Prediction & Planning - Robotics Institute Carnegie Mellon University
Graphical depiction of the Prediction & Planning project
Prediction & Planning

This project analyses the safety and interaction of moving objects in complex road scenes, using a prediction and planning framework. 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 criteria, 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.


Movie of car overtaking an obstacle with oncoming traffic.
 

Movie of car avoiding a pedestrian, which suddenly steps out onto the road.
Displaying 3 Publications

2005
Adrian E. Broadhurst, Simon Baker, and Takeo Kanade
Conference Paper, Proceedings of IEEE Intelligent Vehicles Symposium (IV '05), pp. 319 - 324, June, 2005
2004
Adrian E. Broadhurst, Simon Baker, and Takeo Kanade
Conference Paper, Proceedings of 11th World Congress on Intelligent Transportation Systems, October, 2004
Adrian E. Broadhurst, Simon Baker, and Takeo Kanade
Tech. Report, CMU-RI-TR-04-11, Robotics Institute, Carnegie Mellon University, February, 2004

current staff

past head

  • Adrian E Broadhurst

past staff

  • Simon Baker

past contact

  • Adrian E Broadhurst