Imagine driving a new car down the highway. You’re enjoying the open road when a notification appears on the dashboard informing you that you should deviate from your usual route because there is a major backup. You select the alternate route suggested by the car and continue on your merry way, having saved at least an hour of travel time.
Several minutes later you receive another notification letting you know that if your brake pads are not changed within the next week, your brake rotors will begin assessing damage. You summon your car’s artificial intelligence assistant and request that they pencil a brake inspection into next week’s schedule. It informs you that the note has been made, saving you from hefty repair costs. As you approach the exit nearest your home, your car’s dashboard blinks a notification that the cars in front of you are slowing down rapidly. Rather than squinting and scanning for brake lights ahead of you, you begin slowing down and comfortably come to a stop several feet behind the car in front of you at the stoplight.
By imagining the scenario above, you are imagining a world in which cars are fuelled by predictive analytics, not just gasoline. As the role of predictive data analytics becomes more pronounced in the auto industry, so will features that draw upon the technology to create a better experience for both drivers and manufacturers. Consider how predictive analytics is already shaping the automotive industry what it could mean for the future of cars.
At the root of much of the pending technological progress of the auto industry is the fact that many new cars connect to the Internet. This allows for signals used for navigation, music streaming and even connectable Wi-Fi, to be transmitted through the car. Perhaps even more useful is the way some auto manufacturers have used data to capitalize on connected cars. Tesla, for example, uses car connectivity to not only track a car’s location and local driving conditions, but also to use a predictive data analysis program to help improve the car’s autopilot feature. This software combines the insights generated by thousands of individual Tesla cars to improve the precision and safety of the autopilot feature in Tesla cars collectively.
Data management and security
Research suggests that 75% of cars on the road will be connected by 2020. Think about it. Hundreds of millions of cars will be connected to the internet. They will be generating massive amounts of data daily – 25 gigabytes per hour according to some analysts. This means two things the data will need to be managed and protected. In order to manage such an incredible amount of data, automakers will not only need to utilize advanced storage mediums such as cloud and all flash storage, they will need to use rely on predictive data analytics to effectively manage the data. The same technology will also be useful in detecting and thwarting cyber-attacks on connected cars. Since predictive analysis technology excels at identifying patterns, this tool will be used to monitor the behavior of authorized users with the connected car interfaces. As a means of preventing hackers from entering a car’s network to steal data or even take control of the vehicle, predictive data software will identify and flag any unusual behavior so that it can be investigated and addressed.
Think of all the money car owners would save if they could negate the need for costly repairs through calculated maintenance procedures. When I say calculated, I mean through predictive analysis. By correlating data gathered from integrated sensors with the data from warranty repairs, car makers will be able to use this technology to predict major repairs before they are needed. The same strength used to identify patterns will draw upon data from thousands of cars to explain an anomaly in performance. This could not only prevent costly repairs for customers, but also costly recalls for manufacturers.
Predictive collision avoidance
Integrated sensors will not only be good for predicting maintenance – they will be helpful for predicting (and preventing) accidents as well. By monitoring the activity of surrounding cars and processing the data through predictive analytics, it is expected that data-driven cars will eventually be able to predict traffic accidents, ideally making them a thing of the past. Car companies have already begun exploring the possibilities. One of the most prominent examples of this application of the technology is Nissan’s predictive forward collision warning feature, which assesses the speed and distance of the cars in front and behind the vehicle in question. Drivers are warned and seatbelts are momentarily locked when either car drives in such a way that requires the vehicle in question to brake sharply.