Blame-Free Motion Planning in Hybrid Traffic

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
Bla
me-
F
ree hybrid
T
raffic 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.