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What’s in a Name: Vehicle Technology Branding & Consumer Expectations for Automation

Hillary Abraham MIT AgeLab

Cambridge, US [email protected]

Bobbie Seppelt MIT AgeLab &

Touchstone Evaluations


Bruce Mehler MIT AgeLab


Bryan Reimer MIT AgeLab


ABSTRACT Vehicle technology naming has the potential to influence drivers’ expectations (mental model) 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 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) and automation features relate to expected automation levels. Systems with “Cruise” in their names were associated with lower levels of automation. “Assist” systems appeared to create confusion between whether the driver is assisting the system or vice versa. Survey findings indicate the importance of vehicle technology naming and its impact in influencing drivers’ expectations of responsibility between the driver and system in who performs individual driving functions.

Author Keywords Advanced Driver Assistance Systems; Branding; Automation; Confusion

CCS Concepts • Human-centered computing~User centered design INTRODUCTION Most automotive manufacturers now offer, or are currently pursuing research on, advanced driver assistance systems (ADAS) and automated driving 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 provide

common definitions for different types of automation in vehicles, the Society of Automotive 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 manufacturers 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 based 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] are representative of a system designer’s 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 driver’s mental model aids him or her in understanding a system’s 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 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 longitudinal 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 system’s design characteristics may lead to misuse [22].

Human Machine Interfaces (HMIs) for automated features are intended, by design, to help support driver understanding of features and to promote proper system

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Proceedings of the 9th ACM International Conference on Automotive User Interfaces and Interactive VehicularApplications (AutomotiveUI ’17), September 24–27, 2016, Oldenburg, Germany .

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use. 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 drivers 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-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 degree of control on driver attentiveness, GM’s 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]. The standardization of such approaches is currently under consideration in Europe [9] and is supported by research [23].

Multiple factors contribute to a driver’s expectations of system 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 driver’s prior experience with similar technologies, predisposed trusting tendencies, and attitudes formed from exposure to media and societal opinion might all contribute to a driver’s 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 perceptions 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 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 system, 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 of

current 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 driver assistance systems affect a customer’s perception of the level of automation of that system?

2. 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?

METHOD

Participants Participants 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 20-69, 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 completed a Bachelor’s degree, 2% had an Associate’s 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 Instrument

Systems Addressed 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 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 manufacturer 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.

Session 7 - AV-Driver Interaction Paradigms: What is the Role of the Human? AutomotiveUI ’17, Oldenburg, Germany

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Automation Categories Seven descriptions of differing levels of automation were created for participants to classify systems (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 function 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 attention was paid to the distinction between general tasks the driver would be responsible for, versus general tasks the driving assistance system would be

responsible 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 classifications for individual ODDs and DDTs, in an attempt to avoid overwhelming the survey respondents (Figure 1).

Survey Design Methodology After selecting systems for inclusion and developing a first draft of automation categories, a survey instrument was developed by the research team. This instrument went through 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 questions. Pilot subject feedback was integrated into the final instrument detailed within this report. Survey Procedure Participants 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 classifications, 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 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 level 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, participants 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 their vehicles had any of the survey systems installed.

System Manufacturer Availability LoA Active Cruise Control BMW Available 1 AutoCruise N/A N/A N/A Autopilot Tesla Available 2 Distronic Plus Mercedes-Benz Available 1 Drive Pilot Mercedes-Benz Available 1.5 Driving Assistant Plus BMW Available 1.5 Enhanced Autopilot Tesla In Development 3 Eyesight Subaru Available 1 Highway Pilot Audi In Development 3 Intelligent Assist N/A N/A N/A Intelligent Cruise Control Nissan Available 1 Intelligent Drive Mercedes-Benz Available 1 Intelligent Pilot N/A N/A N/A Pilot Assist Volvo Available 1.5 Pilot Plus N/A N/A N/A ProPilot Nissan In Development 1.5 Super Cruise GM In Development 2 Traffic Jam Assist Audi Available 1.5 Traffic Jam Pilot Audi In Development 3

Table 1. Systems, availability, and level of automation (LoA) at time of survey deployment.

Figure 1. After a survey introduction, participants were presented with this graphic representing seven categories of automation, ranging from SAE Level 0 (fully manual, far left box) to SAE Level 5 (fully automated, far right box). These seven categories

provide layman’s definitions of the division of driving task responsibility between driver and system.


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The survey ended by collecting demographic information, including date of birth, highest level of education, employment status, household income, gender, and zip code.

Participants who completed the survey were offered the opportunity to enter a raffle to win one of 10 $50 Amazon gift 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 February 22nd – March 6th 2017.

RESULTS Data 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 15 deployed or in-development systems (.05 / 15). For age

analyses, respondents were grouped into five age ranges: 20-29, 30-39, 40-49, 50-59, and 60-69.

Familiarity & Correctness Most 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 familiarity than the other systems in the sample. Importantly, it is unclear whether or not respondents were familiar with Tesla’s Autopilot, the term “autopilot” within the context of aviation, or the colloquial “autopilot,” used when referring to completing a task absentmindedly or without focus. While 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.

Table 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.

Figure 2. Participants rated themselves as being not at all familiar with most of the systems prior to taking the survey.

0%10%20%30%40%50%60%70%80%90%

100%

Active Cruise

Control

Autopilot Distronic Plus

Drive Pilot Driving Assistant

Plus

Enhanced Autopilot

Eyesight Highway Pilot

Intelligent Cruise

Control

Intelligent Drive

Pilot Assist ProPilot SupercruiseTraffic Jam Assist

Traffic Jam Pilot

Not at all familiar Slightly familiar Moderately familiar Very familiar Extremely familiar


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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% correct), overall accuracy remained low (Table 2). Active Cruise Control had the highest proportion of correct categorizations, with 50% of respondents correctly categorizing it as a Level 1 system. Intelligent Drive, also a Level 1 system, had the lowest proportion of correct categorizations (9%).

Confidence ratings varied across systems. On a scale of 1 (low confidence) to 5 (high confidence), over half of participants rated their confidence as a 4 or 5 for Active Cruise Control, AutoCruise, and Autopilot. Distronic Plus had the highest proportion of low confidence ratings, with 66% of participants rating their confidence at a 1 or 2.

The Mann-Whitney U test was applied to investigate differences in confidence of system categorization between respondents who correctly versus incorrectly classified a system. Most systems showed no significant difference in confidence rating between individuals that correctly categorized a system compared to those that incorrectly categorized the system (Table 2).

One system (EyeSight) showed significant age differences in correct categorization; no other system showed significant differences between different age groups and correct categorization (Table 3). There were no significant gender differences in correct categorization of any system; men were not more frequently correct than women and vice versa. However, there were significant differences in confidence and familiarity ratings between genders (Table

3). In all significant cases, men rated themselves more confident than women in their responses. Men also reported being more familiar with systems prior to taking the survey.

Categorization Patterns Chi-square goodness of fit tests were used to explore whether or not the distribution of level categorization differed from random guessing; that is, equal distribution of responses across the seven levels of automation. Chi-squared residuals were explored to determine which cells contributed most toward the results [29].

Every system showed a significant difference from equal distribution of categorizations (Table 2). When exploring the raw residuals, one system (EyeSight) showed residuals close to random guessing. The remaining 18 systems showed that a disproportionate number of participants were not selecting L0 (no automation) for most systems. Aside from EyeSight, every system had an L0 residual of less than -25, and ten had residuals of less than -50. As the survey instructions indicated each system was an automated system, it seems plausible that respondents were not selecting L0 due to the survey design, rather than any impact of naming. As such, chi-square analysis was re-run using only the data points assigned to L1-5. Each system still showed a significant difference from equal distribution of categorizations (Table 4).

The residuals for the second set of chi-square analyses revealed two strong relationships between name and categorization within the 19 systems (Table 4). Table 4 was color-coded to more easily display patterns in the residuals. Dark green cells are those with the highest residuals, dark grey are those with the lowest, and white are those with

Table 3 No significant differences were exhibited in gender and accuracy, but significant gender differences were exhibited in confidence and familiarity with systems.


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residuals closest to zero. The first relationship was between the systems with “cruise” in the name; these four systems were consistently rated at the lower end of the automation scale. They also generally received higher confidence ratings. The second relationship was regarding the four “assist” systems, which received high frequency of categorizations in L1.5 and L3. Confidence ratings were consistently in the middle for these systems. Four systems (ProPilot, Highway Pilot, Distronic Plus, and EyeSight) showed residuals that were widely distributed across the automation scale; these systems were still significantly different from random guessing, but had the lowest range of residuals. They also showed the lowest confidence ratings of all systems. The remaining 8 systems showed responses different from guessing, but no easily identified pattern was apparent between any of the 8 systems and their names.

An alternate visualization of the results appears in Figure 3. Here, colored squares represent mean response, while black lines indicate the bounds of the first and third quartiles. While the mean categorization for most systems is higher than the correct categorization, it is important to note that mean has limited value for interpretation for two reasons: first, the categories provided are ordinal with dissimilar differences between each category. Second, as some participants were likely guessing, there were more opportunities to select levels of automation above the correct category than below. The quartiles bounds indicate some systems, such as Active Cruise Control, Intelligent Cruise Control, and AutoCruise, have short distributions

ranging between L1 and L2. Others, such as Highway Pilot, Traffic Jam Pilot, and Intelligent Assist, have wider distributions spanning from L1.5 to L4.

DISCUSSION The first research question was, does the name of driver assistance systems affect a customer’s perception of the level of automation of that system. The survey results indicate that the name of a system does have an impact on the degree of responsibility that respondents expected to have as a driver when using a partially automated system. Overall reported familiarity with the systems was low and participants were instructed not to use outside resources when categorizing systems. Consequently, the primary information contributing to significantly different categorizations was likely centered on the name of the system. Initial exploration into age effects suggests these results are pervasive across all ages, though small sample sizes for respondents in their 40s may limit interpretation of these results.

Active Cruise Control was the only system that a majority of respondents categorized correctly, and it received the highest confidence and familiarity ratings. Tesla’s Autopilot also received comparatively high familiarity ratings, but accuracy was in line with other systems. Low accuracy could be due to Autopilot’s name, but as prior familiarity with the system was notable, little can be said about the effects of solely the term “Autopilot” on determination of system capability. Rather, the differing results of these two higher familiarity systems suggest

Different from

Guessing, no L0 Residuals Confidence**

System X2 df p L1 L1.5 L2 L3 L4 L5 Median Mode "Cruise" systems: lower levels

Active Cruise Control 473.9 5 <0.001 159 5 -41 -20 -53 -50 4 5 AutoCruise 175.3 5 <0.001 65 60 -9 -18 -51 -45 4 4 Intelligent Cruise Control 149.9 5 <0.001 63 52 -6 -30 -42 -39 3 4 Super Cruise 68.2 5 <0.001 -22 40 38 -4 -17 -34 3 1

"Assist" systems: split between 1.5 & 3

Driving Assistant Plus 139.6 5 <0.001 -45 38 13 61 --18 -51 3 3 Intelligent Assist 38.4 5 <0.001 -23 35 -9 23 -4 -20 3 3 Pilot Assist 156.5 5 <0.001 -35 66 17 39 -32 -56 3 3

Traffic Jam Assist 70.0 5 <0.001 1 39 -14 34 -17 -43 3 3

Closest to random guessing

ProPilot 59.5 5 <0.001 -50 -22 20 20 20 13 3 1 Highway Pilot 67.6 5 <0.001 -42 10 24 33 10 -36 3 3 Traffic Jam Pilot 32.4 5 <0.001 -26 15 10 27 -1 -24 3 3 Distronic Plus 48.5 5 <0.001 -24 15 30 24 -24 -23 1 1 EyeSight* 36.8 5 <0.001 -11 23 -6 32 -20 -16 3 1

Drive Pilot 89.7 5 <0.001 -46 10 39 38 -3 -36 3 1

Pilot Plus 113.6 5 <0.001 -51 2 46 23 30 -49 3 1

Enhanced Autopilot 97.6 5 <0.001 -59 -28 -5 42 30 20 3 3

Intelligent Pilot 100.7 5 <0.001 -54 -2 41 46 -5 -25 3 3

Intelligent Drive 76.6 5 <0.001 -31 48 31 5 -25 -27 3 3

Autopilot 58.6 5 <0.001 -47 -2 43 14 -2 -5 3 4 *Eyesight also showed a large proportion of responses on L0 **Confidence was rated on a 5-pt scale, with 1 being low confidence & 5 being high confidence

Table 4. Raw residuals of Chi-Square Goodness of Fit Tests for equal distribution of responses across Levels 1 – 5 showed three patterns in name and level categorization.


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outside factors (e.g., hands-on experience, media reports, educational material) likely have a greater impact than name alone on setting expectations for a system.

Setting expectations from the beginning is important, and first impressions have a long been known to influence use [19]. Nevertheless, misconceptions in perceptions of a system can be overridden as a consumer receives more information and first-hand experience with a system. Moving forward, manufacturers or other parties will need to continue investing in appropriate ways of educating drivers on responsible use of their partially automated driving system. As one example of a more integrated education approach, Subaru has developed asales and delivery system for the EyeSight system that aims to help enhance consumer understanding throughout the purchase process [1]. While Subaru’s developments in this area may be industry leading, other manufactures have the opportunity to leverage observations surrounding consumers’ use of current systems [28] to inform and refine models for use during the sales process or real-time coaching.

The second research question was, 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? Survey results indicated that terms, to varying degrees, influence consumers’ perceptions of automation level. For example, Cruise Control is an established in-vehicle technology with which many drivers are familiar. Unsurprisingly, “cruise” systems were frequently rated at the lower end of the automation scale. It appears drivers interpreted “cruise” systems to be slightly more automated than cruise control, setting an expectation that while a “cruise” system might be able to handle part of the driving task, ultimate responsibility remained on the driver. Though name may not be the most important factor for setting consumer

expectations, manufacturers could benefit from leveraging understood terminology from established and high-familiarity systems to properly orient consumer understanding of their responsibilities while driving and using a system.

Care should be taken when using potentially ambiguous terms to name systems. “Assist” systems, which attempt to indicate that the driver should not be relying on the vehicle to complete all of the driving tasks, were confused between two non-adjacent level classifications. Participants frequently either rated the system as L1.5, which involves providing speed and steering support while requiring the driver to keep their hands on the wheel, or L3, which involves the system handling most tasks and the driver being prepared to take over if requested. In one (L1.5), the system is assisting the driver, who holds responsibility for OEDR. In the other (L3), the driver is expected to be ready to assist the system, which is responsible for the dynamic driving tasks. These two levels require very different input from the driver, and avoiding confusion regarding his/herrole is crucial.

Overall, the results suggest that brand names do influence perceptions of technologies; yet, brand names do not offer enough information to appropriately set driver expectations for their role while driving. The wide distribution of responses for some systems, notably Highway Pilot, Traffic Jam Pilot, and Intelligent Assist, indicate that name of a system may be interpreted numerous different ways by individual consumers. As many consumers own more than one vehicle, a greater degree of commonality of design and naming characteristics (e.g. ABS, ESC, etc.) in combination with increased driver education may be critical in the successful transformation of personal mobility from largely manual control through to higher levels of automation. As the aviation literature has long stipulated, with increasing

Figure 3. Simplified visualization of level classification distributions. Colored boxes indicate mean ranking, and black lines represent bounds of the first and third quartiles.


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levels of automation, increased education is required to ensure operators are fully aware of their role [26]. It is unclear how current automotive and technology developers building automated driving systems across the levels are fully embracing human-centered design principles from conceptualization to technology naming, marketing, delivery, and eventual use. It is clear that naming conventions could be used to help amplify system intuitiveness (e.g., where the driver’s and system’s mental models by nature have a high degree of overlap), and to better facilitate adoption of automated driving features that have the potential to revolutionize mobility and increase vehicle safety.

Consistent with previous gender research, men in this survey were more confident in their categorizations when they were in fact incorrect than were women who were incorrect [16]. This overconfidence, combined with a general male preference to seek out information on their own rather than to be provided with assistance from dealership staff or the car itself [3], could create additional challenges for male consumers in setting appropriate driver mental models at first exposure to a system. As these results suggest, name alone is not enough to appropriately orient drivers to system limitations and appropriate use.One solution might be to necessitate a self-guided tutorial or other training sessions run by the system itself, in which the system could be locked until the driver completes the tutorial.

As research on this topic expands, it is recommended that a naming guide be created to provide insight for manufacturers when designing and marketing new systems. To that end, future research on this topic would benefit from a larger, more nationally representative sample. This survey was limited in the age ranges represented in the sample, as well as the geographic distribution and education level of respondents. Future research may also need to limit the number of systems addressed. Due to the similarity and overlapping nature of many of the technology names (e.g., Traffic Jam Pilot, Traffic Jam Assist, and Pilot Assist), it was difficult to interpret which term was affecting classification to the higher degree. A more targeted approach could provide deeper insight toward the specific portions of each name and how they relate to automation and driver role expectations.

ACKNOWLEDGMENTS Support for this work was provided by the Advanced Vehicle Technology (AVT) consortium at MIT. The views and conclusions being expressed are those of the authors, and have not been sponsored, approved, or endorsed by members of the consortium.

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