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 AVs 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 BlaFT Rules - or Blame-Free hybrid Traffic motion planning Rules. An AV following BlaFT Rules 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 accident will happen if all AVs are using a BlaFT Rules conforming motion planner, and that an AV using BlaFT Rules will be blame-free even if it is involved in a collision in hybrid traffic. We implemented a motion planning algorithm that conforms to BlaFT Rules called BlaFT. We instantiated scores of BlaFT controlled AVs and HVs in an urban roadscape loop in the SUMO simulator and show that over time that as the percentage of BlaFT vehicles increases, the traffic becomes safer even with HVs involved. Adding BlaFT vehicles increases the efficiency of traffic as a whole by up to 34% over HVs alone.

Original languageEnglish (US)
Pages (from-to)259-268
Number of pages10
JournalIEEE Transactions on Intelligent Vehicles
Issue number1
StatePublished - Jan 1 2024


  • Automated highways
  • automotive safety
  • intelligent vehicles
  • motion planning
  • transportation

ASJC Scopus subject areas

  • Control and Optimization
  • Artificial Intelligence
  • Automotive Engineering


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