Research Intern: Semantic-Aware Interaction and Behavior Planning Autonomous Driving
Listed on 2026-09-12
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Research/Development
Robotics
Research Intern:
Semantic-Aware Interaction and Behavior Planning for Autonomous Driving - Honda Research Institute USA Research Intern:
Semantic-Aware Interaction and Behavior Planning for Autonomous Driving
Job Number: P25IN
T-56
Honda Research Institute USA (HRI-US) is seeking a research intern to develop semantic-aware interaction and behavior planning methods for autonomous driving. The intern will investigate how scene context, agent intent, interaction information, and semantic relationships can be used to make higher-level driving decisions such as when to proceed, yield, wait, reroute, or adapt to other road users. The developed methods will integrate with HRI’s existing real-time navigation and motion-planning stack and will be evaluated in challenging low-speed autonomous-driving and parking scenarios.
Mountain View, CA
Key Responsibilities
- Conduct research on semantic-aware behavior planning and interaction-aware decision making for autonomous driving.
- Develop methods that use scene context, agent intent, interaction relationships, and uncertainty to make high-level driving decisions.
- Investigate representations such as semantic features, scene/interaction graphs, multi-hypothesis agent behaviors, or learned behavior models.
- Develop decision-making strategies for interactions such as yielding, passing order, waiting, rerouting, and conflict resolution.
- Integrate developed methods with existing HRI navigation and motion-planning algorithms.
- Train and evaluate methods using simulation and real-world driving datasets.
- Evaluate both algorithm-level performance and impact on closed-loop autonomous-driving behavior.
- Collaborate with HRI researchers and contribute toward technical publications and system demonstrations.
Minimum Qualifications
- Ph.D. candidate in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field.
- Strong background in at least one of the following: behavior planning, motion planning, decision making, reinforcement learning, autonomous driving, or multi-agent systems.
- Experience developing and evaluating algorithms for robotics or autonomous systems.
- Programming proficiency in Python and/or C++.
- Familiarity with machine learning frameworks such as PyTorch or Tensor Flow.
- Ability to conduct independent research and prototype solutions.
Bonus Qualifications
- Experience with interaction-aware planning, behavior planning, or decision making for autonomous vehicles or mobile robots.
- Experience with numerical optimization and optimization-based planning, including familiarity with QP/NLP solvers or tools such as IPOPT, CasADi, or similar.
- Familiarity with intent prediction, trajectory prediction, semantic reasoning, or scene understanding.
- Experience with methods such as reinforcement learning, graph-based representations, or uncertainty-aware planning.
- Experience integrating learning-based or decision-making algorithms with downstream motion planners.
- Experience with ROS/ROS2 and autonomous-driving or robotics simulation environments.
- Experience working with real-world autonomous-driving datasets and closed-loop evaluation.
- Experience implementing algorithms on a real robotic or autonomous vehicle platform is a plus.
- Prior research publications in robotics, autonomous driving, machine learning, or related areas are a plus.
Years of Work Experience Required 0
Desired Start Date 1/11/2027
Internship Duration 3 Months
Position Keywords Autonomous Driving,Robotics,Behavior and Motion Planning
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