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Carnegie Mellon Robotics Institute

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Abstract

In this paper, an offline learning mechanism based on the genetic algorithm is proposed for autonomous vehicles to emulate human driver behaviors. The autonomous driving ability is implemented based on a Prediction- and Cost function-Based algorithm (PCB). PCB is designed to emulate a human driver’s decision process, which is modeled as traffic scenario prediction and evaluation. This paper focuses on using a learning algorithm to optimize PCB with very limited training data, so that PCB can have the ability to predict and evaluate traffic scenarios similarly to human drivers. 80 seconds of human driving data was collected in low-speed (< 30miles/h) car-following scenarios. In the low-speed car-following tests, PCB was able to perform more human-like carfollowing after learning. A more general 120 kilometer-long simulation showed that PCB performs robustly even in scenarios that are not part of the training set.

Keywords

autonomous driving, traffic scenario evaluation, human behavior emulation, genetic algorithm

Notes

Associated Project(s): Robust Autonomous Freeway Driving Behaviors

Number of pages: 12

Text Reference

Junqing Wei, John M. Dolan, and Bakhtiar Litkouhi, "A Learning-Based Autonomous Driver: Emulating a Human Driver’s Intelligence in Low-speed Car Following," Proceedings of the SPIE Defense, Security, and Sensing Conference, Unmanned Systems Technology, April, 2010.

BibTeX Reference

@inproceedings{Dolan_2010_6747,
   author = "Junqing Wei and John M Dolan and Bakhtiar Litkouhi",
   title = "A Learning-Based Autonomous Driver: Emulating a Human Driver’s Intelligence in Low-speed Car Following",
   booktitle = "Proceedings of the SPIE Defense, Security, and Sensing Conference, Unmanned Systems Technology",
   month = "April",
   year = "2010",
   volume = "7693",
}

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