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RI | Publications | A prediction and planning framework for road safety analysis, obstacle avoidance and driver information
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A prediction and planning framework for road safety analysis, obstacle avoidance and driver information
A.E. Broadhurst, S. Baker, and T. Kanade
tech. report CMU-RI-TR-04-11, Robotics Institute, Carnegie Mellon University, February, 2004.
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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.
| Notes |
Sponsor: Denso Corporation
Associated center: VASC
Associated lab/group: Vision for Safe Driving
Associated project: Prediction & Planning
| Text Reference |
A.E. Broadhurst, S. Baker, and T. 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",
institution = "Robotics Institute, Carnegie Mellon University",
month = "February",
year = "2004",
number = "CMU-RI-TR-04-11",
address = "Pittsburgh, PA"
}