Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers

  address = {Philadelphia, USA},
  title = {Game-{Theoretic} {Planning} for {Autonomous} {Driving} among {Risk}-{Aware} {Human} {Drivers}},
  abstract = {We present a novel approach for risk-aware planning  with  human  agents  in  multi-agent  traffic  scenarios.  Our approach  takes  into  account  the  wide  range  of  human  driver behaviors on the road, from aggressive maneuvers like speeding and  overtaking,  to  conservative  traits  like  driving  slowly  and conforming  to  the  right-most  lane.  In  our  approach,  we  learn a  mapping  from  a  data-driven  human  driver  behavior  model called  the  CMetric  to  a  driver’s  entropic  risk  preference.  We then  use  the  derived  risk  preference  within  a  game-theoretic risk-sensitive  planner  to  model  risk-aware  interactions  among human  drivers  and  an  autonomous  vehicle  in  various  traffic scenarios. We demonstrate our method in a merging scenario, where our results show that the final trajectories obtained from the risk-aware planner generate desirable emergent behaviors.Particularly,  our  planner  recognizes  aggressive  human  drivers and  yields  to  them  while  maintaining  a  greater  distance  from them.  In  a  user  study,  participants  were  able  to  distinguish between  aggressive  and  conservative  simulated  drivers  based on  trajectories  generated  from  our  risk-sensitive  planner.  We also  observe  that  aggressive  human  driving  results  in  more frequent lane-changing in the planner. Finally, we compare the performance  of  our  modified  risk-aware  planner  with  existing methods and show that modeling human driver behavior leads to  safer  navigation.},
  publisher = {IEEE},
  author = {Chandra, Rohan and Wang, Mingyu and Schwager, Mac and Manocha, Dinesh},
  year = {2022},
  note = {Under Review}