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Decision making and trajectory planning for intelligent vehicles using partially observable Markov decision processes and inverse reinforcement learning

Grant number: 18/19732-5
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): January 01, 2019
Effective date (End): February 28, 2021
Field of knowledge:Engineering - Electrical Engineering
Principal Investigator:Denis Fernando Wolf
Grantee:Júnior Anderson Rodrigues da Silva
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated research grant:14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment, AP.TEM

Abstract

Autonomous mobile robots that share the same environment with people require motion planning approaches that resemble, in some aspects, the human behavior. Such behavior facilitates other agents to predict the robot's movement’s vice-verse, resulting in a smoother joint navigation. This is presumably the scenario that autonomous vehicles will face: they will share the traffic roads with other vehicles (autonomous or not), integrating cooperatively to them. This doctorate project proposes the development and implementation of a decision making framework for autonomous vehicles in an urban scenario. The actions of other vehicles will be inferred through Inverse Reinforcement Learning, whereby the behavior of agents is learned through the imitation of an expert. Decision making will take place through Partially Observable Markov Decision Processes, since the actions of other vehicles can not be observed directly. Finally, a trajectory will be planned and executed considering the expert driving style. The test platform CaRINA II, a probe vehicle under development by the Laboratory of Mobile Robotic at USP São Carlos, will be used in results validation. The test environment will be the Campus 2 at USP - São Carlos, since it has attributes of an urban scenario. This project is part of the Thematic Project supported by FAPESP, process n. 2014/50851-0 "National Institute of Science and Technology for Cooperative Autonomous Systems Applied to Security and Environment". (AU)