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Grid-Based Multi-Sensor Fusion for On-Road Obstacle Detection: Application to Autonomous Driving
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Rutnätsbaserad multisensorfusion för detektering av hinder på vägen: tillämpning på självkörande bilar (Swedish)
Abstract [en]

Self-driving cars have recently become a challenging research topic, with the aim of making transportation safer and more efficient. Current advanced driving assistance systems (ADAS) allow cars to drive autonomously by following lane markings, identifying road signs and detecting pedestrians and other vehicles. In this thesis work we improve the robustness of autonomous cars by designing an on-road obstacle detection system.

The proposed solution consists on the low-level fusion of radar and lidar through the occupancy grid framework. Two inference theories are implemented and evaluated: Bayesian probability theory and Dempster-Shafer theory of evidence. Obstacle detection is performed through image processing of the occupancy grid. Last, the Dempster-Shafer additional features are leveraged by proposing a sensor performance estimation module and performing advanced conflict management.

The work has been carried out at Volvo Car Corporation, where real experiments on a test vehicle have been performed under different environmental conditions and types of objects. The system has been evaluated according to the quality of the resulting occupancy grids, detection rate as well as information content in terms of entropy. The results show a significant improvement of the detection rate over single-sensor approaches. Furthermore, the Dempster-Shafer implementation may slightly outperform the Bayesian one when there is conflicting information, although the high computational cost limits its practical application. Last, we demonstrate that the proposed solution is easily scalable to include additional sensors. 

Place, publisher, year, edition, pages
2015.
Keyword [en]
autonomous driving, occupancy grid mapping, sensor fusion, Dempster-Shafer, obstacle detection
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-173316OAI: oai:DiVA.org:kth-173316DiVA: diva2:852457
External cooperation
Volvo Car Corporation
Educational program
Master of Science - Systems, Control and Robotics
Supervisors
Examiners
Available from: 2015-09-10 Created: 2015-09-09 Last updated: 2015-09-10Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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