The Distracted Driving Experience for Fleet Drivers

The Fleet Management Industry

Let’s take a look at the background and outlooks of the fleet management system industry.

Distracted Driving Impacts

Most have heard of the term "distracted driving", but there's more to it than just someone texting while driving.

For fleet truck drivers, some accidents can be caused by in-cabin distractions. For example, a long-haul driver might experience fatigue after their long shift, resulting in slower reaction times and increased cognitive overload.

In addition, factors like delivery punctuality and economic pressure to keep their jobs might contribute to behaviors like speeding.

They might also engage in other behaviors that take their hands off the wheel or eyes off the road, like smoking or phone use.

So with the advanced systems of fleet management systems, our project aims to better understand distracted driving habits. Therefore, we continued to the next stages of the project with this problem statement.

1. On-Site Visit & Hardware Walkthrough at Verizon Connect

In coordination with one of our mentors, my team visited the Verizon Connect office in Alpharetta.

Findings

• Drivers have difficulty hearing the safety/distraction alerts due to in-cabin noise & sounds in their environments
• Drivers are often distracted by frequent repetition of alerts and false alarms triggered by inaccurate AI detection
• Drivers are concerned about their privacy being invaded by driver-facing cameras

2. Semi-structured Interviews

This study was a qualitative method where we wanted to learn about fleet drivers and their habits. We interviewed 9 drivers.

Findings

• Alerts from the system cause information overload, but are helpful reminders in the long run
• Some drivers had imperfect driving habits, like using their phone while driving
• Drivers feel like these systems are scrutinizing, intrusive, distractions, and don’t always capture the extended context

3. Surveys

To gather some quantitative data from our target audience, we sent out online surveys via the platform userinterviews.com. We received 76 valid survey responses.

Previous Next

Findings

• Drivers are most uncomfortable with driver-facing cameras
• Driver-facing cameras have the most drawbacks, including privacy intrusion, false alarm, trust issues, lack of customization, and are distracting
• Road and rear view cameras are the most helpful
• Alerts are more distracting rather than accurate or helpful

4. Competitive Analysis

Lastly, we gathered some data about competitors, what features stood out and were standard in the fleet management industry. These advantages and disadvantages from other products could show what would may help drivers as they work.

My Role

• Research and gathered information on specific competitor features and metrics

Goals

• Assess strengths & weaknesses across fleet management system competitors
• Identify gaps in driver-friendliness, privacy features, control over hardware

Procedure

• Each team member analyzed a competitor based on the set metrics (Geotab, Lytx, Omnitracs, Samsara)
• Organized information in a Google Sheet
• Reviewed the data that we gathered as a team
• Constraint: For some competitors, it was more difficult to directly access their product for comparison

Research Findings & User Needs

From our research, we analyzed the data and found common themes on what the users, the fleet drivers, need when they are driving and interacting with the fleet management systems.

We found that a big point made by many drivers was that the alert system was often more distracting than it was helpful. These alerts come through the systems built-in audio system, but the alerts that chime are described as many times being uninformative, too loud, or too repetitive. In the cases of repetitiveness, drivers felt especially annoyed when they were false alerts, errors from the detection system. They felt that they had no control over the system, even in the events of these technical failures. Most of the drivers we interviewed took their jobs extremely seriously, detailing how they are under strict regulations on their driving habits, so having these added in-cabin issues can lead to further distraction.

From the data across our research, we created a list of user needs. To summarize, drivers need to stay focused on driving without being distracted and have system alerts clearly, concisely, and effectively communicated to them either verbally or visually. By doing so, drivers can feel more confident and safe, knowing that they will be adequately warned about their surroundings while they are working. However, they should also feel confident that in cases that the system fails, they have a way to take some control over the problem. Like some of our interviewees stated, they should not be burdened with false alerts or audio alerts that chime so loud and often that they get headaches.

Low Fidelity Wireframes

Suggested from the feedback on our sketches, our wireframes combined most of our sketch concepts into a single, physical prototype, which was simulated on Figma (embedded below). There were 3 flows involving dialogue, external alerts, and internal alerts. There are also physical buttons at the button of the tablet: volume control, emergency, false report.

Cabin-View & Tablet

Since we involved digital prototypes, we wanted to create a view that simulates the driver being in the cabin. The cabin-view contains a steering wheel, a camera, and a tablet. The tablet has buttons and knobs at the button, for volume control, emergency reporting, and false alert reporting.

Dialogue Flow - Voice Assistant Only

The system, beyond just giving audio alerts, comes with a voice assistant agent, like brainstormed from our sketches. In the first flow, the driver can initiate a conversation, like sending a message to their manager to update them on delays. In the second scenario, when the driver is detected as detected as drowsy, the assistant will provide the warning and also ask if they would like to be rerouted.

Internal Alerts

When the system flags an alert based on the driver’s behaviors from within the cabin, the alert will display on the tablet screen alongside an audio alert (e.g. smoking detected). We created two versions, where one had the alerts as banners and the other displayed the alert in full-screen. Each type of alert has a unique icon next to the text description.

External Alerts

When the driver encounters a hazard from outside, like surrounding traffic or lane departures, the system alerts the user audibly and visually. The camera views affected are also highlighted based on the type of warning.

How it Works

We created a frame out of firm cardboard and used a laser cutter to create button spaces at the bottom and a center window for an iPad to slide in. The left button is the volume knob and the right two spring-loaded buttons are the emergency and false report buttons. The iPad displays a Figma prototype. From wireframe to this prototype, most of the features remained similar. The frame with the tablet inserted can then be attached to the dashboard of a vehicle.