Blame-Free Motion Planning in Hybrid Traffic

Sanggu Park, Edward Andert, Aviral Shrivastava

Research output: Contribution to journalArticlepeer-review


Despite the potential of autonomous vehicles (AV) to improve traffic efficiency and safety, many studies have shown that traffic accidents in a hybrid traffic environment where both AV and human-driven vehicles (HVs) are present are inevitable because of the unpredictability of HVs. Given that eliminating accidents is impossible, an achievable goal is to design AVs in a way so that they will not be blamed for any accident in which they are involved in. In this paper, we propose <bold>BlaFT Rules</bold> &#x2013; or <bold>Bla</bold>me-<bold>F</bold>ree hybrid <bold>T</bold>raffic motion planning <bold>Rules</bold>. An AV following <bold>BlaFT Rules</bold> is designed to be cooperative with HVs as well as other AVs, and will not be blamed for accidents in a structured road environment. We provide proofs that no accidents will happen if all AVs are using a <bold>BlaFT Rules</bold> conforming motion planner, and that an AV using <bold>BlaFT Rules</bold> will be blame-free even if it is involved in a collision in hybrid traffic. We implemented a motion planning algorithm that conforms to <bold>BlaFT Rules</bold> called <bold>BlaFT</bold>. We instantiated scores of <bold>BlaFT</bold> controlled AVs and HVs in an urban roadscape loop in the SUMO simulator and show that over time that as the percentage of <bold>BlaFT</bold> vehicles increases, the traffic becomes safer even with HVs involved. Adding <bold>BlaFT</bold> vehicles increases the efficiency of traffic as a whole by up to 34&#x0025; over HVs alone.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalIEEE Transactions on Intelligent Vehicles
StateAccepted/In press - 2023


  • Accidents
  • Autonomous vehicles
  • Law
  • Machine learning
  • Planning
  • Roads
  • Safety

ASJC Scopus subject areas

  • Automotive Engineering
  • Control and Optimization
  • Artificial Intelligence


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