ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2504.12744
26
0

Biasing the Driving Style of an Artificial Race Driver for Online Time-Optimal Maneuver Planning

17 April 2025
Sebastiano Taddei
Mattia Piccinini
Francesco Biral
ArXivPDFHTML
Abstract

In this work, we present a novel approach to bias the driving style of an artificial race driver (ARD) for online time-optimal trajectory planning. Our method leverages a nonlinear model predictive control (MPC) framework that combines time minimization with exit speed maximization at the end of the planning horizon. We introduce a new MPC terminal cost formulation based on the trajectory planned in the previous MPC step, enabling ARD to adapt its driving style from early to late apex maneuvers in real-time. Our approach is computationally efficient, allowing for low replan times and long planning horizons. We validate our method through simulations, comparing the results against offline minimum-lap-time (MLT) optimal control and online minimum-time MPC solutions. The results demonstrate that our new terminal cost enables ARD to bias its driving style, and achieve online lap times close to the MLT solution and faster than the minimum-time MPC solution. Our approach paves the way for a better understanding of the reasons behind human drivers' choice of early or late apex maneuvers.

View on arXiv
@article{taddei2025_2504.12744,
  title={ Biasing the Driving Style of an Artificial Race Driver for Online Time-Optimal Maneuver Planning },
  author={ Sebastiano Taddei and Mattia Piccinini and Francesco Biral },
  journal={arXiv preprint arXiv:2504.12744},
  year={ 2025 }
}
Comments on this paper