Natural Speed Planning for Connected Car Using Slack Variable within Social Norm

Solyeon Kwon, Kyoungseok Han

Research output: Contribution to journalArticlepeer-review

Abstract

In recent years, studies on natural behavior planning for autonomous vehicles have been popular in both the academia and the industry. This study proposes receding-horizon control, framework-based vehicle speed planning. Using a sufficiently long prediction horizon, our approach is aimed at a smoothed velocity trajectory, reducing the entire trip time by passing the traffic light properly. Moreover, the proposed method mimics a human driver naturally in terms of driving styles (i.e., conservative, general, and aggressive behaviors), In the view of the social norm, the planned speed from our approach sometimes exceeds the specified speed limit, but it does not violate strict traffic rules. In this manner, the controlled, connected, and automated vehicle behaves as human drivers do in their daily driving. Specifically, we impose a slack variable to the proposed control framework when upcoming traffic information is available, so that the vehicle can pass the traffic signal at the yellow light phase. By relaxing state constraints, the test results in this paper show a reduction in trip time and fuel consumption.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalTransactions of the Korean Society of Automotive Engineers
Volume31
Issue number1
DOIs
StatePublished - Jan 2023

Keywords

  • Connected and automated vehicle
  • Deviant behavior
  • Model predictive control
  • Signal phase and timing
  • Social norm

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