Caroline: An Autonomously Driving Vehicle for Urban Environments

Rauskolb, Fred W.; Berger, Kai; Lipski, Christian; Magnor, Marcus; Cornelsen, Karsten; Effertz, Jan; Form, Thomas; Graefe, Fabian; Ohl, Sebastian; Schumacher, Walter; Wille, Jörn-Marten; Hecker, P.; Nothdurft, Tobias; Doering, Michael; Homeier, Kai; Morgenroth, Johannes; Wolf, Lars; Basarke, Christian; Berger, Christian; Gülke, Tim; Klose, Felix; Rumpe, Bernhard

doi:10.1007/978-3-642-03991-1_11

Cited by 19 publications

(17 citation statements)

References 11 publications

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“…The accuracy was improved with a laser system performing lane and curb detection. In [251], a visual lane detection algorithm combined with the RNDF was responsible for the vehicle localization. The authors pointed out the difficulty to have a reliable localization with a vision-only system because of environmental conditions like shadows, etc.…”

Section: Vehiclesmentioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

703

366

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Abstract-In this article, we propose a survey of the Simultaneous Localization And Mapping field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multi-vehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various largescale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

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Section: Vehiclesmentioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

703

366

View full text Add to dashboard Cite

show abstract

“…High-speed car control was tested by Stanford [5] in real life scenarios in Pikes Peak Rally drive. Autonomous operations in real scenarios were targeted by many others like Google [6], Braunschweig University [7], and Berlin University [8], which create new milestones in autonomous driving. But these cars use sophisticated sensors like 3D LIDAR scanner, 2D LIDAR scanner, stereo cameras and many other software resources which most of the times exceeds the cost of the car itself.…”

Section: Adas and Autonomous Drivingmentioning

confidence: 99%

Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm

Dheekonda

Panda

Khan

et al. 2017

SAE Technical Paper Series

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Accuracy in detecting a moving object is critical to autonomous driving or advanced driver assistance systems (ADAS). By including the object classification from multiple sensor detections, the model of the object or environment can be identified more accurately. The critical parameters involved in improving the accuracy are the size and the speed of the moving object. All sensor data are to be used in defining a composite object representation so that it could be used for the class information in the core object's description. This composite data can then be used by a deep learning network for complete perception fusion in order to solve the detection and tracking of moving objects problem. Camera image data from subsequent frames along the time axis in conjunction with the speed and size of the object will further contribute in developing better recognition algorithms. In this paper, we present preliminary results using only camera images for detecting various objects using deep learning network, as a first step toward multi-sensor fusion algorithm development. The simulation experiments based on camera images show encouraging results where the proposed deep learning network based detection algorithm was able to detect various objects with certain degree of confidence. A laboratory experimental setup is being commissioned where three different types of sensors, a digital camera with 8 megapixel resolution, a LIDAR with 40m range, and ultrasonic distance transducer sensors will be used for multi-sensor fusion to identify the object in real-time.

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“…Hierzu muss dann allerdings der aktuelle Fahrzeugzustand auf die ausführende Ebene im Sinne einer klassischen Regelung rück-geführt werden, sodass das Fahrzeug trotz Störungen und Modellfehler der Bahn dauerhaft folgt. Diese Strategie wurde erfolgreich durch die Teams [9], [56], [63], [67], [47] und [97] (bei inaktiven Tentacles) umgesetzt. Von einer systematischen Kombination beider Strategien zur Vereinigung der jeweiligen Vorteile wird für die Fahrzeuganwendung mit Ausnahme von [41] 12 in der Literatur nicht berichtet.…”

Section: High-und Low-level-stabilisierungunclassified

“…die geplante Bahn zu versetzen [97]. Die hierdurch auftretenden Manöver waren trotz einfach gehaltener Szenarien nicht selten in gänzlich unkritischen Situationen durch abgehackte Bewegungen bis hin zu Unfällen [21] gekennzeichnet, da eine kombinierte Optimierung des Einsatzes von Gas/Bremse und Lenkung auf Basis einer Prädiktion des dynamischen Verkehrsgeschehens bei keinem der Teams [9], [56], [63], [67], [47], [88], [7], [54], einschließlich dem eigenen [97], stattfand.…”

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Ein neues Konzept für die Trajektoriengenerierung und -stabilisierung in zeitkritischen Verkehrsszenarien

Werling¹

2012

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Caroline: An Autonomously Driving Vehicle for Urban Environments

Cited by 19 publications

References 11 publications

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm

Ein neues Konzept für die Trajektoriengenerierung und -stabilisierung in zeitkritischen Verkehrsszenarien

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