The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

This paper presents a prediction and planning framework for analysing the safety and interaction of moving objects in complex road scenes. Rather than detecting specific, known, dangerous configurations, we simulate all the possible motion and interaction of objects. This simulation is used to detect dangerous situations, and to select the best path. The best path can be chosen according to a number of different criterion, such as: smoothest motion, largest avoiding distance, or quickest path. This framework can be applied, either as a driver warning system (open loop), or as an action recommendation system (human in the loop), or as an intelligent cruise control system (closed loop). This framework is evaluated using synthetic data, using simple and complex road scenes.

Keywords

motion prediction, path planning, decision tree, artificial intelligence

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

Text Reference

Adrian E. Broadhurst, Simon Baker, and Takeo Kanade, "A prediction and planning framework for road safety analysis, obstacle avoidance and driver information," tech. report CMU-RI-TR-04-11, Robotics Institute, Carnegie Mellon University, February, 2004

BibTeX Reference

@techreport{Broadhurst_2004_4600,
   author = "Adrian E Broadhurst and Simon Baker and Takeo Kanade",
   title = "A prediction and planning framework for road safety analysis, obstacle avoidance and driver information",
   booktitle = "",
   institution = "Robotics Institute",
   month = "February",
   year = "2004",
   number= "CMU-RI-TR-04-11",
   address= "Pittsburgh, PA",
}

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