NavVizor,

Overview

NavVizor is an augmented reality (AR)-driven navigation system designed to enhance the driving experience by reducing cognitive load and keeping drivers more engaged with their surroundings. Our goal was to create a navigation system that not only provides clear, concise guidance but also minimizes mental strain, ensuring that drivers can make decisions quickly and safely. The project focused on using AR to simplify complex driving situations, particularly in urban environments, by offering seamless, intuitive navigation cues.

This case study outlines the approach we took to solve key driving challenges, how we integrated AR technology, and the user testing insights that validated our solution.

‍

Role and Team

I served as the UX Designer for this project, this is one for capstone project which we were able to showcase on a bigger platform. I would like to give a big shout out to my teammates; Swarna Pandu and Ashwitha Jathan. The project spanned 4 months, and we focused on a range of driving scenarios, from freeways to local streets, ensuring our solution was flexible and adaptable for different driving conditions.

Key Challenges

Navigating through complex urban environments is stressful and often confusing. Traditional navigation systems fail to provide sufficient guidance in these situations, especially when driving on multi-lane roads with frequent merges or lane switches.

Existing navigation systems often overwhelm drivers with too much information at once. We aimed to simplify the decision-making process and minimize distractions, ensuring the driver’s attention remains focused on the road. Three main objectives were:

1. Improve driver attention
2. Increase engagement with the road
3. Reduce mental effort

User Research Phase

We followed a structured UX process to ensure our design addressed user needs and aligned with project goals.

1. Literature Review

We researched around 20 articles, relevant to our study. Our research aimed to understand how AR technology affects drivers' cognitive load and psychological stress during navigation. Additionally, we examined visual distraction and how AR affects overall driver performance, with particular attention to its potential to enhance performance in monotonous or repetitive driving scenarios.

2. Benchmarking

We started benchmarking by exploring existing and futuristic technologies and concepts. Studied their features and discovered their pros and cons. Studied the maturity levels and the technologies used. This helped us to identify market gaps. Like we saw, nobody actually worked on the parking feature yet.

3. User Interviews and Contextual Inquiries

Conducting focus group interviews with individuals who have driving experience in the United States, we discovered that longer distances, watching exits, maintaining speed, and searching for parking spots are difficult. Drawbacks of existing apps:

• Rerouting is confusing
• Destination locations are not precise
• Audio navigation is distracting, especially during turns and when it interrupts other media.
• It is difficult to add stops/change the route while on the move.
• Preference for audio and visual navigation of maps depends on the driving situation.
• Notifications on the infotainment are distracting
• Voice assistants like Siri don’t help in adding/changing destination, as they pick the wrong location.

Discovery Phase

After analyzing interview data, we conducted an affinity mapping workshop to identify common pain points. These insights formed the foundation of our user personas. Based on our research, we created two key personas: Jordan, a 19-year-old college student who values simple, distraction-free technology to ensure safety while driving. Marie, a 30-year-old tech enthusiast who enjoys exploring new routes and relies heavily on connected car features for real-time updates.

“How might we design AR elements that keep the driver’s attention on the road while delivering clear navigation instructions?”

Ideation

The ideation phase was pivotal in generating innovative solutions to the challenges we identified:

1. Brainstorming & Idea Generation

We employed the Crazy 8’s method, where each team member sketched eight ideas in eight minutes. This rapid ideation process resulted in 65 potential ideas. We then filtered these ideas using the STAR method (evaluating on importance, feasibility, and novelty), ensuring we focused on solutions that were practical and effective for urban driving.

2. Storyboarding

After narrowing down our ideas, we created storyboards to visualize the user journey, particularly focusing on critical moments like lane changes, navigating complex intersections, and making turns

Prototyping

Our primary step was to convert our storyboards into feasible pen-and-paper low-fidelity prototypes. Then, the plan was to record videos while driving in the Dearborn and Detroit region and then augment our AR elements onto those videos. We considered 3 scenarios: freeways, local streets, and parking.

Finally, the next step was to identify the best tool for building our high-fidelity prototype videos. So we did in-depth research and implementation on tools like Adobe Aero, After Effects, Google's Geospatial Creator, Runway, and even tools like Canva and Clipchamp. We used After Effects to create our design system.

Usability Testing

We did a Qualitative study through user interviews. The quantitative study was carried out through NASA TLX and Likert scale ratings. NASA TLX was used to measure mental and physical load through each trial. A Likert scale was used to gauge the overall effectiveness and satisfaction of the participants.

At the end of the test, participants were asked a few questions related to demographics, usability, and effectiveness, and aspects of AR did users found most and least distracting.

‍

Feedback from users

Participants indicated certain advantages of our prototypes over traditional navigation apps, like allowing them to focus on the road without constantly looking at the map. They liked that they received lane guidance, warnings, and spatial awareness. They mentioned special examples like being able to be on the right lane, differentiating whether they have to take roads or bridges.

At the same time, they indicated disadvantages like elements appearing at instances where they are not expected to can be distracting, and users would take time to get used to such a kind of navigation.

High Fidelity Prototypes

The NavVizor high-fidelity prototype is installed on the infotainment system. Upon loading, the user selects diamond heist and is able to give preferences on text assistance, caution signals, and voice assistance. The preview is shown to the user based on their selection. The user can also turn the navigation on and off with one click. This prototype provided the flexibility for users to select their preferred theme and customize the elements display position, text, and voice assistance.

While the results on cognitive load were inconclusive, we believe there is significant potential for AR-based navigation systems to reduce mental strain in the future. Moving forward, we plan to investigate the effects of cognitive tunneling in AR navigation systems.