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 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. 01 231 4523 67 89 8 %& 'ÿ )*+ ,-,.,*/ÿ 0123 45 1 '142-,5 ,67ÿ 8+ *97 :; <=>ÿ @AB; CDÿ ; AE =F; GH ÿIJ KFL; M NM OFB; ÿ =F>DH ÿPQR SPT ULM VGLDH ÿPWW XGCM NY GDH ÿWZ [M Y GDÿ =LM VGBH ÿQPPR SI\ XM =GAÿ ] LF@GDH ÿJPPÿa b b a cd :; <=>ÿ =F; Fÿ NAY Y GN; M ABÿ M Dÿ ABeAM Bef ÿ :; F; M D; M NDÿ

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Multi-function in-vehicle interfaces are an increasingly common feature in automobiles. Over the past several years, these interfaces have taken on an ever-greater number of functions and the ways in which drivers interact with information have become more complex. Parallel with these technical developments, interest in ensuring that these systems minimize demand placed upon the driver has also increased. Voice command capability has become a popular and desirable feature, as interacting with a vehicle interface through auditory/vocal interactions is often hypothesized to allow the driver to keep their eyes on the road and hands on the wheel. However, research has shown that production level voice command systems may still impart considerable visual demands on the driver [18]. These demands might be due in part to screen displays associated with extensive confirmatory dialogue and the driver's desire for visual confirmation that commands were accurately recognized. This study extends this work by comparing the default mode of a production voice system with an "Expert" mode which streamlines tasks by removing several confirmatory steps. We found that, although the use of the Expert mode significantly reduces overall task completion time, it has no appreciable effect on the amount of visual engagement; drivers still glance off the road for durations that are consistent with the Default mode. Implications for interface design and driver safety are discussed.

Summary:Interaction with a voice-command interface for radio control, destination entry, MP3 song selection, and phone dialing was assessed along with traditional manual radio control and a multi-level audio-verbal calibration task (nback) on-road in 60 drivers. Subjective workload, compensatory behavior, and physiological indices of cognitive workload suggest that there may be both potential benefits and cautions in the implementation of a representative production level interface.

Summary:As the population has become both older and more technologically literate, a new class of "brain training" computer programs have gained in popularity. Though these programs have attracted substantial attention from scientists and consumers, the extent of their benefits, if any, remain unclear. Here we employ neuropsychological tests and behavioral metrics collected during periods of real-world driving (with and without manipulations of cognitive load) to evaluate the effects of training with Posit Science's DriveSharp software. We find that DriveSharp's training effects appear in in-lab measures of Useful Field of View but did not translate to changes in actual driving performance or changes in visual behavior in consistent or quantifiable ways in the sample assessed. The implications of these results and relevant limitations of the present research are discussed.

A simulator study evaluated the extent to which the use of a smartwatch to initiate phone calls while driving impacts driver workload, attention, and performance, relative to visual-manual (VM) and auditory-vocal (AV) calling methods on a smartphone. Participants completed four calling tasks using each method while driving in a simulator and completing a remote detection response task (R-DRT). Among the 36 participants evaluated, R-DRT miss rates and reaction time were comparable between AV calling on the smartwatch and smartphone, but significantly higher using VM calling on the smartphone. Participants also exhibited more erratic driving behavior (lane position deviation and major steering wheel reversals) with smartphone VM calling compared to both AV calling methods. Finally, participants rated AV calling on both devices as entailing lower workload than VM calling on the smartphone. Overall, few differences emerged for the metrics reported between voice calling on a smartphone versus a smartwatch while driving.

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