Interaction-Aware Motion Planning for Autonomous Driving - Honda Research Institute USA

Interaction-Aware Motion Planning for Autonomous Driving

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Interaction-Aware Motion Planning for Autonomous Driving

Job Number: P24INT-37
This position focuses on researching interaction-aware motion planning and decision-making algorithms for autonomous driving (AD) applications. Autonomous vehicles frequently interact with pedestrians and other vehicles, requiring them to learn intentions in real-time and adapt motion plans accordingly. To ensure safe and reliable operation, the vehicle's movement must align with human expectations. This internship involves the development of an interaction-aware motion planner in uncertain dynamic environments within the model predictive control (MPC) framework, integrating social norms (e.g., yielding to pedestrians at crossings or signaling intentions) into the planning process, and exploring the trade-off between operational efficiency and social compliance. The intern will collaborate closely with HRI scientists and engineers and have the opportunity to publish research findings at leading academic conferences or journals.
Mountain View, CA

 

Key Responsibilities

 
  • Conduct a comprehensive literature review and analyze state-of-the-art approaches on topics relevant to the internship.
  • Develop, implement, and test novel algorithms for autonomous driving focusing on areas such as interaction-aware decision-making, socially compliant motion planning, and intention recognition in uncertain dynamic environments to be used within an MPC framework.
  • Enhance and extend the existing MPC-based motion planning stack to incorporate socially acceptable behaviors and real-time adaptability.
  • Evaluate the performance, safety, and robustness of the proposed algorithms in simulation environments, particularly in uncertain dynamic scenarios involving pedestrians and other vehicles.
  • Collaborate with HRI scientists and engineers to refine methodologies and integrate findings into practical applications.
  • Publish research results in leading academic conferences or journals.

 

Minimum Qualifications

 
  • Ph.D. candidate in Robotics, Computer Science, Mechanical Engineering, or similar fields.
  • Strong background and experience in at least one of the followings: Active Learning, Probabilistic Motion Planning and Decision-Making under Uncertainty, Interaction-Aware Motion Planning, Reinforcement Learning.
  • Experience with model predictive control (MPC).
  • Programming and software design skills in C++ and Python.
  • Self-motivated and able to work independently.

 

Bonus Qualifications

  • Strong publication record in top-tier research conferences or journals.
  • Strong software development experience.
  • Fluency in Object-Oriented Programming (OOP) with C++.
  • Experience with ROS2 (Robot Operating System).
  • Experience using public datasets and simulators for autonomous driving.

 

Years of Work Experience Required  0
Desired Start Date 9/15/2025
Internship Duration 3 Months
Position Keywords Interaction-aware decision making, Socially compliant motion planning, Intention recognition, Model Predictive Control (MPC), Autonomous driving.

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