SPRING – Mountain View, CA

Honda Ri; Mountain View (CA); On-site; USD 34,000 - 48,000; Full time

Job summary

Honda Research Institute USA in Mountain View 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

Qualifications

• Ph.D. candidate in Robotics, CS, EE, ME, or a related field.
• Strong background in behavior planning and motion planning.
• Experience in reinforcement learning or multi-agent systems.
• Experience developing and evaluating algorithms for robotics or autonomous systems.
• Programming proficiency in Python and/or C++.
• Familiarity with PyTorch or TensorFlow.
• Ability to conduct independent research and prototype solutions.

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.

Skills

behavior planning

motion planning

decision making

reinforcement learning

autonomous driving

multi-agent systems

Education

Ph.D. candidate in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field

Tools

PyTorch

TensorFlow

ROS/ROS2

IPOPT

CasADi

Job description

Research Intern: Semantic-Aware Interaction and Behavior Planning for Autonomous Driving - Honda Research Institute USA

Job Number: P25INT-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.

SPRING – 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 TensorFlow.


• 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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