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MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios.

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This study introduces a novel motion planning algorithm for autonomous vehicles, adaptable to diverse urban scenarios. The system generates multiple trajectories, selecting the optimal one based on safety, comfort, and utility, enabling varied driving styles.

Keywords:
autonomous drivingmerit functionmotion planningspeed profiletrajectory generation

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Urban Mobility

Background:

  • Motion planning for autonomous vehicles in complex urban environments presents significant challenges due to unpredictable situations and behaviors.
  • Traditional rule-based approaches struggle to handle the inherent variability and intractability of urban driving scenarios.

Purpose of the Study:

  • To develop a use-case-independent motion planning algorithm for autonomous vehicles.
  • To create a system capable of generating and selecting optimal trajectories based on multiple criteria.

Main Methods:

  • A novel motion planning algorithm was proposed, generating a set of potential trajectories.
  • A merit function was designed to evaluate trajectories based on longitudinal comfort, lateral comfort, safety, and utility.
  • The algorithm was tested in simulated and real-world urban environments.

Main Results:

  • The algorithm successfully generated a set of possible trajectories for autonomous vehicles.
  • The merit function effectively selected the best trajectory according to predefined criteria.
  • The system demonstrated the ability to achieve different driving styles by adjusting priorities within the merit function.

Conclusions:

  • The proposed motion planning algorithm is adaptable and effective for autonomous vehicles in urban settings.
  • The system consistently meets safety and comfort parameters while allowing for customization of driving styles.
  • This approach offers a viable solution for safe and adaptable motion planning in complex environments.