Human Interactive Driving Intern - Human Aware Interaction and Learning

At Toyota Research Institute (TRI), we're on a mission to improve the quality of human life. We're developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we've built a world-class team in Automated Driving, Energy & Materials, Human-Centered AI, Human Interactive Driving, Large Behavior Models, and Robotics.

This is a Summer 2026 paid 12-week internship opportunity. Please note that this internship will be a hybrid in-office role.

The Mission

Our team works to understand how people behave on the road, understand the human driver, and understand how to interact with them.

The Team

Within the HAIL team, we use approaches from machine learning, robotics, and computer vision, along with insights from human factors literature, to devise new techniques that improve on the state of the art towards better machine understanding, prediction, and interactions with people in the driving domain, both in and around the vehicle.

The Internship

We are looking for motivated interns with excellent capabilities for research in various areas of AI around autonomous vehicles and related topics. Areas of interest include multi-agent modeling and human-robot interactions with emphasis on environments such as autonomous and semi-autonomous vehicles. Related topics include trajectory prediction, driver state and behavior understanding, activity recognition, shared control, as well as human-machine teaming/interaction and computational teaching of humans.

In this project, we're looking at creating innovative approaches that reframe how driver and intelligent vehicles interact. Our overall goal is to create AI approaches that challenge more traditional approaches for prediction, planning, and interaction with the driver, and approach the interaction as longer-term human-AI teaming, including co-adaptation and teaching, with multiple research opportunities along the way.

The project will be focused on researching a new approach in a specific sub-area of the overall goal, towards publication in a top-tier conference. Aside from the publication goal of the internship, the technology developed in the internship is crucial for next-generation safety systems, (semi-)autonomous driving systems, and novel robotic applications of the kind that TRI is helping create. The internship will be in our Cambridge, MA research center, where you will be working with strong researchers towards publications that matter, with many resources to utilize, and with competitive compensation for your hard and fun work.

TRI internships promise a summer of research and problem-solving in areas of impact on autonomous driving, robotics, and society in general. Applicants with relevant publications in these fields and good collaboration skills are highly encouraged to apply.

Responsibilities

* Perform research and publish in a relevant venue on relevant topics. Publication target venues include CVPR, ICRA, NeurIPS, and HRI, depending on the exact project outcome. Emphasis on how novel representations allow us to capture human characteristics and interact with humans towards long-term shared autonomy, in a data-efficient, robust, and explainable way. The exact topic is to be finalized with the mentor.

* Exploration of both computational and cognitive phenomena, working with a team of researchers to create new approaches for understanding, predicting, and interacting with humans.

* Work from approach inception and ideation to validation of the developed approaches.

Qualifications

* Ph.D. Student in related fields - ML/AI, robotics, human-centric AI, or computer vision.

* Publication background in relevant venues in the field. Specific areas:
Imitation/reinforcement learning (RL), control and decision theory, human behavior modeling and understanding, shared control, and computational tutoring.

* Frameworks:
Pytorch/Tensor Flow, and similar DL frameworks and tools.

* Coding:
Python, experience working in a team on joint scientific projects.

* An ideal candidate can refine a topic and complete a scientific research plan in collaboration with the mentor and other researchers.

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