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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This work may have important implications for attention management in the context of the increasing prevalence of in-vehicle demands as well as of vehicle automation.

Until recently most models of word recognition have assumed that semantic auditory naming effects come into play only after the identification of the word in question. What little evidence exists for early semantic effects in word recognition lexical decision has relied primarily on priming manipulations using the lexical decision task, and has used visual stimulus presentation. The current study uses semantics auditory stimulus presentation and multiple experimental tasks, and does not use priming. Response latencies for 100 common nouns were found to speech perception depend on perceptual dimensions identified by Osgood (1969): Evaluation, Potency, and Activity. In addition, the two-way interactions between these word recognition dimensions were significant. All effects were above and beyond the effects of concreteness, word length, frequency, onset phoneme characteristics, stress, and neighborhood density. Results are discussed against evidence from several areas of research suggesting a role of behaviorally important information in perception.

Summary: Improved measures of the attentional effects of cognitive load are needed to reduce potential crashes caused by secondary tasks performed while driving. The Tactile Detection Response Task (TDRT) in the proposed ISO Draft Standard WD17488 was tested in laboratory and on-road venues with 16 and 15 subjects, respectively. A sensitivity test used a purely cognitive load increase from an easy (0-back) to hard (1-back) auditory-vocal task. The TDRT response time increased by 90±21 msec in the laboratory, and by 135±34 msec on the road, while the miss rate increased by 4% in the laboratory and 5% on the road, thus validating TDRT sensitivity to an increase in purely cognitive load. A specificity test used a visual load increase with little cognitive load difference from an easy to hard visual-manual "Surrogate Reference Task" (SuRT), to which the TDRT should not respond. The TDRT response time and miss rate to the SuRT did not increase in the laboratory or road as a result of the increased visual load, providing preliminary validation that the TDRT may be both specific and sensitive to the attentional effects of cognitive load.