Manufacturers are relying on data more than ever, and drawing inspiration from physics, driver behavior, and statistics to design better vehicles. The more data we’re able to collect, analyze, and turn into actionable insights, the more advanced our consumer and commercial vehicles will become.
But how are data analysts and engineers working together to bring us better-designed road vehicles?
Types of Data to Collect
Let’s start by exploring some of the most important kinds of data that engineers use when they set out to design a new vehicle (or improve a current model):
- Traffic and accident data. First, data collectors look to historical collision data, such as this map of motorcycle accidents in Texas. By studying when, where, and how collisions occur, vehicle designers can track down fault points in their own machines, learn the root causes for accidents in a live environment, and thereby design vehicles that have a higher likelihood of avoiding or mitigating damage from collisions.
- Consumer-submitted data. Engineers try not to underestimate the power of consumer feedback. Vehicle designers take customer opinions very seriously, and collect as much data as they can from surveys and reviews to learn how to make their cars and trucks more appealing in the future. On a large enough scale, with quantifiable data points, they can spot useful, broad trends.
- App data. Finally, data analysts work with app developers to gain access to even more information about how and why people travel via road vehicles. Ridesharing apps like Lyft and Uber, along with traffic apps like Google Maps, provide worthwhile data to work with.
How Data Is Influencing Design
So how do analysts use these various kinds of data to create better cars and trucks?
- Better safety features. First, data analysts can use good information to determine the large-scale fault points that cause accidents. Previous means of data collection led to general insights (such as the role of speed or inebriation in causing traffic collisions), but modern data collection methods are much more precise and detailed. This empowers analysts to develop high-tech solutions to some of the most common problems they have to tackle (such as forward collision detection), and devise more highly reinforced portions of a vehicle that have been shown to be the most vulnerable to damage during collisions.
- Fewer manufacturing errors. Designers are also imagining vehicles with greater precision according to their vision, which feature more sophisticated systems that entail fewer manufacturing errors. This helps engineers stay in better communication with the manufacturers, and results in a finished product that’s closer to the original specifications than ever before.
- Fewer and better maintenance cycles. Data also enables engineers to pinpoint weak spots in a vehicle’s design that degrade over time, so they can increasing the machine’s lifespan and prevent the over-accumulation of wear. That creates fewer and better maintenance cycles, so it’s cheaper and easier to keep a truck or car on the road and safely functioning for an extended number of years.
- Provisions for future updates. Most new cars have at least some software features that help the vehicle operate more safely or more efficiently. Whichever is the case, the existence of such systems is beneficial in two ways. First, they provide yet another mechanism for data collection (by measuring consumer input); and second, they allow engineers to update the vehicle even after it’s been purchased, with new or improved features (to say nothing of bug fixes).
If you think this is impressive, the age of big data has only just begun for vehicles. Once self-driving cars begin to roll out, engineers and designers will have even more worthwhile data to work with ¦ and possibly more urgent imperatives in terms of consumer safety and convenience.
By some estimates, the average autonomous vehicle will produce up to one gigabyte of new data every second, which would total more than two petabytes per year once such machines become available to mainstream consumers. Now is the time for data analysts to build a foundation to create better vehicles by collecting and interpreting this data.
