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Abstract

This paper presents a framework for reasoning about the future motion of multiple objects in a road scene. Unlike previous approaches, we do not look for known dangerous configurations of objects, but rather we reason about the future paths of all objects in the scene, and assess their danger. Monte Carlo path planning is used to generate a probability distribution for the possible future motion of every car in the scene.

This framework can be used to either control the car, or to display warnings for the driver.

Keywords

Artificial Intelligence, Path planning, Monte Carlo, Road, Safety

Notes

Sponsor: DENSO CORPORATION

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Vision for Safe Driving

Associated Project(s): Prediction & Planning

Number of pages: 6

Text Reference

Adrian E. Broadhurst, Simon Baker, and Takeo Kanade, "Monte Carlo Road Safety Reasoning," IEEE Intelligent Vehicle Symposium (IV2005), June, 2005, pp. 319 - 324.

BibTeX Reference

@inproceedings{Broadhurst_2005_5004,
   author = "Adrian E Broadhurst and Simon Baker and Takeo Kanade",
   title = "Monte Carlo Road Safety Reasoning",
   booktitle = "IEEE Intelligent Vehicle Symposium (IV2005)",
   pages = "319 - 324",
   publisher = "IEEE",
   month = "June",
   year = "2005",
}

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