{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lucidgames-online-unscented-inverse-dynamic-1","title":"LUCIDGames: onLine UnsCented Inverse Dynamic Games forAdaptive Trajectory Prediction and Planning","arxiv_id":null,"date":"2020-11-16","proceeding":null,"authors":["Simon Le Cleac’h","Mac Schwager and Zachary Manchester"],"abstract":"Existing  game-theoretic  planning  methods  assumethat the robot knows the objective functions of the other agentsa  prioriwhile,  in  practical  scenarios,  this  is  rarely  the  case.This paper introduces LUCIDGames, an inverse optimal controlalgorithm  that  is  able  to  estimate  the  other  agents’  objectivefunctions  in  real  time,  and  incorporate  those  estimates  onlineinto  a  receding-horizon  game-theoretic  planner.  LUCIDGamessolves   the   inverse   optimal   control   problem   by   recasting   itin  a  recursive  parameter-estimation  framework.  LUCIDGamesuses  an  unscented  Kalman  filter  (UKF)  to  iteratively  update  aBayesian estimate of the other agents’ cost function parameters,improving  that  estimate  online  as  more  data  is  gathered  fromthe  other  agents’  observed  trajectories.  The  planner  then  takesaccount  of  the  uncertainty  in  the  Bayesian  parameter  estimatesof  other  agents  by  planning  a  trajectory  for  the  robot  subjectto  uncertainty  ellipse  constraints.  The  algorithm  assumes  noexplicit  communication  or  coordination  between  the  robot  andthe  other  agents  in  the  environment.  An  MPC  implementationof  LUCIDGames  demonstrates  real-time  performance  on  com-plex  autonomous  driving  scenarios  with  an  update  frequencyof   40   Hz.  Empirical   results   demonstrate  that   LUCIDGamesimproves  the  robot’s  performance  over  existing  game-theoreticand  traditional  MPC  planning  approaches.  Our  implementa-tion  of  LUCIDGames  is  available  athttps://github.com/RoboticExplorationLab/LUCIDGames.jl","url_abs":"https://arxiv.org/abs/2011.08152","url_pdf":"https://arxiv.org/pdf/2011.08152.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lucidgames-online-unscented-inverse-dynamic-1","repo_url":"https://github.com/RoboticExplorationLab/LUCIDGames.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}