Daniel E. Brown

Work - Research - Publications


Massachusetts Institute of Technology

Primary focus on automotive safety research using semi-autonomous driving features, HMI distractions, data collection and analysis. I have experience building and integrating intelligent systems into Tesla, Ford, GM, Volvo, Toyota, Jaguar Land Rover, and Mercedes vehicles. Some of our work has recently been featured in NPR news

Currently I am a teaching assistant for the MIT IAP course 6.S094 Deep Learing for Self-Driving Cars

Main work involved conceiving, prototyping, building, iterating and thoroughly testing RIDER, Real-time Intelligent Driving Environment Recording System. RIDER is a new compact low-cost automotive data logging platform to capture GPS, Acceleration, Gyroscope, Magnetometer, Vehicle Telemetry Data throughout differing data types including CANbus data - along with audio and four HD video streams.

RIDER is currently employed in the MIT founded Advanced Vehicle Technologies consortium - joined by research groups, automotive companies, tier 1 suppliers, and insurance companies. AVT is researching drivers behavior using ADAS and Level 2 automated driving systems. Currently 20 RIDER unit are out in the field collecting every day naturalistic driving data.

RIDER utilizes a low cost single board computer, along with self designed and manufactured PCBs:
Merlin's Hat
The Knights of CANelot

I also manage our labs' fleet of research vehicles, from instrumentation and conducting research studies to keeping up with oil changes and vehicle inspection.

University of Massachusetts - Amherst

Assisted Professor Joseph Goldstein’s lab research in artificially forming Tetrataenite a highly magnetic phase of iron-nickel found primarily in meteorites. 

NeoGraft Technologies, Inc. Taunton, MA

Summer 2011
Performed experiments collecting and evaluating data using scanning electron microscopy and force testers on polycaprolactone biodegradable polymer coatings on large animal model veins for coronary bypass surgery. Collaborated with senior engineers in conceiving designs and building specialty electrical and electromechanical assemblies to add functionality, value and analytical capability to specific process instruments.


Comparing effectiveness and use of near-infrared (NIR) HD cameras along with visible light cameras in vehicles for in-cab and exterior use.

Currently I am playing around with a mix of things...CAN hacking, optics for cheap robust IR vision, behavior and function of ADAS system and autonomous vehicles. Maybe some CVD stuff (the materials sci side of me). 

If you're curious what I'm up to now, send me an email.

Below are some videos created by Lex Fridman using the RIDER system to capture all sensor data, then synchronized and processed through computer vision algorithms to return the visual and quantitate result of the drivers, (my) gaze.

Car sync demo video


Thomas McWillams, Daniel E. Brown, Bryan Reimer, Bruce Mehler, and Jonathan Dobres. “Observed Differences in Lane Departure Warning Responses during Single-Task and Dual-Task Driving: A Secondary Analysis of Field Driving Data”. In SAE Technical Papers. [SAE Papers]

Irman Abdić, Lex Fridman, Erik Marchi, Daniel E. Brown, William Angell, Bryan Reimer, and Björn Schüller. “Detecting Road Surface Wetness from Audio: A Deep Learning Approach”. In In IEEE International Conference on Pattern Recognition, 2016. [arxiv] [pdf]

Lex Fridman, Daniel E. Brown, William Angell, Irman Abdic, Bryan Reimer, Hae Young Noh, "Automated Synchronization of Driving Data Using Vibration and Steering Events", In Pattern Recognition Letters, 2016. [arxiv] [pdf]
More info on Car Sync

Brown, D., Reimer, B., Mehler, B., & Dobres, J. An on-road study involving two vehicles: observed differences between an auditory and haptic lane departure warning system. Proceedings of the 2015 International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Nottingham, UK, September 1-3, 2015. doi: 10.1145/2809730.2809747(Poster)


Daniel E. Brown

P:(617) 253-3238
E: danbr@mit.edu