Highway Lane change under uncertainty with Deep Reinforcement Learning based motion planner

• Author / Creator

  Sakib, Nazmus

• Motion Planning is a fundamental component of a mobile robot to reach its
  goal safely avoiding collision. For a self-driving car on a highway, the presence of non-communicating vehicles, specially those whose intent is unknown,
  creates a lot of uncertainty for the motion planner in generating a safe trajectory. State-of-the-art planning methods do not work well in case of adversary driving scenarios, where the other vehicles may make mistakes or have
  a competing or malicious intent. We use reinforcement learning framework
  to improve safety under those scenarios. In most recent deep reinforcement
  learning applications, there is a neural network that maps an input state to
  an optimal policy over actions. However, learning a policy over such original
  or primitive actions is very slow and inefficient and is therefore not suitable
  for many robotics tasks. On the other hand, the knowledge already learned in
  classical planning methods should be inherited and reused. In this thesis, in
  order to take advantage of reinforcement learning good at exploring the action
  space for optimal solution and classical planning skill models good at handling
  most driving scenarios, we propose to learn a policy over an action space of
  primitive actions augmented with classical planning methods. By doing so, we
  show that our agent outperforms the primitive-action reinforcement learning
  agent and the classical planning methods in terms of collision rate

• Subjects / Keywords

  • Motion Planner
  • Deep Reinforcement Learning
  • Robotics

• Graduation date

  Spring 2020

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-qm5k-s682

• License

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