Sensor Data Fusion for Road Obstacle Detection

Labayrade, Raphaël; Perrollaz, Mathias; Gruyer, Dominique; Didier, A.

doi:10.5772/9973

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…In [4], a laser scanne detect objects, and then a stereo vision sy validate the detections. A depth based seg radar and stereo camera is given in [5].…”

Section: Related Workmentioning

confidence: 99%

Radar depth association with vision detected vehicles on a highway

Sikdar

Cao

Zheng

et al. 2014

2014 IEEE Radar Conference

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No single sensor can independently predict the 3-D environment around us. A vision sensor helps locate objects in a 2-D plane. However, estimating distance using one vision sensor has a limitation. A single radar sensor returns the range of objects accurately; however, the complexity and cost increase if good spatial resolution is required. Thus a radar does not indicate which range corresponds to which object. In this paper, we associate vision detected objects to radar returned echoes, focusing on highways, estimating the 3-D locations of cars around an ego vehicle. This information would help cars driving autonomously to maneuver around.

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“…In [4], a laser scanne detect objects, and then a stereo vision sy validate the detections. A depth based seg radar and stereo camera is given in [5].…”

Section: Related Workmentioning

confidence: 99%

Radar depth association with vision detected vehicles on a highway

Sikdar

Cao

Zheng

et al. 2014

2014 IEEE Radar Conference

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show abstract

“…Based on the results of this project, as an extension of it, new developments were carried out in the CyberCars2 project [27] in 2006-2008. Cybernetic Transportation Systems have also been proposed in countries such as China [25], focusing on the vehicle coordination problem. Although not specifically conceived for autonomous road vehicles, but for many types of unmanned robots, we finally remark on the ELROB (The European Robot Trial) [24] initiative, that is explicitly designed to assess current unmanned systems technologies in realistic scenarios by means of competitions.…”

Section: Introductionmentioning

confidence: 99%

“…LIDAR sensors are widely used for their capability to operate at high frequency (70 Hz) and to provide high resolution measures (0.01 degree) in a range of up to 60 metres. They can be applied as a standalone device or in combination with vision sensors in order to achieve enhanced measurement results [24], [25].…”

Section: Hardware Descriptionmentioning

confidence: 99%

Autonomous Navigation and Obstacle Avoidance of a Micro-Bus

Fernández

Domínguez

Llorca

et al. 2013

International Journal of Advanced Robotic Systems

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At present, the topic of automated vehicles is one of the most promising research areas in the field of Intelligent Transportation Systems (ITS). The use of automated vehicles for public transportation also contributes to reductions in congestion levels and to improvements in traffic flow. Moreover, electrical public autonomous vehicles are environmentally friendly, provide better air quality and contribute to energy conservation.The driverless public transportation systems, which are at present operating in some airports and train stations, are restricted to dedicated roads and exhibit serious trouble dynamically avoiding obstacles in the trajectory. In this paper, an electric autonomous mini-bus is presented. All datasets used in this article were collected during the experiments carried out in the demonstration event of the 2012 IEEE Intelligent Vehicles Symposium that took place in Alcalá de Henares (Spain). The demonstration consisted of a route 725 metres long containing a list of latitude-longitude points (waypoints). The mini-bus was capable of driving autonomously from one waypoint to another using a GPS sensor. Furthermore, the vehicle is provided with a multi-beam Laser Imaging Detection and Ranging (LIDAR) sensor for surrounding reconstruction and obstacle detection. When an obstacle is detected in the planned path, the planned route is modified in order to avoid the obstacle and continue its way to the end of the mission. On the demonstration day, a total of 196 attendees had the opportunity to get a ride on the vehicles. A total of 28 laps were successfully completed in full autonomous mode in a private circuit located in the National Institute for Aerospace Research (INTA), Spain. In other words, the system completed 20.3 km of driverless navigation and obstacle avoidance.

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“…In order to identify the road, dominant line in the v-disparity map should be identified. In [7], authors use Hough Transform to detect the road surface from the v-disparity map. For planar roads, road line in the v-disparity map can be successfully approximated by a straight line.…”

Section: Road Surface Estimation Based Methodsmentioning

confidence: 99%

“…However, for non-planar road conditions, road line in the v-disparity map is a curve. In [7], non-planar road approximation is done using piece-wise linear curve fitting using C highest Hough Transform values. Once the road profile is extracted obstacles are detected by scanning each line in disparity image.…”

Section: Road Surface Estimation Based Methodsmentioning

confidence: 99%

Vision based solutions for autonomous navigation

Karunsekera¹

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I would like to express my heartfelt gratitude to my dear supervisor, Associate Professor Wang Han, for all his support, guidance and motivation given to succeed in my postgraduate studies. I am equally grateful to all the members of the Thesis Advisory Committee (TAC) and the other examiners, for allocating their time from busy schedules to review my thesis and to be present at my oral examination. I wish to appreciate the support given by my fellow colleagues especially, Zhang Handuo, Zhou Hui and Mahdi Abolfazli during the duration of my PhD study.Added to that, I would like to thank my dear wife, parents and family for their understanding and support given to me whenever requested. Finally I would like to sincerely appreciate all the efforts of the research community whose contribution has made the current stand of this area and especially who has made their work and codes publicly available to support the others.vContents Statement of Originality ii Supervisor Declaration Statement iii Authorship Attribution Statement iv Acknowledgement v

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Sensor Data Fusion for Road Obstacle Detection

Cited by 7 publications

References 14 publications

Radar depth association with vision detected vehicles on a highway

Radar depth association with vision detected vehicles on a highway

Autonomous Navigation and Obstacle Avoidance of a Micro-Bus

Vision based solutions for autonomous navigation

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