MIT Autonomous Vehicle Technology Study

It may be several decades before sensors, algorithms, and data collection are sufficiently developed to “solve” the full driving task. Until that time, human beings will remain an integral part of the driving task, monitoring the AI system as it performs anywhere from just over 0% to just under 100% of the driving. We launched the MIT Autonomous Vehicle Technology (MIT-AVT) study to understand, through large-scale real-world driving data collection and large-scale deep learning based parsing of that data, how human-AI interaction in driving can be safe and enjoyable. The emphasis is on objective, data-driven analysis. The following video introduces the study:

See arXiv paper for details on methods of data collection, processing, and study design. If you find this work useful in your own research, please cite:

BibTeX citation (click to expand)
author = {Lex Fridman and Daniel E. Brown and Michael Glazer and William Angell and Spencer Dodd and Benedikt Jenik and Jack Terwilliger and Julia Kindelsberger and Li Ding and Sean Seaman and Hillary Abraham and Alea Mehler and Andrew Sipperley and Anthony Pettinato and Linda Angell and Bruce Mehler and Bryan Reimer},
title = {{MIT} Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation},
journal = {CoRR},
volume = {abs/1711.06976},
year = {2017},
url = {},
archivePrefix = {arXiv},
eprint = {1711.06976}

To date, we have instrumented 21 Tesla Model S and Model X vehicles, 2 Volvo S90 vehicles, and 2 Range Rover Evoque vehicles for both long-term (over a year per driver) and medium term (one month per driver) naturalistic driving data collection. The study is ongoing and growing. We have 89 participants, 8,851 days of participation, 326,387 miles, and 4.2 billion video frames:

This work is supported by the AVT Consortium. Contact Lex Fridman ( for technical/engineering questions and Bryan Reimer ( for consortium questions.