The growth of connected car provides both opportunities and challenges for the automotive industry and the developers who support it. Among the greatest challenges is what to do with all that data. As connected cars increasingly stream data into the cloud from telematics systems, infotainment systems, and the dizzying array of smart IoT sensors, each connected vehicle is apt to produce more than 25 gigabytes per hour as more cloud-based services come online. The key to using this deluge of data wisely lies in vehicle data analytics — and predictive analytics, in particular.
In this article, we will look at five of the ways predictive automotive data analytics will steer the growing connected car industry.
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Predictive maintenance, as you might suppose, aims to identify vehicle maintenance issues before they occur. By leveraging data from warranty repairs with current vehicle sensor data, predictive data analytics can find meaningful correlations that would be difficult for a human to discover. A performance anomaly that may appear insignificant when observed on a single vehicle can be a red flag when aggregated with data from hundreds or thousands of other vehicles that have the same problem. Predictive maintenance analytics applications can pull in data from virtually every vehicle of a given year and model and compare that information with warranty repair trends.
Developers new to the automotive data analytics space should explore the Pivotal technology stack. Among the services available within Pivotal is the Apache Hadoop backend, which is an ideal landing zone for connected car sensor data. The Hadoop framework interfaces seamlessly with Apache HAWQ (incubating), which provides native Apache Hadoop SQL database services that are designed for robust analytics involving large datasets. Further, Spring Cloud Data Flow enables developers to build cloud-based data pipelines for use in big data and real-time analytics.
As predictive analytics has access to increasingly larger datasets, automakers will be able to help your connected vehicle to spend more time on the road and less time in the shop.
Predictive Collision Avoidance
Technology offers drivers no feature that is, perhaps, more appreciated than predictive collision avoidance systems. Through the use of advanced sensors, big and fast data, and car-to-car connectivity, predictive analytics technology may one day make auto accidents a thing of the past.
A relatively low-tech example of a predictive collision avoidance system is Nissan’s Predictive Forward Collision Warning feature. By utilizing sensors on the front of the vehicle, the system is able to analyze the speed and distance to the vehicle traveling ahead of the Nissan, as well as that of the next preceding vehicle — which is usually outside the driver’s field of view. When either of the proceeding two vehicles behave in a manner that could force the Nissan driver to brake suddenly, the system alerts the driver with a visual alert and audible signal. A signal is also sent to temporarily lock the seat belts in case of impact.
Nissan’s noble effort represents the most fundamental example of a predictive collision avoidance system. As developers create applications that increase communications between connected vehicles, more complex and more effective collision avoidance systems will emerge based on predicting drivers’ behaviors.
Connected Car Cyber Security
If you want to know what keeps connected car makers up at night, you need to look no further than cyber security. With Gartner forecasting that a quarter billion connected vehicles will be on the road by 2020, it’s easy to see why they are concerned. The fact is, connected vehicles are no less susceptible to cyber attacks than any other device with an internet connection, but the consequences of a security breach could be infinitely more catastrophic. One can only imagine the lure autonomous vehicles, especially, pose for modern-day hackers.
If automakers are to make their vehicles secure from cyber threats, they must use technology that stays a step ahead of cyber criminals. Predictive analytics offers the power to do just that.
To understand how predictive analysis can detect a cyber attack before it occurs, we need a brief lesson on what a cyber attack is.
Whether cybercriminals target connected vehicles for the mere challenge, in order to profit, or as a form of activism, their approach is the same: find vulnerabilities in the networks and systems within the connected car ecosystem and use them to access driver’s personal information, or to gain control of vehicle systems. As cyber criminals “hack” into a system, they always introduce evidence of their presence. Conventional security measures are fairly effective at detecting evidence of an intruder when they use the same means to gain access as other intruders have used. But when an attacker exploits a yet-unknown vulnerability, the security program may offer little or no impediment.
What makes predictive analytics effective at securing connected cars, where conventional security measures might fail, is its ability to identify patterns. At some point, every intruder’s behavior will differ from that of an authorized user. Predictive analysis doesn’t just look for an intruder to repeat the same behaviors as pervious attackers. Instead, it looks for any behavior or combination of behaviors that are not consistent with what would be expected of an authorized user. While this is a very over-simplified explanation, the important thing to realize is the power of predictive data analytics to learn normal patterns of behavior, and to spot deviations from those behaviors.
Without any doubt, all serious development into cybersecurity for connected vehicles will rely on predictive analysis to outwit would-be attackers.
Next to keeping their products safe, you could say the most important thing automakers do is to attract new customers. Failure, here, means declining market share, or worse. Traditional marketing strategies have kept most car makers afloat, but television, radio, and print media are quickly losing their effectiveness as advertising mediums as audience sizes steadily decline. Today, every advertising dollar an automaker or dealership spends must go further than ever, and the need to nurture repeat business cannot be overstated.
Predictive analytics is perfectly suited to help the auto industry overcome these marketing challenges. By tapping the power of big data, predictive analytics applications can accurately identify persons most likely to purchase a vehicle in the near future. Complex algorithms consider such factors as time till last purchase, number of repairs on current vehicle, current mileage, percentage of last vehicle paid off before trade-in, and information culled from social media to identify likely buyers.
Developers who can integrate predictive analytics with Customer Relationship Management (CRM) platforms can help dealers to deliver highly-targeted advertisements to willing buyers.
Connected Car Data Management
In a sense, all connected car analytics applications represent examples of data management. Whether we are talking about using predictive data to improve the effectiveness of maintenance, marketing, security, or other activities related to the connected vehicle industry, the data must be managed in a way that makes it useful for the intended purpose.
However, the need exists to manage the data to and from connected vehicles from a whole-system aspect. This need will become increasingly apparent as more and more onboard applications send and receive data through the Internet. Considering that each connected vehicle generates about 25 gigabytes per hour, and that more than 250 million connected vehicles are expected to be on the road by 2020, and you begin to see a problem. Even with low-cost cloud storage, simply storing such huge amounts of data — even in the cloud — is not an option. Further, there isn’t a data plan in the world that can handle the required bandwidth without affecting drivers’ wallets in a big way.
The solution to the data glut is to implement intelligent data management solutions that can manage data efficiently both in the car and in the cloud. Only by employing predictive analysis, and probably deep learning, can connected car big data be managed efficiently.
Whether solutions are developed as stand-alone applications or if they are integrated into various platforms, the market potential is huge for developers who can innovate in this area. By intelligent analysis of data streams to and from connected vehicles, data management applications will permit only data that is needed to be exchanged, and only when it can be used. Rather than analyzing stored data, effective solutions will manage data in real-time, making efficient use of the connected car resources.
How Ignite Can Help?
One of the ripest markets for predictive analytics development is the connected car industry. From infotainment systems to self-driving cars, the need for driver based analytics applications will provide opportunity for forward-thinking innovators.
Ignite is a leader in custom automotive software development. Our world-class developers are experienced in all aspects of connected car and data analytics technology. With six R&D labs across Europe, we are able to handle your application in-house, regardless of its scope.
Predictive analytics use in the auto industry is expected to be a booming market. If offshore development is a part of your strategy, why not contact us today and let’s get started?