Maximum Acceptable Risk as Criterion for Decision-Making in Autonomous Vehicle Trajectory Planning

Autonomous vehicles are being developed to make road traffic safer in the future. The time when autonomous vehicles are actually safe enough to be used in real traffic is a current subject of discussion between industry, science, and society. In their work, the authors propose a new approach to the risk assessment of autonomous vehicles based on risk-benefit analysis, as it is already established in other areas, such as the registration of pharmaceuticals. In this context, they address the question of socially acceptable risk for mobility and investigate this concept as a decision-making criterion in trajectory planning. They make the first attempt to quantify an accepted risk by comparing autonomous vehicles with other types of mobility while taking into account the ethical and psychological effects important to the acceptance of autonomous vehicles. They show how an accepted risk contributes to the transparent decision-making of autonomous vehicles at the maneuver level. Finally, they present a method for considering accepted risk in trajectory planning. The evaluation of this algorithm in a simulation of 2,000 scenarios reveals that lower risk thresholds can actually reduce risks in trajectory planning. The code used in this research is publicly available as open-source software: https://github.com/TUMFTM/EthicalTrajectoryPlanning.

• Record URL:
  http://dx.doi.org/10.1109/OJITS.2023.3298973
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/26877813
• Supplemental Notes:
  • © 2023 The Authors.
• Authors:
  • Geisslinger, Maximilian
  • Trauth, Ranier
  • Kaljavesi, Gemb
  • Lienkamp, Markus
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 570-579
• Serial:
  • IEEE Open Journal of Intelligent Transportation Systems
  • Volume: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2687-7813
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Decision making; Risk assessment; Social values; Vehicle trajectories
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01891337
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 28 2023 9:19AM