Abstract
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.
Highlights
The idea that human beings are poor drivrs is welldocumented in popular culture [1], [2]
Fridman et al.: MIT Advanced Vehicle Technology (MIT-Advanced Vehicle Technology (AVT)) Study: Large-Scale Naturalistic Driving Study when 6 of the 11 autonomous vehicles in the finals successfully navigated an urban environment to reach the finish line, with the first place finisher traveling at an average speed of 15 mph
Over ten years later, the problems of localization, mapping, scene perception, vehicle control, trajectory optimization, and higher-level planning decisions associated with autonomous vehicle development remain full of open challenges that have yet to be fully solved by systems incorporated into a production platforms for even a restricted operational space
Summary
The idea that human beings are poor drivrs is welldocumented in popular culture [1], [2]. The MIT Advanced Vehicle Technology (MIT-AVT) study seeks to collect and analyze large-scale naturalistic data of semi-autonomous driving in order to better characterize the state of current technology use, to extract insight on how automation-enabled technologies impact humanmachine interaction across a range of environments, and to understand how we design shared autonomy systems that save lives as we transition from manual control to full autonomy in the coming decades. A semi-structured interview is conducted in person between a research associate and the study participant at the end of the one-month naturalistic driving period, and lasts approximately 30-60 minutes It consists of predefined questions focusing on initial reactions to the vehicle, experience during the training drive, how training affected their understanding of the technologies, and driver perceptions of the technologies
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