The Pulse Labs automotive platform provides a breadth and depth of driver behavioral data unique to the market. The platform acquires data through cameras configured at key vantage points within the automotive cabin, augmenting this data with cameras that capture the environment outside the car and telematics data from the car itself.

These data sources are combined and analyzed using proprietary machine learning models that identify and classify activities of interest. This removes the need for people to review potentially thousands of hours of video from multiple sources, facilitating a level of scale and consistency previously unavailable. With Pulse Labs, automotive researchers and product managers get the insights they need to make decisions and improvements without wasting time.

Pulse Labs offers two complementary options, benchmarking and evaluation, for driver data.

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The benchmarking data is sourced from a large and demographically representative panel of drivers, recording every drive each driver takes. Their natural driving interactions are observed without interference, providing an unbiased measure of consumer behavior within cars today. Some ways this data can be used include:

The comparison of critical features across different automotive makes and models, illuminating the most successful consumer experiences and the gaps between the best and the rest.

The measurement of key safety metrics like driver distraction across different cars and types of drivers, the analysis of factors that increase or mitigate distraction, and the surfacing of opportunities to improve automotive safety.

The evaluation of driver behavior at critical moments of interest, such as when an unexpected obstacle appears on the road or when the driver wants to find a place to eat in an unfamiliar location.


The evaluation data is sourced from drivers in panels who meet specific criteria and who are instructed in the tasks to perform during their drives. These drivers then provide feedback on their experience, including their opinion of the features used during the drive. Some examples of how evaluation data can be used include:

The comparison of key user interactions, like voice-controlled navigation, among different applications and automotive operating systems. The comparisons are designed to surface which applications served the user best and how they did this.

The measurement of user sentiment around safety and trust for an auto-pilot feature in different driving environments. For example, the evaluation could instruct drivers to use the auto-pilot feature on an open highway, a crowded city center, and a residential district, and provide their opinions on its performance in these environments and how safe they felt.

The testing of a new conversational drive-through ordering application among drivers who eat fast food on their way home from work at least two times per week. This test might be designed to uncover pain points in the ordering process and opportunities for improvement before the application’s marketing and wide release.

Data that matters

Utilize our real-world, naturalistic data to make informed product and design decisions that cut costs and reduce customer frustration.

*Supporting pre-release and production vehicles spanning hundreds of drivers globally.

An average Automotive study


190 hrs

Videos synthesized



Findings identified



Interactions analyzed



CUJs evaluated

Actions speak louder than words

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