presented and discussed.

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Obstacle Detection and Tracking for the Urban Challenge

Cited by 118 publications

References 15 publications

Objektverfolgung bei einer vollständigen Fahrzeugumfeldüberwachung mittels Radar

Objektverfolgung bei einer vollständigen Fahrzeugumfeldüberwachung mittels Radar

Occlusion and Abandoned Object Detection for Surveillance Applications

Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments

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