Socially Acceptable Interactive Decision Making for Autonomous Driving - Honda Research Institute USA

Socially Acceptable Interactive Decision Making for Autonomous Driving

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Socially Acceptable Interactive Decision Making for Autonomous Driving

Job Number: P24INT-22
This position focuses on researching 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 movements must align with human expectations, thereby enhancing user trust and pedestrian comfort. This internship involves the development of motion planners capable of generating smooth and predictable trajectories, 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 their findings at leading academic conferences.
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.
  • Evaluate the performance, safety, and robustness of the proposed algorithms in simulation environments, particularly in scenarios involving pedestrians and other vehicles.
  • Enhance and extend the existing motion planning stack in the simulator to incorporate socially acceptable behaviors and real-time adaptability.
  • 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 following: Active learning, Probabilistic Planning with Uncertainty, Interaction aware planning, Reinforcement Learning.
  • Must have experience with coding in C++, Python, and MATLAB.
  • Self-motivated and able to work independently

 

Bonus Qualifications

  • Research experience in Robotics/Automated vehicles Motion Planning and Control, Machine learning.
  • Strong publication record.
  • Strong software development experience.
  • Experience with using ROS-framework packages.
  • Experience using public datasets and simulators for navigation research.

 

Years of Work Experience Required  0
Desired Start Date 5/12/2025
Internship Duration 4 Months
Position Keywords Autonomous Driving, Decision Making, Social Norm

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