Probabilistic driver modeling to characterize human behavior for semiautonomous framework

Katherine Driggs Campbell, Victor Shia, Ram Vasudevan, Francesco Borrelli, Ruzena Bajcsy

Research output: Contribution to conferencePaper

Abstract

In this paper, we explore a probabilistic driver model that predicts human behavior based on the external environment, the state of the driver, and previously observed trajectories for an individual driver. The overall goal of this driver model is to create an individualized prediction scheme over a long time horizon in various situations. The novel feature of this particular model is that it uses empirical driver data to create a set of potential future trajectories. A clustering algorithm was developed to identify scenarios and collect the associated driver behavior in a controllable form. This model can then be used to determine when a safe controller should intervene to keep the vehicle in a safe region of the current environment. Experimental results show that this method is has an accuracy of up to 90%, showing that human drivers tend to drive in a reproducible manner. Here, this model is extended to a probabilistic method to examine the reliability of the model, characterize human behavior, and quantify the dissimilarities of driver behavior in different scenarios. In addition, a semiautonomous framework is proposed that encapsulates the driver behavior and vehicle dynamics to examine threat in a given situation.

Original languageEnglish (US)
StatePublished - Jan 1 2013
Externally publishedYes
Event6th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems and Safety 2013, DSP 2013 - Seoul, Korea, Republic of
Duration: Sep 29 2013Oct 2 2013

Conference

Conference6th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems and Safety 2013, DSP 2013
CountryKorea, Republic of
CitySeoul
Period9/29/1310/2/13

Keywords

  • Active safety
  • Computer vision
  • Driver modeling
  • Semiautonomous vehicles

ASJC Scopus subject areas

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Campbell, K. D., Shia, V., Vasudevan, R., Borrelli, F., & Bajcsy, R. (2013). Probabilistic driver modeling to characterize human behavior for semiautonomous framework. Paper presented at 6th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems and Safety 2013, DSP 2013, Seoul, Korea, Republic of.