Use Challenges and Training Needs of In-Vehicle Technologies for Older Drivers With Vision Impairments

Abstract

Importance: Older drivers with impaired vision may face challenges using in-vehicle technologies, affecting user experience.

Objective: To explore use challenges with in-vehicle technologies and training preferences of older drivers with and without central vision loss (CVL).

Design: A telephone questionnaire on perceptions of driver–vehicle interface use difficulties, challenges with existing advanced driver assistance systems (ADAS), and preferences for ADAS acquisition and training.

Setting: Community-dwelling U.S. older adults.

Participants: A total of 126 volunteers 18 yr and older with valid driver’s licenses who had driven within the last 2 mo were recruited. The CVL group self-reported diagnoses of various forms of CVL.

Results: Fifty-eight older drivers with CVL (Mdn age = 71.4 yr, 41% male) and 68 without (Mdn age = 71.8 yr, 37% male) completed the questionnaire. The CVL group reported greater difficulty using in-vehicle technologies compared with the non-CVL group, particularly in recognizing visual information in vehicle interfaces. Greater difficulty correlated with poorer self-reported vision status and with increased self-regulated driving exposure. Primary challenges for the CVL group included issues with small button and font sizes, inadequate color contrast, and display glare. Both groups expressed a desire for guidance and training in effective ADAS use, with the CVL group preferring guidance from health care professionals.

Conclusions and Relevance: Older drivers with CVL face challenges using in-vehicle technologies, especially in accessing visual information. They prefer guidance from health care professionals, suggesting potential for tailored support from occupational therapists specializing in driving rehabilitation.

Plain-Language Summary: This study explored the challenges faced by older drivers with central vision loss when using in-vehicle technologies. Older drivers with central vision loss reported significantly more difficulty than drivers without central vision loss, particularly in recognizing and using visual information in vehicle interfaces and with advanced driver assistance systems. Challenges included issues with small buttons and font sizes, poor color contrast, and display glare. Individuals with more severe vision impairment, who drive less and avoid more driving situations, face more challenges with in-vehicle technologies, yet these are the drivers who might benefit most from such technologies. Both groups expressed a desire for guidance in selecting and using driver assistance technologies, with drivers with central vision loss preferring support from health care professionals. The study findings highlight the need for tailored training and support, which could enhance occupational therapy clients’ ability to effectively use modern vehicle technologies to drive safely.

The study findings highlight the need for tailored training and support for older drivers with central vision loss when using in-vehicle technologies to drive safely.

Age-related functional declines may pose risks associated with operating motor vehicles among older adults. Current cars are increasingly equipped with advanced in-vehicle technologies, such as advanced driver assistance systems (ADAS) and in-vehicle information systems (IVIS), aimed at enhancing driving safety and experience. ADAS are electronic systems in vehicles that use advanced technologies to assist the driver in performing driving tasks, improve safety, and enhance the driving experience. ADAS typically include features such as cruise control, blind spot warning, and lane departure warning. IVIS are electronic systems in vehicles that provide information, entertainment, and vehicle control functions, helping drivers manage navigation, communication, and vehicle diagnostics features through touchscreens, voice commands, and infotainment systems. Drivers use and communicate with both ADAS and IVIS through driver–vehicle interfaces, which represent all physical controls such as buttons, knobs, and steering wheel– mounted controls, as well as digital interfaces like touchscreen displays and voice controls, among others. Although the adoption of in-vehicle technologies offers great potential to enhance safety for older drivers (Bengler et al., 2014; Davidse, 2006; Eby et al., 2015, 2018; Liang et al., 2020), age-related declines in sensory, cognitive, and physical functioning can affect how they use them—even among those without vision impairments. For example, typical age-related reductions in vision may make it difficult to read interfaces with low brightness or poor color differentiation (Meyer, 2004). Cognitive challenges, such as slower processing speed and difficulty learning novel systems, could further compound usability issues. Liang et al. (2020) found that older drivers often found interface designs unintuitive, such as locating ADAS controls or recalling operational sequences for specific functions. Physical limitations, such as reduced manual dexterity, can also make it harder to interact with touch screens or haptic controls. These barriers exist independently of pathological vision loss; however, most current design guidelines for in-vehicle technologies do not adequately account for age-related declines among older drivers (Young et al., 2017).

Central vision loss (CVL), often caused by age- related macular degeneration, affects vision in various ways, including reduced visual acuity, reduced contrast sensitivity, sensitivity to glare, impaired dark adaptation, and reduced night vision (Massof et al., 2007; Owsley & McGwin, 2010; Wood, 2002). Although some individuals with CVL may still legally drive in many U.S. states, they frequently report difficulties while driving because of visual challenges (Bowers, 2016; DeCarlo et al., 2003; Sengupta et al., 2014; Xu, Hutton, et al., 2023). Prior studies have shown that CVL can lead to delayed hazard detection and responses (Bronstad et al., 2013, 2015; Xu et al., 2025). Emerging evidence from recent survey studies suggests that drivers with vision impairments perceive in- vehicle technologies as beneficial for mitigating driving challenges (Cucuras et al., 2017; Deffler et al., 2022; Stevens et al., 2022; Wallace-Carrete et al., 2024; Xu, Hutton, et al., 2023; Xu et al., 2022). For instance, Xu, Hutton, et al. (2023) identified specific driving tasks that older drivers with CVL found particularly challenging, including detecting road users in vehicle blind spots, managing glare-induced vision disruptions, responding to unexpected pedestrians, and navigating unfamiliar areas. These self-reported difficulties align with the drivers’ expressed preferences for ADAS features that prioritize collision prevention, such as blind spot warnings, pedestrian detection alerts, and forward collision avoidance systems. Although these findings are preliminary and based on self-reported data rather than on-road performance evidence, they provide valuable insights into ADAS features that could better support drivers with CVL.

Despite these perceived benefits, older drivers with CVL also reported experiencing challenges using these technologies because of their vision impairment. For example, although GPS can compensate for their vision loss and increase driving comfort and safety (Cucuras et al., 2017), many CVL drivers struggled with using visual information displayed on GPS screens (Stevens et al., 2022). In a case study, a driver with 20/180 vision (holding a bioptic driving license, which is only permitted in certain states) struggled to use a 15-inch touchscreen in a Tesla, raising safety concerns because of missed or delayed warning information (Xu, Kendrick, & Bowers, 2022, 2023). When the safety performance of using different hazard warning types in ADAS in a driving simulator was compared, most drivers with CVL reported a tendency to not use visual warning information when warnings with other modalities (auditory or tactile) were available (Xu & Bowers, 2024). From the research, it is evident that vision impairments can significantly affect the use of in-vehicle technologies and may compromise the safety benefits of these technologies.

Unfortunately, despite many challenges faced by older drivers in using in-vehicle technologies, many did not receive proper training on how to use them (Eichelberger & McCartt, 2014). This could be especially problematic for drivers with vision impairments, who may struggle with navigating complex user manuals and must rely on experimentation while driving. Occupational therapy practitioners specializing in driving rehabilitation, such as driver rehabilitation specialists, often serve as a prime resource for assessment and intervention when vision imposes significant limitations on driving. They work closely with drivers to recommend suitable aftermarket vehicle adaptations tailored to their specific functional declines (National Highway Traffic Safety Administration, 2007). However, to our knowledge, there is currently no established practice for providing recommendations or knowledge on the utilization of various existing advanced driver assistance technologies to drivers with vision impairments. We only found recent discussions about the potential evolving role of occupational therapists and driver rehabilitation specialists in educating and training individuals with disabilities to optimize the use of future personal and public autonomous vehicles (Classen et al., 2024; Liu, 2018; Schultz-Krohn et al., 2019).

From our experience working with research participants with vision impairments, they often express challenges in using new car technologies because of vision limitations but are at a loss regarding where to seek information. To address these unmet needs, we conducted a telephone questionnaire study. The first goal was to gain a better understanding of the specific difficulties experienced by older drivers with and without CVL when using in-vehicle technologies, including driver–vehicle interfaces and ADAS. We hypothesized that drivers with more severe vision impairments would report greater visual difficulty when interacting with visual information in the in-vehicle technologies. The second goal was to investigate the training received on using in-vehicle technologies by older drivers with and without CVL, understand their training needs and preferences, and explore the potential interests of individuals with impaired vision in seeking assistance from occupational therapy interventions.

Method

Study Design and Setting

We conducted a cross-sectional, observational telephone questionnaire study to compare self-reported experiences with in-vehicle technologies between community-dwelling older adults with CVL and a control group without vision loss (non-CVL). Participants were recruited from U.S. community-based settings, including low-vision rehabilitation clinics, low-vision support groups, senior centers, a laboratory database, and online platforms. Data collection occurred between January 2022 and February 2023.

Participants

We recruited 58 volunteers with CVL and 68 age- and gender-similar volunteers without CVL (non-CVL). This study used the same cohort of participants as Xu, Hutton, et al. (2023). The inclusion criteria for this study required CVL participants to have a diagnosed form of central vision impairment, whereas non-CVL participants must not have any form of central vision impairment. All participants must be 18 yr or older, possess a valid driver’s license, and have driven within the last 2 mo. Participants who had difficulty understanding survey questions or could not complete the entire survey were excluded. The study conformed to the Declaration of Helsinki and was approved by the Wichita State University Institutional Review Board.

Telephone Questionnaire and Procedures

The questionnaire was administered via telephone and lasted 40 min to 1 hr, and the responses were recorded in Qualtrics. All questionnaires were administered by Abbie Hutton and Jing Xu, who were trained through pilot tests to standardize survey administration and data entry. The questionnaire included (1) demographics and driving patterns, (2) perceived difficulty in 11 tasks when using driver–vehicle interfaces (Table 1), (3) use and perceived difficulty when using 12 existing ADAS technologies (forward collision warning, forward collision avoidance, blind spot warning, lane departure warning, rearview camera, cruise control, adaptive cruise control, GPS, pedestrian warning system, intersection assistant system, night vision system, and adaptive headlights; descriptions of each ADAS feature are included in Table A.1 in the Supplemental Material), and (4) preferences for ADAS acquisition and training. Driving patterns include weekly driving mileage, weekly driving days, and avoidance of 11 common situations (e.g., night driving, heavy traffic; Ball et al., 1998), with participants indicating avoidance (yes/no) for each. The 11 common tasks associated with using the vehicle interface were primarily derived from previous studies on human–machine interface challenges for older drivers (Baldwin, 2002; Classen et al., 2019; Young et al., 2017), as well as insights into the accessibility difficulties experienced by drivers with CVL (Xu et al., 2022, 2023).

Table 1.

Eleven Common Tasks When Using Driver–Vehicle Interfaces

No.	Task^a
1	Reading text or symbols on the instrument panel (dashboard or central console) displays
2	Discriminating between similar colors when reading dashboard or central console information
3	Seeing the markings on a control or button while using it
4	Reaching and adjusting controls (e.g., changing radio, adjusting air conditioning)
5	Recognizing visual messages and warnings inside the car
6	Recognizing visual messages and warnings from other devices used in your car (e.g., GPS)
7	Figuring out the right button or menu to use to change settings
8	Programming a navigation system
9	Programming music or tuning your radio
10	Recognizing and locating sound or audio signals inside the car
11	Programming or changing settings on the touch screen

^aRating scale: 1 = not difficult at all, 2 = slightly difficult, 3 = moderately difficult, 4 = very difficult, 5 = extremely difficult, 0 = not applicable.

The rating questions regarding perceived difficulty with 11 tasks when using a driver–vehicle interface were formatted on a 5-point Likert scale. Participants were instructed to rate the difficulty of each task both in daytime conditions and under reduced visibility conditions (e.g., night, bad weather). Prior to the questionnaire, participants received a training document designed with large fonts and high-contrast colors for enhanced readability. The document detailed the names, functional descriptions, and visual icon images of the 12 existing ADAS technologies. This information and descriptions were drawn from the AAA “Vehicle Owners’ Experiences With and Reactions to Advanced Driver Assistance Systems” project report (McDonald et al., 2018) and the MyCarDoesWhat.org website (https://mycardoeswhat.org), with links included in the document for further reference. Participants were instructed to review the document thoroughly beforehand and keep it accessible during the questionnaire administration. Before asking the specific question about each ADAS, the examiner will read out its description, ask if the participant has any questions, and answer those questions to ensure the participant understands the core features of that ADAS.

Data Analyses

All data were self-reported, and data analyses were conducted by using R Studio, with p < .05 used to define statistical significance. We conducted descriptive analysis to examine participant demographics, vision status, driving patterns, and vehicle information. To analyze continuous variables (e.g., driver–vehicle interface use difficulty ratings), we used t tests or Wilcoxon tests; to examine categorical variables (e.g., self- reported vision status), we used χ² tests to evaluate differences among vision groups (CVL vs. non-CVL) or visibility conditions (good visibility vs. reduced visibility). We used Spearman correlation and Kruskal– Wallis tests to assess relationships among participant characteristics, driving patterns, vehicle interface use difficulty ratings, and the number of ADAS used. Short responses from open-ended questions (e.g., specific challenges encountered when using driver–vehicle interface) were analyzed and summarized into common themes, with the most frequently mentioned themes reported in the Results. To ensure accuracy and minimize bias, Xu and Hutton reviewed, cross-checked, and agreed on all identified themes.

Results

A total of 126 volunteers were included in the analyses: 58 drivers with CVL (mean age = 71.4 yr, SD = 12.6; 41% male) and 68 without CVL (mean age = 71.8 yr, SD = 7.7; 37% male). The self-reported characteristics of both groups included in the final analysis are presented in Table 2. The t tests and χ² tests confirmed no significant differences in age or gender between the two groups. We found that 91% of CVL participants reported having some conditions that made driving difficult (vs. 22% of non-CVL participants), with vision-related issues being the most common (85%). The primary causes of CVL were age-related macular degeneration (85%) and juvenile macular degeneration (14%). Participants with CVL, compared with those without CVL, respectively, reported driving significantly fewer miles per week (Mdn = 50, interquartile range [IQR] = 12.5–97.5 vs. Mdn = 60, IQR = 37.5–100; p = .02), driving less days per week (Mdn = 4, IQR = 2.3–5.8 vs. Mdn = 6, IQR = 5–7; p < .001), and avoiding significantly more driving situations (range = 1–11; Mdn = 4, IQR = 2–6 vs. Mdn = 1, IQR = 0–3; p < .001). The most common situations that CVL participants avoided were night driving (78%), peak-hour traffic (55%), long-distance driving (53%), and bad weather (53%).

Table 2.

Self-Reported Demographic, CVL Diagnoses, Driving Exposure, and Owned Car Characteristics

Characteristic	n (%)		p
Characteristic	CVL Group (n = 58)	Non-CVL Group (n = 68)	p
Age, year, M (SD)	71.4 (12.6)	71.8 (7.7)	.23
Years since CVL diagnosis, median (IQR)	8 (4–12.3)
Driving mileage per week, median (IQR)	50 (12.5–97.5)	60 (37.5–100)	.02
Number of days driven per week, median (IQR)	4 (2.3–5.8)	6 (5–7)	<.001
Number of driving situation avoided, median (IQR) a	4 (2–6)	1 (0–3)	<.001
Age of vehicle, year, median (IQR)	7 (3–11.8)	8 (5–11)	.43
Total number of ADAS used, median (IQR)	3 (2–5.75)	3 (2–3)	.46
Male	24 (41.4)	25 (36.8)	.73
Self-reported CVL causes
Age-related macular degeneration	49 (84.5)
Juvenile macular degeneration	9 (15.5)
Vehicle ownership duration			.66
0–6 mo	2 (3.4)	2 (2.9)
7–12 mo	1 (1.7)	0 (0)
>1 yr–5 yr	33 (56.9)	33 (48.5)
6–10 yr	14 (24.1)	19 (27.9)
>10 yr	8 (13.8)	14 (20.6)

Note. Values in bold indicate significance at p < .05. CVL = central vision loss; IQR = interquartile range, reported as 25th to 75th percentile; ADAS = advanced driver assistance systems.

^aThe total number of driving situations in this survey was 11.

Perceived Difficulties When Using Driver–Vehicle Interfaces

CVL drivers perceived greater difficulties compared with non-CVL drivers, respectively, in 11 tasks when using in-vehicle information interfaces (Mdn = 3.46, IQR = 1.95–4.22 vs. Mdn = 2.07, IQR = 1.43–2.96; p < .001). The CVL group perceived greater difficulties in reduced visibility condition compared with good visibility conditions (Mdn = 3.85, IQR = 2.15–5 vs. Mdn = 2.22, IQR = 1.42–3.38; p = .01; Figure 1). In comparison, the perceived difficulty did not differ between reduced and good visibility conditions for the non-CVL group (Mdn = 2.33, IQR = 1.65–3.31 vs. Mdn = 1.66, IQR = 1.00–2.69; p = .06). Both groups reported the same tasks as the most difficult in reduced visibility conditions as they experienced in daytime conditions. Specifically, they found it difficult to identify the correct button or menu on the dashboard when adjusting settings and had difficulty seeing the markings on buttons, particularly in daytime conditions. CVL drivers found reading text or symbols on the instrument panel and programing/changing settings on the touch screen to be difficult, whereas non-CVL drivers found recognizing and programming information in GPS to be difficult.

Figure 1.

Perceived vehicle interface use difficulties for central vision loss (CVL) and non-CVL groups in good and reduced visibility conditions.

When participants were asked to elaborate on the specific challenges they encountered (through open-ended questions), the most frequently reported difficulties among CVL participants included issues related to the small font size on visual displays and buttons (41%), display glare (21%), and insufficient color contrast leading to difficulties in distinguishing between colors (13%), such as black and gray appearing similar. Other difficulties mentioned included dim dashboard lighting (which was particularly problematic during bright daytime but was easier at night with background illumination), excessively small touch screen information, and a user menu that was difficult to read. Because of visibility challenges, several participants mentioned that they tried not to modify any settings in their vehicles, used magnifiers or magnification apps on their phones, or made adjustments by feel or memory. Only a small number of CVL drivers reported no problems with the car interface, attributed to their familiarity with the location of settings because of long-term usage. Among non-CVL drivers who reported difficulties, 43% also noted challenges with the font size and button size of visual information, and 23% reported difficulty understanding dashboard information. In terms of warning indicators or symbols on the dashboard specifically, 20% of CVL drivers reported they were hard to recognize, compared with 2% of non-CVL drivers (p = .01). About 24% of participants in both groups reported being distracted when using in-vehicle technologies.

Correlation Between Vision Status, Driving Patterns, and Perceived Difficulties in Driver–Vehicle Interface Use

For all participants, those who reported poorer vision perceived greater difficulties using the driver–vehicle interface under both good and reduced visibility conditions (p = .02 and p < .001, respectively). These associations were particularly pronounced for the CVL group, in which significance was observed for both good and reduced visibility conditions (p = .05 and p = .003, respectively). However, similar patterns were not observed for the non-CVL group (p = .68 and p = .98, respectively). Moreover, higher levels of perceived difficulty in vehicle interface use were correlated with greater driving avoidance (r = .54, p < .001) for both groups. Among CVL participants, such difficulties were associated with lower weekly mileage (r = –.46, p < .001) and fewer days driven (r = –.37, p = .004), whereas no significant correlations were found among non-CVL participants. Age, vehicle age, and the number of ADAS owned were not associated with perceived use difficulties for either group (r = .06, p = .44; r = .001, p = .98; and r = –.07, p = .45, respectively). The perceived vehicle interface use difficulties did not differ by gender or vehicle ownership duration for either group.

Perceived Difficulties When Using ADAS

Both the vehicle age and ownership duration did not differ between two groups (p = .43 and p = .66, respectively; Table 2). The median age of vehicle was 7 yr and 8 yr for the CVL and non-CVL group, respectively. Only approximately 3% of participants owned their vehicle less than 6 mo, and 56.9% of CVL and 48.5% of non-CVL participants owned their vehicles for 5 yr or less. Participants with CVL reported using a median of 3 of the surveyed ADAS systems (vs. 3 of non-CVL participants; p = .46). The ADAS most commonly used by both groups were GPS (74.1% CVL vs. 80.9% non-CVL drivers), rearview camera (63.8% vs. 58.8%) and cruise control (55.2% vs. 86.8%; Table 3). According to participant feedback, 94% of the ADAS they owned and used featured both visual and auditory cues. CVL drivers who used ADAS reported encountering more difficulties with GPS and rearview camera (over 10%; Table 3), and 25% of non-CVL drivers reported difficulties in setting up cruise control and GPS.

Table 3.

Percentage of Self-Reported ADAS Use, Use Challenges Encountered Because of Vision Loss, and Training Received for ADAS Use in Participants’ Vehicles

ADAS	CVL Group		Non-CVL Group, % Use	% Both Groups Received Training
ADAS	% Use	% Experienced Challenges Because of Vision Loss	Non-CVL Group, % Use	% Both Groups Received Training
GPS	74.1	12.8	80.9	11.2
Rearview camera	63.8	10.5	58.8	20.8
Cruise control	55.2	4	86.8	7.6
Blind spot warning	43.1	4	25	23.8
Lane departure warning	29.3	5.3	16.2	22.6
Forward collision warning	25.9	6.7	11.8	28.6
Forward collision avoidance	19	8.3	4.4	33.3
Adaptive cruise control	15.5	7.1	4.4	30.4
Pedestrian warning system	1.7	0	0	0
Night vision system	0	0	0	0
Intersection assistant system	1.7	0	0	0
Adaptive headlights	8.6	0	4.4	0

Note. ADAS = advanced driver assistance systems; CVL = central vision loss.

About 27% of participants reported using voice control, with the primary uses being for phone calls (94%), GPS (21%), and other car controls (14.7%; e.g., radio, air conditioning). In addition, 67% of CVL drivers found voice control very useful (vs. 78% of non-CVL drivers), and 50% of CVL drivers felt it compensated for their vision impairment.

Preferences for ADAS Acquisition and Training

Among the participants who owned and used ADAS, 22% reported having received some form of training on ADAS use. Of all participants, 72% expressed interest in obtaining a vehicle with ADAS, and 91% expressed interest in seeking professional advice on purchasing ADAS. Roughly 76% of CVL participants reported that they would want guidance in figuring out which technology would best compensate for their vision loss, and 56% would want to be able to individually select which ADAS to install.

A high percentage (96%) of all participants stated that they would seek professional advice on how to use ADAS. The primary sources of guidance were the dealership (62%) and self-exploratory (39%; e.g., YouTube, car manual). In addition, 86% of all participants expressed interest in taking a training course to learn how to use ADAS (92% CVL drivers vs. 80% non-CVL drivers). Interestingly, there was a significant difference between the preferred format of training (p = .004) and training frequency (p = .03) between the two groups. Specifically, 36% of CVL drivers preferred to receive hands-on training by health care professionals, such as having a health care specialist drive with them and show them how to use ADAS (vs. 6% of non-CVL drivers), and 28% of CVL drivers wanted to be trained one-on-one by car professionals (vs. 58% of non-CVL drivers). Finally, 61% of CVL drivers preferred receiving training multiple times per year (vs. 28% of non-CVL drivers) rather than just a one-time training.

Discussion

This study found that the CVL group reported significantly more difficulty when using the driver–vehicle interfaces under both good (daytime) and reduced visibility conditions (e.g., night, bad weather). The findings support the hypothesis that drivers with more severe vision impairments experience greater difficulty when using driver–vehicle interfaces, particularly within the CVL group. This increased perception of difficulties in using the interfaces correlated with increased driving avoidance and reduced driving frequency. Among common tasks when using vehicle interfaces, the two most challenging ones identified by both groups were figuring out the correct button or menu to change car settings and seeing car interface markings. For drivers with CVL, the additional challenges that received high ratings included difficulty in reading text or symbols and using the touch screen (information too small).

In exploring the specific difficulties encountered by participants using car interfaces, we found that small font size on visual displays, small buttons, display glare on screens, and insufficient color contrast leading to poor distinction were commonly reported by the CVL group. On the basis of these findings, it is evident that all difficulties reported by drivers with CVL were predominantly related to impaired central vision, attributable to decreased visual acuity, contrast sensitivity, sensitivity to glare, and insufficient color contrast. Older drivers without CVL also had visual difficulties in perceiving information, potentially because of their age-related declines in vision (Baldwin, 2002; Cooper et al., 2020; Davidse, 2006), as well as difficulties related to other aspects of function declines, such as understanding of dashboard information, hearing loss, and diminished motor skills. Although these challenges may exist for both groups, we believe that the vision problems may be more pronounced for CVL drivers than non-CVL drivers.

Drivers with CVL reported higher use rates for blind spot warning, lane departure warning, forward collision warning, and forward collision avoidance than drivers without CVL, which suggests that drivers with CVL may perceive the potential benefits of ADAS technologies in helping them cope with vision loss (Deffler et al., 2022; Xu, Hutton, et al., 2023). The reported challenges with ADAS use were largely attributed to difficulties in perceiving visual information for drivers with CVL. For example, in line with previous studies (Cucuras et al., 2017; Stevens et al., 2022), the CVL participants in our study reported difficulty with GPS because of its predominantly visual information and small text size; consequently, they tended to rely more on the auditory instructions, which could lead to more errors. Challenges with cruise control and adaptive cruise control were attributed to small operation buttons on the steering wheel, whereas rearview camera issues were from its small display screen size and display glare.

Nearly all drivers reported rare or no failures of their ADAS (although this may only reflect their awareness), underscoring the technical reliability of these systems. However, the current car interface and ADAS systems primarily rely on visual and auditory information. Because of visibility difficulties, CVL participants tried to use strategies such as refraining from modifying settings, relying on tactile memory for adjustment, or using magnifiers. They suggested improvements including increasing contrast and dashboard lighting during daytime driving, using digital numbers rather than needles for dashboard information display, and using audio-based information. Regarding touchscreen challenges, participants expressed a desire for zoom capabilities to enlarge screen content. Therefore, a customizable user interface could significantly benefit this driving group. For instance, earlier tactile warnings could enhance safety for drivers with CVL when responding to imminent hazards (Xu & Bowers, 2024).

Voice control could alleviate difficulties and distractions associated with using visual information and touchscreens for older drivers with normal vision (Eby et al., 2018; Svancara et al., 2020). Although some studies argue that voice interaction may require more time commitment to complete tasks by older drivers (Stigall & Caine, 2020), the benefits of voice control may outweigh this concern for older drives with impaired vision. In our study, 67% of CVL participants found voice control useful, with approximately half feeling it compensates for their vision loss. However, in line with a prior study (Eby et al., 2018), the utilization of voice control remains limited, with only 27% of participants reporting its use, primarily for making phone calls (94%), and less frequently for GPS navigation (21%) or controlling car functions (15%). Expanding the range of functions controllable by voice commands and ensuring that these commands are highly reliable could potentially increase their usage and benefit among drivers with vision impairments.

Most participants with CVL expressed a desire for ADAS in their current or future vehicles, with over half wanting to choose specific features. Some hesitated because of perceived complexity, likely because of lack of knowledge, misconceptions, and past negative experiences. Among those interested, 76% of CVL participants sought guidance from health care professionals to identify the most suitable ADAS for compensating their vision impairments, indicating an awareness of the technology’s potential benefits for driving safety. Only 22% of current ADAS users reported receiving training from dealerships, consistent with a previous study of older drivers (Eby et al., 2018). Nearly all participants interested in future ADAS purchases wanted professional advice, and 86% showed interest in taking training courses. Notably, drivers with CVL preferred formal training from health care professionals, wanting multiple training sessions and check-ins throughout the year, whereas the non-CVL group favored one-time training from car professionals. This suggests that drivers with vision impairments are more motivated to understand and embrace ADAS because of the specific challenges their vision presents.

Several limitations should be noted when interpreting the results. First, self-reported information from the questionnaire may introduce recall bias. Both participants’ vision status and ADAS ownership in their current vehicles were self-reported without confirmation. Second, participants’ perceptions of vehicle interfaces and ADAS were informed by their familiarity and personal experience with in-vehicle technologies. Unmeasured factors such as differences in interface design across different cars or prior experience with specific ADAS technologies may be confounding. Future longitudinal studies in real-world driving contexts could help clarify these influences. Drivers’ perceptions of their willingness to adopt ADAS in future vehicles and their preferences for training may have been positively influenced by the specific focus of this study. CVL participants’ inclination toward receiving guidance from health care professionals may also have been positively influenced by the recruitment sources, because many of them were recruited from a low-vision rehabilitation clinic. However, drivers with CVL clearly needed professional guidance on selecting and using these technologies correctly, emphasizing the importance of knowledge and expert advice in maximizing the safety benefits of these systems for these drivers.

Implications for Occupational Therapy Practice

This study highlights the significant challenges older drivers with CVL face when using in-vehicle technologies and advanced driver assistance systems. Occupational therapy professionals have a unique opportunity to address these challenges and support safe driving for this population. The findings suggest the following implications for occupational therapy practice:

▪ Practitioners should be aware that drivers with CVL often struggle with using visual information on vehicle interfaces, including small fonts, small buttons, glare, and poor contrast, and may benefit from strategies and recommendations for navigating these difficulties.

▪ In-vehicle technology use can be considered during driver rehabilitation, particularly by assessing vision-related car use barriers and helping clients to explore individualized strategies for accessing and adjusting commonly used features.

▪ Practitioners can educate drivers with CVL about the potential benefits of ADAS for supporting driving. They can also incorporate existing educational resources (e.g., ADED, AARP Smart DriverTEK, MyCarDoesWhat.org) into client discussions and use them to stay informed about current automotive technologies relevant to older drivers.

▪ The strong interest among drivers with CVL in receiving professional guidance on ADAS use suggests a potential opportunity for occupational therapy to expand in this area. If integrated into practice, such training may need to be ongoing and tailored to clients’ visual capabilities and familiarity with technology.

Conclusion

This study met its objectives by identifying specific challenges that older drivers with CVL experience when interacting with in-vehicle technologies and ADAS. The findings enhance understanding of how visual impairments affect real-world driving tasks and highlight technology-related usability barriers that may affect this driving population. The documented need for guidance and training in using technologies safely and effectively suggests potential for tailored support from occupational therapists specializing in driving rehabilitation.

Supplemental Material

Supplemental Information

Supplementary material for Use Challenges and Training Needs of In-Vehicle Technologies for Older Drivers With Vision Impairments

Supplementary material, sj-pdf-1-aot-10.5014_ajot.2025.051078.pdf for Use Challenges and Training Needs of In-Vehicle Technologies for Older Drivers With Vision Impairments by Jing Xu and Abbie Hutton in The American Journal of Occupational Therapy

Footnotes

Acknowledgments

We thank all the participants who took part in this study, and we acknowledge the support of Dr. Donald Fletcher, Celina Litz, Karen Kendrick from Envision Low Vision Rehabilitation Center, Dr. Rui Ni from Wichita State University, Dr. Bradley Dougherty from Ohio State University, and MD support for their help in participant recruitment and data collection. We also acknowledge Dr. Ron Schuchard and Dr. Güler Arsal from the Envision Research Institute and Dr. Alex Bowers from the Schepens Eye Research Institute for their support of this project. We also thank Bosma Enterprises for support in this research. This work is supported by a Bosma Enterprises Low Vision Research Award to Jing Xu.

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