63-whats in a name- vehicle technology branding consumer expectations for automation

1、Proceedings of the 9th ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 17), September 2427, 2016, Oldenburg, Germany.Whats in a Name: Vehicle Technology Branding & Consumer Expectations for AutomationHillary Abraham MIT AgeLab Cambridge

2、, US Bobbie Seppelt MIT AgeLab & Touchstone Evaluations Cambridge, US Bruce Mehler MIT AgeLab Cambridge, US Bryan Reimer MIT AgeLab Cambridge, US ABSTRACTVehicle technology naming has the potential to influence drivers expectations (mental mod

3、el) of the level of autonomous operation supported by semi-automated technologies that are rapidly becoming available in new vehicles. If divergence exists between expectations and actual design specifications, it may make it harder to develop trust or clear expectations of systems, thus mitigating

4、potential benefits. Alternately, over-trust and misuse due to misunderstanding increase the potential for adverse events. An online survey investigated whether and how names of advanced driver assistance systems (ADAS)common definitions fordifferent types of automation invehicles, the Society of Aut

5、omotive Engineers (SAE) developed a taxonomy with detailed descriptions for vehicles equipped with automated features 24. At present, consumers are only able to purchase vehicles equipped with driver assistance (Level 1) and partial automation (Level 2) systems. However, several automotive manufactu

6、rers have announced production vehicles to be available this year with conditional automation (Level 3). High automation (Level 4) technologies are being tested globally with expected commercial availability being forecast in less than 5 years 15.Efforts to develop ADAS and automation features are b

7、ased upon manufacturer-specific design specifications. These specifications aim to produce a technology with the capability to perform in a particular operational design domain (ODD). The system implementation and specific use conditions encompassed in the static and dynamic aspects of the ODD 28 ar

8、e representative of a system designers mental model for the technology. How drivers learn about individual systems is influenced by their pre- existent mental models those formed prior to initial use, e.g., from exposure to other technologies 12. A drivers mental model aids him or her in understandi

9、ng a systems ODD, interface characteristics and other system limitations necessary for proper system use 4,27. While driver education and other more active methods for encouraging proper use (in vehicle coaching, etc.) face challenges at each level of automation, the most relevant current challenge

10、exists with partial driving automation (Level 2), for which governments, businesses, researchers and consumers have argued the marketing name of a system may promote the misalignment of driver and designer expectations 5,7,18. In Level 2 automation, the system performs sustained lateral and longitud

11、inal management of the driving task, while the driver performs the remaining subtasks, including object and event detection and response (OEDR). Driver belief that a system has the ability to perform OEDR at a level greater than the systems design characteristics may lead to misuse 22.Human Machine

12、Interfaces (HMIs) for automated features are intended, by design, to help support driver understanding of features and to promote proper systemand automation levels. Systems associated withfeatures relate to expected automationwith “Cruise” in their names lower levels of automation. “Assist”weresyst

13、ems appeared to create confusion between whether the driver is assisting the system or vice versa. Survey findings indicate the importance of vehicle technology naming andits impact ininfluencing drivers expectations ofresponsibility between the driver and system performs individual driving function

14、s.Author KeywordsinwhoAdvanced Driver Assistance Systems; Branding; Automation; ConfusionCCS Concepts Human-centered computingUser centered designINTRODUCTIONMost automotive manufacturers now offer, or are currently pursuing research on, advanced driver assistance systems (ADAS) and automated drivin

15、g features. Collectively, semi- automated vehicle technologies (ADAS and lower level automation systems) are rapidly becoming standard or optional features on new vehicles. In order to help providePermission to make digital or hard copies of all or part of this work for personal or classroom use is

16、granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored.Abstracting with credit is permitted. To c

17、opy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from P.AutomotiveUI 17, September 2427, 2017, Oldenburg, Germany 2017 Association for Computing Machinery. ACM ISBN 978-1-4503-5150-8/17/

18、09$15.00/10.1145/3122986.3123018226Session 7 - AV-Driver Interaction Paradigms: What is the Role of the Human? AutomotiveUI 17, Oldenburg, Germanyuse. At Levels 1 and 2, in which features assist drivers for only a partial set of the dynamic tasks of driving, their HMIs aim to support d

19、rivers in maintaining their attention to the roadway. One adopted implementation strategy to support this aim (e.g. Tesla, Volvo, etc.) is to require drivers to keep their hands on the wheel with minimal steering input; however, the amount of input and amount of time a driver can go before hands-off

20、-wheel warnings are issued varies between system and use conditions, resulting in the potential for prolonged intervals of declining situation awareness. Further, there is not currently a proven link between hands-on-wheel during Level 2 use and situational awareness. Looking to enforce a greater de

21、gree of control on driver attentiveness, GMs SuperCruise, anticipated to be commercially available in the 2018 Cadillac CT6, is reported to be designed with an integrated head pose detection system in order to monitor driver awareness and to trigger a range of cues to promote driver attentiveness 8.

22、 The standardization of such approaches iscurrent and proposed driver assistance / automation systems impact driver expectations may help guide future naming discussions and considerations for standardization. A survey was designed to investigate two primary research questions:1.Does the name of dri

23、ver assistance systems affect a customers perception of the level of automation of that system?If so, do commonly used terms when branding ADAS (e.g. Auto, Pilot, Assist, Cruise) direct consumer perceptions toward presumptions of lower or higher levels of automation?2.METHODParticipantsParticipants

24、were recruited using online advertisements and web posts to the MIT AgeLab website. In total, a convenience sample of 453 participants was analyzed. The sample was 37% male and 61% female; the remaining 2.6% of individuals selected “Other or choose not to answer.” Age of respondents ranged from 20-6

25、9, with 30% of respondents in their 20s, 19% in their 30s, 6% in their 40s, 18% in their 50s, and 27% in their 60s. Respondents were generally highly educated; 38% had completed a graduate or professional degree as their highest level of education, 18% had completed some graduate education, 29% had

26、completed a Bachelors degree, 2% had an Associates degree, 1% had a trade school certificate, 12% had completed some college, 1% had graduated high school, and 0% had completed some high school. Most respondents (71%) were from the state of Massachusetts in the USA.Survey InstrumentSystems Addressed

27、Nineteen driver assistance systems were selected for inclusion in the survey (Table 1). Attempts were made to incorporate all systems commercially available or publicly proposed at the time of survey deployment that feature both adaptive cruise control and a lane centering component, yet require the

28、 driver to engage in some of the dynamic aspects of driving, either actively or as a fallback-ready user (e.g. Level 1 Level 3). Researchers were particularly interested in how common English terms might affect perceptions of system capabilities; as such, systems that included the name of the manufa

29、cturer in their title were not included (e.g. Honda Sensing). Four fabricated system names were included in the survey to explore differences between terms typically used in systems at higher levels of automation and those typically used for systems at lower levels.currently under consideration in E

30、urope 9 and supported by research 23.Multiple factors contribute to a drivers expectationsisofsystem capability e.g., 1,13,21,22,26. Drivers attitudes and beliefs about system capability and performance are known to influence their use of technology 6,10,14,30.Factors such as a drivers prior experie

31、nce with similar technologies, predisposed trusting tendencies, and attitudes formed from exposure to media and societal opinion might all contribute to a drivers belief that a system can handle a task outside of its ODD.The name of a driver assistance system also has the potential to impact their p

32、erceptions of system capability. From consumer psychology research, there is an ascribed importance of branding and the names assigned to products; naming influences expectation of product attributes and preconditions consumers to assign valence based on induced biases 17. In application to driving

33、automation systems, the names assigned to technologies have the potential to shape driver perceptions in a way that bias attitudes and affect use 30. Other than a small survey by Tesla 31, little structured research has investigated whether the name of a system impacts driver expectations of a syste

34、m, particularly in relation to what the driver expects their role should be while using the vehicle and system.As brand names are increasingly used to discuss vehicle automation systems with a vast range of design models, improved understanding of whether or not brand names of227Session 7 - AV-Drive

35、r Interaction Paradigms: What is the Role of the Human? AutomotiveUI 17, Oldenburg, Germanyresponsible for, while the system was engaged or active. Categorizations generalized ODD and dynamic driving task (DDT) into broad categories of responsibility, rather than listing and requesting classificatio

36、ns for individual ODDs and DDTs, in an attempt to avoid overwhelming the survey respondents (Figure 1).Survey Design MethodologyAfter selecting systems for inclusion and developing a first draft of automation categories, a survey instrument was developed by the research team. This instrument went th

37、rough a series of internal revisions before piloting with additional staff members not involved in the project to ensure layman understanding of all terms and definitions involved. After piloting, research staff spoke informally with pilot subjects about the survey design, format, and clarity of que

38、stions. Pilot subject feedback was integrated into the final instrument detailed within this report.Survey ProcedureParticipants were first presented with a brief introduction to the survey and a description of each level of automation (Figure 1). After reading the introduction and level classificat

39、ions, participants were asked to imagine they were the driver in a vehicle equipped with an automated system. Participants were then provided with the list of 19 systems. For each system, participants selected the category from the seven levels of automation that best described the division of task

40、responsibility that they would expect to exist between them as the driver and the system. In order to maximize the likelihood that categorization would be made based on name alone, survey takers were instructed not to use any outside resources when making their categorization. After assigning a leve

41、l to a system, participants rated their confidence in their level assignment on a 5-pt scale ranging from 1 (low confidence) to 5 (high confidence). This was repeated for all 19 systems.After assigning every system to a category of automation and rating their confidence in their assignment, particip

42、ants were asked, “before taking this survey, how familiar were you with any of the systems?” and provided a 5-pt scale ranging from “Not familiar at all” to “Extremely familiar.” Participants were asked six questions to gauge their early adopter status, vehicle information, and whether or not any of

43、 their vehicles had any of the survey systems installed.Active Cruise Control AutoCruiseAutopilot Distronic Plus Drive PilotDriving Assistant Plus Enhanced Autopilot EyesightHighway Pilot Intelligent AssistIntelligent Cruise Control Intelligent Drive Intelligent PilotPilot Assist Pilot Plus ProPilot

44、 Super CruiseTraffic Jam AssistTraffic Jam PilotBMWN/A TeslaMercedes-Benz Mercedes-Benz BMWTesla Subaru Audi N/A NissanMercedes-Benz N/A Volvo N/ANissan GMAudiAudiAvailable N/A Available Available Available AvailableIn Development AvailableIn Development N/AAvailable Available N/A Available N/AIn De

45、velopment In Development AvailableIn Development1N/A 211.51.5313N/A 11N/A 1.5N/A 1.521.53Table 1. Systems, availability, and level of automation (LoA) at time of survey deployment.Automation CategoriesSeven descriptions of differing levels of automation were created for participants to classify syst

46、ems (Figure 1). These categories were developed based on the six SAE J3016 levels of automation 24, plus an additional level (“L1.5,” conceptually between 1 & 2) to accommodate commercially available systems that require the driver to keep their hands on the wheel at certain frequencies, as a functi

47、on of the adopted implementation strategy, in order to perform continuous lane centering. Care was taken to ensure these categories accurately represented J3016 levels, while simultaneously being understandable to the layman in terms of the division of driving task responsibility. Particular attenti

48、on was paid to the distinction between general tasks the driver would be responsible for, versus general tasks the driving assistance system would beFigure 1. After a survey introduction, participants were presented with this graphic representing seven categories of automation, ranging from SAE Leve

49、l 0 (fully manual, far left box) to SAE Level 5 (fully automated, far right box). These seven categories provide laymans definitions of the division of driving task responsibility between driver and system.228SystemManufacturerAvailabilityLoASession 7 - AV-Driver Interaction Paradigms: What is the R

50、ole of the Human? AutomotiveUI 17, Oldenburg, GermanyNot at all familiarSlightly familiarModerately familiarVery familiarExtremely familiar100%90%80%70%60%50%40%30%20%10%0%Active Autopilot Distronic Drive Pilot Driving Enhanced Eyesight Highway Intelligent Intelligent Pilot Assist ProPilot Supercrui

51、seTraffic Jam TrafficCruise ControlPlusAssistant Autopilot PlusPilotCruise ControlDriveAssistJam PilotFigure 2. Participants rated themselves as being not at all familiar with most of the systems prior to taking the survey.The survey ended by collecting demographic information,analyses, respondents

52、were grouped into five age ranges: 20-29, 30-39, 40-49, 50-59, and 60-69.Familiarity & CorrectnessMost participants selected “not familiar at all” for familiarity with each of the systems prior to taking the survey (Figure 2). Two systems, Active Cruise Control and Autopilot, had higher levels of fa

53、miliarity than the other systems in the sample. Importantly, it is unclear whether or not respondents were familiar with Teslas Autopilot, the term “autopilot” within the context of aviation, or the colloquial “autopilot,” used when referring to completing a task absentmindedly or without focus. Whi

54、le more respondents were familiar with these systems, more than half (54.5% and 66.2% respectively) reported being either “not familiar at all” or only “slightly familiar” with either system.including date of employment status, code.birth, highest level of education,household income, gender, andzipP

55、articipants who completed the survey were offeredtheopportunity to enter a raffle to win one of 10 $50 Amazongift cards. The survey was constructed in Qualtrics, and participants were asked to take the survey online via a desktop or laptop computer. The survey was open for data collection from Febru

56、ary 22nd March 6th 2017.RESULTSData were analyzed using SPSS Version 24. As analyses were run multiple times (once for each system), a Bonferroni correction was used to determine significance. Significance was set at p .0026 for analyses of all 19 systems (.05 / 19), and p .0033 for analyses of the

57、15deployedor in-development systems(.05/ 15). ForageTable 2. Overall accuracy for system categorization was low. There was no relationship between correct categorization and confidence. Most participants did not select L0 for most systems.229Session 7 - AV-Driver Interaction Paradigms: What is the R

58、ole of the Human?AutomotiveUI 17, Oldenburg, GermanyTable 3 No significant differences were exhibited in gender and accuracy, but significant gender differences were exhibited in confidence and familiarity with systems.Most respondents did not accurately classify most systems into their correct level of automation. While most systems had a slightly higher percentage of correct categorizations than would be expected from random guessing (14% corre