A Prediction and Planning Framework for Road Safety Analysis, Obstacle Avoidance and Driver Information

Adrian E. Broadhurst, Simon Baker, and Takeo Kanade
Proceedings of the 11th World Congress on Intelligent Transportation Systems, October, 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(s) / Consortia: Vision and Autonomous Systems Center
Associated Lab(s) / Group(s): Vision for Safe Driving
Associated Project(s): Prediction & Planning
Number of pages: 12

Text Reference
Adrian E. Broadhurst, Simon Baker, and Takeo Kanade, "A Prediction and Planning Framework for Road Safety Analysis, Obstacle Avoidance and Driver Information," Proceedings of the 11th World Congress on Intelligent Transportation Systems, October, 2004.

BibTeX Reference
@inproceedings{Broadhurst_2004_4716,
   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 = "Proceedings of the 11th World Congress on Intelligent Transportation Systems",
   month = "October",
   year = "2004",
}