Multi-attribute decision making on mitigating a collision of an autonomous vehicle on motorways

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Autonomous vehicles have the potential to improve automotive safety, largely by removing human error as a possible cause of collisions. However, it cannot be guaranteed that autonomous vehicles will be able to eliminate all collisions. Therefore, automotive safety will continue to be a necessity for automotive design. This paper proposes a decision making system which selects the least severe collision for an autonomous vehicle to take, when facing multiple imminent and unavoidable collisions on a motorway. The novel decision making system developed combines simulation results and multi-attribute decision making (MADM) methods. The simulator includes models of vehicle dynamics and the manoeuvre trajectory path. MADM methods are used to decide which vehicle(s) the autonomous vehicle should collide with, based on the severity of collisions. Severity of collisions is calculated in the simulator using the following variables: impact velocity between autonomous vehicle and vehicle ahead, impact velocity between vehicle behind and autonomous vehicle, manoeuvre acceleration and time-to-collision. Various MADM methods are investigated and three methods are selected including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Analytical Hierarchy Process (AHP), and the Analytical Network Process (ANP). Various collision scenarios are defined and tested in order to understand the impact that small changes in parameters of the autonomous vehicle and vehicles ahead and behind have on the decision made. The analysed decision making results are promising and lead to the conclusion that MADM methods can be successfully applied in autonomous vehicles.
Original languageEnglish
Article number114581
Pages (from-to)(In-press)
JournalExpert Systems with Applications
Early online date8 Jan 2021
Publication statusE-pub ahead of print - 8 Jan 2021


UK Engineering and Physical Sciences Research Council (EPSRC), Industrial Cooperative Awards in Science & Technology (iCASE) grant no. EP/L505614/1, and the industrial collaborator Jaguar Land Rover


  • Autonomous vehicle
  • Collision avoidance and mitigation
  • Multi-attribute decision making
  • Simulation model

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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