[Machine Learning/AI] Path Planning and Lane Selection Research

[Machine Learning/AI] Path Planning and Lane Selection Research

Job Number: P20INT-45
This position investigates how formal path planning methods and/or data driven approaches can be used as a multi-objective lane selection algorithm.
San Jose, CA

​​​​Highway is an essential road type in urban transportation systems and one of the major areas where fatal accidents occur. Motion planning under high speed traffic hence requires multi-directional perceptions and prediction of other drivers, as well as a timely decision making, and needs to be formulated as a multi-objective problem.

You are expected to:

  • Implement state-of-the-art long horizon lane selection algorithms (for lane choice and/or gap choice).
  • Research and develop a long horizon lane selection algorithm while considering risk, drive comfort, and travel time.


  • M.S. or Ph.D. candidate computer science, or related STEM field.
  • Strong familiarity and research experience in deep learning, optimizations, and control of robotic systems.
  • Highly proficient in software engineering using C++ and/or Python.

Bonus Qualifications:

  • Experience with deep learning software like TensorFlow or PyTorch.
  • Familiarity with ROS.
  • Hands-on experience with robotic control.

Duration: 3 months

How to apply

Candidates must have the legal right to work in the U.S.A.​ Please add Cover Letter and CV in the same document

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