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DATA ANALYTICS

Driving and Data Collection

Driving and Data Collection
The Siliconreview
09 September, 2020

Automotive development services are largely responsible for the AI-based software that is revolutionizing the automotive industry and vehicular travel experience. These in-vehicle software systems merge with complex data platforms to create a seamless line of communication. Naturally, this software relies on the data drivers create by utilizing their vehicles – whether for a short trip to the grocery store or on a road trip across the country.

What Kind of Data Do Vehicles Create?

One of the issues that arise with software integration in vehicles is simply the massive amount of data that is created. In-vehicle data reads the status of every component of that vehicle, from the fuel gauge to the battery life – and regularly updates this information in real-time. Sensor data, such as the kind obtained from the tire pressure monitoring system, can inform drivers as to their tire’s average lifespan. Traffic information displayed on a GPS navigational software relies on existing data to determine average travel times. The same traffic information may also rely on user-reported information such as car accidents to provide more accurate data regarding travel times or to offer alternative routes to a particular destination.

What to Do With the Data?

The collection of data is undoubtedly important. However, it is more important to be able to translate that data into meaningful information on which we can make decisions. Data collected regarding the battery life in a vehicle is unimportant unless it can be utilized to warn drivers in advance of an impending dead battery. Sensor data is useless unless it can be utilized to warn a driver of inadequate tire pressure (or an oncoming collision!).

Traffic data on its own – that is, that there is traffic generally during the peak hours of 4-6 P.M. on a given weekday is fairly useless unless that data can be utilized to provide drivers with alternative routes that maximize the flow of traffic and helps everyone get to their destination a little faster.

The examples illustrated above generally highlight data that improves communication between the vehicle and the driver. However, some of this data can communicate valuable information to other entities. For example, vehicle-to-vehicle (or “V2V”) communication can be used to share traffic data. One vehicle can “tell” another to avoid a particular route due to an accident, without relying on users to input the data themselves. Vehicles can also provide valuable information to manufacturers by providing insight as to how manufacturers can design and produce vehicles that maximize utility for the end consumer.

Why Should We Care?

While data collection might not be anything new to consumers, the ways it is being interpreted and utilized may come as a pleasant surprise. Automotive development services are expanding on machine learning and AI to continually improve in-vehicle intelligence, recognize objects and pedestrians, and provide smart navigational routing. These features – relying on extensive data collection, automation, and connectivity - ultimately aim to make driving a better and safer experience for everyone on the road.