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

Today, and possibly for a long time to come, the full driving task is too complex an activity to be fully formalized as a sensing-acting robotics system that can be explicitly solved through model-based and learning-based approaches in order to achieve full unconstrained vehicle autonomy. Localizati...

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Bibliographic Details
Published in:	IEEE access 2019, Vol.7, p.102021-102038
Main Authors:	Fridman, Lex, Brown, Daniel E., Glazer, Michael, Angell, William, Dodd, Spencer, Jenik, Benedikt, Terwilliger, Jack, Patsekin, Aleksandr, Kindelsberger, Julia, Ding, Li, Seaman, Sean, Mehler, Alea, Sipperley, Andrew, Pettinato, Anthony, Seppelt, Bobbie D., Angell, Linda, Mehler, Bruce, Reimer, Bryan
Format:	Article
Language:	eng
Subjects:	Algorithms Artificial intelligence Automation Automobile industry Automobiles Autonomous vehicles Autonomy Computer vision Data collection Data transmission Digital video Driver behavior Drivers Electric vehicles Extreme values High definition Human beings human factors human-robot interaction Instruments Machine learning neural networks Perception Roads Robotics Sensors Task analysis Trajectory control Trajectory optimization Trajectory planning Vehicles Video data
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Summary:	Today, and possibly for a long time to come, the full driving task is too complex an activity to be fully formalized as a sensing-acting robotics system that can be explicitly solved through model-based and learning-based approaches in order to achieve full unconstrained vehicle autonomy. Localization, mapping, scene perception, vehicle control, trajectory optimization, and higher-level planning decisions associated with autonomous vehicle development remain full of open challenges. This is especially true for unconstrained, real-world operation where the margin of allowable error is extremely small and the number of edge-cases is extremely large. Until these problems are solved, 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. The governing objectives of the MIT Advanced 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 the analysis of the massive-scale dataset collected from the instrumented vehicle fleet. The recorded data streams include IMU, GPS, and CAN messages, and high-definition video streams of the driver's 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, 15610 days of participation, 511638 mi, 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.
ISSN:	2169-3536 2169-3536