Project Duration
8 Months
Role
User experience, Visual design, prototyping
Driver Risk Episodes
User Interface, User experience (2024)
Context
The Fleet web application serves as a comprehensive web portal for managers and fleet owners to access and analyze data related to their fleet operations. This data is organized around key entities, including subfleets (subsets of vehicles and drivers within a fleet), individual vehicles, and drivers. The platform tracks and displays events—instances where drivers engage in behaviors that increase risk, such as distractions, drowsiness, or tailgating. It also provides reports, which present tables summarizing event totals and rates for specific drivers, vehicles, or subfleets. Additionally, the application logs trips, capturing data from the start to the stop of a vehicle, along with any associated events, offering a detailed view of fleet activity and safety performance.
Problem
The current design of the Events page faces several challenges that limit its effectiveness and user engagement. Many users struggle to engage with the sheer volume of events generated and presented, often missing the "big picture" of driver behavior and safety trends. Close proximity and repeated events are frequently perceived as system bugs, further diminishing trust and usability. Data from FedEx reveals that only 18% of high-severity events and 6% of medium-severity events are viewed, highlighting a significant gap in attention to critical insights. On average, users view only 14-20 events per day, indicating the need for a more streamlined and impactful approach to event presentation.
Solution
The redesign of the Event Details page aims to enhance usability, reduce data costs, and provide deeper insights into driver behavior. By clustering individual events into risk episodes, the redesign focuses on patterns of high-risk behavior rather than isolated incidents. This approach minimizes data volume, enabling users to quickly identify and address critical safety concerns. To further optimize efficiency, only the most representative moments of risk episodes are uploaded, significantly reducing LTE data costs.
Additionally, risk episodes offer a more holistic understanding of driver behavior by highlighting patterns and trends that may be overlooked in individual events. The redesign also resolves the current “news feed” experience by introducing a curated feed organized around categories of driver behavior. This prescriptive user experience allows for easier navigation and interpretation of safety data, empowering fleet managers to take informed and timely action.
Single factor event
I redesigned the components of the event details screen to provide a more intuitive and streamlined information consumption experience. Drawing inspiration from my work on mobile, I implemented a vertically stacked layout for the interior and exterior video views. This approach simplifies the process of analyzing both perspectives simultaneously, enhancing usability and comprehension. Additionally, I aligned the video timeline lengths with the sensor data timelines to resolve inconsistencies in playback synchronization present in the previous design. To address the variability in displayed information across different event types, I introduced a consistent framework where value titles are always visible, leaving inputs blank when data is unavailable. This ensures a cleaner, more cohesive presentation of information, regardless of event type.
Combo event
A combo event occurs when two distinct types of events take place within a short, predefined time window—currently set to approximately 5 seconds. This interval represents the gap between the occurrence of the first event and the subsequent one. The Nauto device is fully capable of detecting and capturing these combo events, enabling a more comprehensive understanding of complex driving behaviors.
Multi-factor/Risk episode event
A risk episode, or multi-factor event, represents a cluster of individual events—including combo events—that occur within a broader, defined time frame. While the exact duration of this time window is still under consideration, it is preliminarily estimated to range between 5 seconds and 2 minutes. This approach provides a more holistic view of high-risk driving behaviors by capturing patterns and correlations that extend beyond isolated incidents.
Impact
The redesign of the Events page and introduction of Risk Episodes delivers impactful improvements across user experience, data efficiency, and differentiation.
From a user experience perspective, the redesign reduces cognitive load by shifting from an overwhelming event feed to a curated view of high-risk episodes, allowing users to easily understand the overall safety picture. By focusing attention on critical areas, users can quickly identify and prioritize significant safety concerns, leading to more efficient coaching and intervention. The adoption of a categorical design—reminiscent of popular streaming services—offers an intuitive and familiar navigation experience, enhancing engagement and usability.
On the efficiency front, the system reduces LTE data costs by uploading only key moments representative of risk episodes rather than media for every individual event. This minimizes data transmission and lowers cloud service costs.
In terms of differentiation, Nauto’s expertise in data-driven risk assessment allows for the identification and prioritization of high-risk episodes, moving beyond simple event filtering to provide a nuanced understanding of driver risk. Analyzing risk episodes also uncovers hidden patterns and trends in driver behavior that may be missed when focusing on individual events, offering deeper insights into the root causes of risky driving.
