# MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction with Automation

arXiv: https://arxiv.org/abs/1711.06976 · HTML: https://mlsanity.com/paper/1711.06976/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2017.

## Built on by (most cited first, 15 of 15)

- [Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies](https://mlsanity.com/paper/2401.12888/) (2024)
- [VILD: Variational Imitation Learning with Diverse-quality Demonstrations](https://mlsanity.com/paper/1909.06769/) (2019)
- [Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification](https://mlsanity.com/paper/2211.06463/) (2022)
- [Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations](https://mlsanity.com/paper/2112.12707/) (2021)
- [Dynamic Conditional Imitation Learning for Autonomous Driving](https://mlsanity.com/paper/2211.11579/) (2022)
- [PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior](https://mlsanity.com/paper/2503.06477/) (2025)
- [FSDAM: Few-Shot Driving Attention Modeling via Vision-Language Coupling](https://mlsanity.com/paper/2511.12708/) (2025)
- [ADAS-TO: A Large-Scale Multimodal Naturalistic Dataset and Empirical Characterization of Human Takeovers during ADAS Engagement](https://mlsanity.com/paper/2603.06986/) (2026)
- [Survey and synthesis of state of the art in driver monitoring](https://mlsanity.com/paper/2110.00472/) (2021)
- [Latent Time Neural Ordinary Differential Equations](https://mlsanity.com/paper/2112.12728/) (2021)
- [Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer](https://mlsanity.com/paper/2310.13906/) (2023)
- [SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos](https://mlsanity.com/paper/2304.09247/) (2023)
- [CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild](https://mlsanity.com/paper/2306.15073/) (2023)
- [Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts](https://mlsanity.com/paper/2411.09199/) (2024)
- [A holistic perception system of internal and external monitoring for ground autonomous vehicles: AutoTRUST paradigm](https://mlsanity.com/paper/2508.17969/) (2025)

## Abstract (arXiv)

For the foreseeble future, human beings will likely 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. The governing objectives of the MIT Autonomous Vehicle Technology (MIT-AVT) study are to (1) undertake large-scale real-world driving data collection that includes high-definition video to fuel the development of deep learning based internal and external perception systems, (2) gain a holistic understanding of how human beings interact with vehicle automation technology by integrating video data with vehicle state data, driver characteristics, mental models, and self-reported experiences with technology, and (3) identify how technology and other factors related to automation adoption and use can be improved in ways that save lives. In pursuing these objectives, we have instrumented 23 Tesla Model S and Model X vehicles, 2 Volvo S90 vehicles, 2 Range Rover Evoque, and 2 Cadillac CT6 vehicles for both long-term (over a year per driver) and medium term (one month per driver) naturalistic driving data collection. Furthermore, we are continually developing new methods for analysis of the massive-scale dataset collected from the instrumented vehicle fleet. The recorded data streams include IMU, GPS, CAN messages, and high-definition video streams of the driver face, the driver cabin, the forward roadway, and the instrument cluster (on select vehicles). The study is on-going and growing. To date, we have 122 participants, 15,610 days of participation, 511,638 miles, and 7.1 billion video frames. This paper presents the design of the study, the data collection hardware, the processing of the data, and the computer vision algorithms currently being used to extract actionable knowledge from the data.