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The data platform is key to connected cars and the internet of automotive things

What do you think of when you think of a computer? Most people envision a laptop or desktop station in an office. Some people might think of a server in a data center. Some people might even recognize that mobile devices, like smartphones and tablets, are actually just small, portable computers. How many people think of a connected car?

The car is emerging as the next personal computing platform. Many people have heard that the processing power in an iPhone is greater than that used in NASA’s Apollo computers of the 1960s. But the processing power onboard a car is an equally remarkable growth story. Consider that the computing power onboard a typical car today has increased 10 million times since the 1990 levels — that’s faster than Moore’s Law. In fact, the amount of computation onboard a modern vehicle is increasing by about an 80% compound annual growth rate (CAGR).

Connected cars and autonomous driving technologies are driving incredible value for the automotive industry and have attracted significant investments to “auto tech” in both the private and public markets. Concomitant with this growth is a fundamental shift in the data platforms used to support connected car applications because of their unique requirements. If we look at connected cars and autonomous driving as the next killer app, we can learn from the platform shifts that they are precipitating as harbingers of even greater movements to come. In particular, the data platforms used to support connected car applications must support:

  1. Real-time processing at scale. Increasingly sophisticated advanced driver-assistance systems (ADAS) need to process large volumes of data onboard the vehicle in real time to provide features such as collision avoidance, automatic braking and adaptive cruise control. The need is compounded as the car achieves greater levels of autonomous driving capabilities. An autonomous driving car gains a holistic understanding of the vehicle’s position and circumstances by combining multiple sensor outputs from devices including radar (10-100 KB/s), sonar (10-100 KB/s), GPS (50 KB/s), cameras (20-40 MB/s) and lidar (10-70 MB/s). In total, about 4 TB of data are generated and processed onboard the vehicle for every autonomous driving hour. The data platform, therefore, needs to support true real-time data processing and decision making (e.g., braking or accelerating).
  2. Machine and deep learning. While some of the systems onboard the vehicle utilize human-curated rules that help the vehicle make decisions quickly while on the road, there is an increasing emphasis on using machine learning and deep learning to make better decisions in real time. For example, pedestrian detection is difficult to implement using a rules-based system; instead, cars use deep learning models that do semantic segmentation of real-time dashboard-mounted camera feeds in order to detect pedestrians. This shift toward using machine learning requires the use of emerging software frameworks — like Caffe2 or TensorFlow — and there will likely be many more new entrants to come. Moreover, the process of training and deploying machine learning models has precipitated new, iterative development processes that require massive volumes of training data and the close collaboration between data scientists, application developers, data engineers and governance professionals. Data platforms supporting these applications need to support an incredibly broad variety of processing engines and data types and need to facilitate a complex application development process with as little friction as possible.
  3. Distributed computing. With increasing computational capabilities onboard the car itself coupled with internet connectivity, the modern car is the ultimate edge processing device. In addition to the real-time ADAS functions onboard the vehicle, the car sends relevant summary information a centralized fleet management application in a data center or cloud where it is aggregated across many vehicles in order to analyze fleet performance and to anticipate maintenance issues. In many instances, the data movement between the car and the data center/cloud must be bidirectional so that machine learning models can be rescored and improved over time through experiments in the data center and can be seamlessly redeployed to the vehicles. Vehicle-to-vehicle functionality will require further that the cars communicate in a peer-to-peer network that supports omnidirectional data movement. While the 2000s might have been termed the cloud era, the shift we are seeing today exemplified by connected cars is indicative of the rapid growth in total processing that is being done outside of any physical data center or public cloud environment. Consider that, compared to the 80% CAGR of computation onboard a car, industry estimates from IDC, Gartner and Wikibon put the growth of public cloud computing between 16%-19% CAGR. The data platforms that support connected car applications have to be infrastructure agnostic and have to support continuous, coordinated data flows — seamlessly moving data and compute between the data center and/or cloud and the vehicles.

As the car emerges as the next great consumer platform, the next vanguard of truly disruptive applications is motivating significantly new capabilities at the data platform level. As computation and data processing becomes ubiquitous with an increasing proportion taking place on the car itself, the primary challenges will be to manage the massive volume and velocity of data being generated on the car from different sensors, to facilitate real-time processing of that data using emerging computational frameworks, including machine learning, and to connect the car seamlessly with the data center. It’s no surprise then that, as with smartphones before, data management platform providers are placed squarely at the center of value creation in the connected car ecosystem.

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Data Center
Data Management