Need for UX Most Wanted: What Tesla Drivers Were Looking For
Did you know that Tesla goes from 0 to 100 km/h in under three seconds? Only. Three. Seconds. Even though this supercar was created for everyday life, its owners attend a racing track once in a while. So we decided to make an interface that would resemble video games (e-ron-don-don :) — easy to understand and use. Here's how data, Tesla drivers and UX research may speed up innovation in automotive.
Today we are witnessing a flying development of automotive technology, with a new flood of electronic devices and programs watching the road and helping drivers react in all sorts of situations. The new technologies are replacing outdated controls, so now, instead of a whole lot of gears, sticks and buttons, we work with a touch screen. This in-vehicle tablet transforms cars into part of an entire ecosystem that includes smartphones, laptops and smart watches. No wonder UI and UX have become such hot topics in the automotive world.
What has spurred our interest is that so little attention in the automotive and beyond is paid to the fact that Tesla goes from 0 to 100 km/h in under three seconds. Only. Three. Seconds. It’s a real supercar! It’s unlikely that Tesla drivers have no interest in trying the vehicle out. What if we direct all the data from all the sensors and processors to help a driver analyze trajectory and speed, measure braking and acceleration within every round accomplished, detect weak spots and provide recommendations for faster and more effective lap times?
We decided to make a more in-depth analysis of this issue. To begin, we reviewed the already existing solutions, namely Performancebox, Volkswagen RaceApp, Harman, RacingMeter and other market leaders. They all provide information about car performance, including the dynamics of acceleration, top speed, braking distance, arrival time and the best lap. No doubt these factors are important, but do they provide enough data for a driver? Many restrictions may be explained by the fact that these are third-party programs with no access to many of a car’s sensors. However, they simply state facts while the racers (i.e., users) need much more information to perform better — in particular rotation speed and trajectory, ability to compare the results and produce a forecast for every lap during the race. The almighty Internet came in handy: we set up a topical discussion to find out what users lack.
Having collected plenty of information, the design solution clocked in by itself — an application enlightening a driver about the real capacity of their car, including the following:
- Maximum speed
- Letting a driver turn like a pro without drifting
- Exchanging lap experience with other drivers
- Evaluating results after each lap, comparing it with the previous results, and the possibility to improve the next round, etc.
Since Tesla is an everyday car, and its owners attend a racing track only once in a while, we decided to make an interface that would resemble video games (e-ron-don-don :) — easy to understand and use.
During the Race
This part reflects dynamic information on every lap: forecasted time, acceleration, braking and g-forces. These details let a driver immediately react and correct their driving style. With the estimated lap time, a driver knows if he falls behind or goes ahead. Being aware of reloading, it’s possible to select speed and trajectory needed to avoid drifting. The dynamics map makes it easier to select trajectory, displaying the next turn in advance. Trajectory recommendations are provided with the voice command so as not to overwhelm the driver with data. Even if a particular track is not yet in the database, it may be projected within the course of the test round.
After the Race
All the data collected during the race is summarized into a single report to create a general image of a user’s driving manner: lap time, speed dynamics, maximum acceleration and braking. What’s more, the map will display the strongest and weakest moments of the race, so the driver may analyze their ups and downs to improve their results.
This is the most gripping part of the app, with comparison being its key feature. A driver may get reviews of his race from different angles and then compare them with the results of any lap of his choice. By dragging a cursor through the graph, the data changes all over the screen displaying the vehicle’s state at any particular point. This way it’s more convenient to get a grasp of the lap trajectory. Also, video recording of the lap helps to analyze the context (environment, map location, etc.).
Since all the racing data is being saved, drivers may track their improvement within the year or compare their own results with the achievements of other racers. Being able to analyze the driving history makes it possible to improve driving skills much easier than previously.
This article serves as an example of how user research may lead to really innovative results and disclose hidden slots of the automotive industry. Nobody knows user needs better than users themselves, so we need to learn to listen to them. This concept doesn’t claim to be the best racing solution ever — we see it rather as an exercise, a sketch of ideas we are personally interested in. If you share our vision and support the presented ideas, take a look at our concept at Behance and let’s keep it spinning together.