A Data-Driven Method Towards Minimizing Collision Severity for Highly Automated Vehicles

M. Parseh, F. Asplund, L. Svensson, W. Sinz, E. Tomasch, M. Torngren

Research output: Contribution to journalArticlepeer-review


The deployment of autonomous vehicles on public roads calls for the development of methods that are reliably able to mitigate injury severity in case of unavoidable collisions. This study proposes a data-driven motion planning method capable of minimizing injury severity for vehicle occupants in unavoidable collisions. The method is based on establishing a metric that models the relationship between impact location and injury severity using real accident data, and subsequently including it in the cost function of a motion planning framework. The vehicle dynamics and associated constraints are considered through a pre-computed trajectory library, which is generated by solving an optimal control problem. This allows for efficient computation as well as an accurate representation of the vehicle. The proposed motion planning approach is evaluated by simulation, and it is shown that the trajectory associated with the minimum cost mitigates the collision severity for occupants of passenger vehicles involved in the collision.
Original languageEnglish
Pages (from-to)723-735
Number of pages13
JournalIEEE Transactions on Intelligent Vehicles
Issue number4
Publication statusPublished - 1 Dec 2021


  • Accidents
  • Planning
  • Trajectory
  • Injuries
  • Vehicle dynamics
  • Libraries
  • Cost function
  • Motion planning
  • collision severity
  • data-driven
  • impact location
  • injury severity
  • trajectory library
  • occupant safety
  • optimal control
  • Occupant safety
  • Collision severity
  • Injury severity
  • Trajectory library
  • Optimal control
  • Impact location
  • Data-driven

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Optimization
  • Automotive Engineering

Fields of Expertise

  • Mobility & Production


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