How Big Data is driving the auto industry
Times are definitely changing when it comes to the transportation industry. Powered by big data, AI and the internet of things, our transportation system is poised to become more efficient and smarter . Some hot topics include self-driving cars, autonomous trucks, connected cars, remote controlled cargo ships, etc. Even though we haven’t yet achieved a widespread adoption of some of these technologies, they have already been tested, prototyped and it’s clear where the future of the auto industry is.
Connected cars, equipped with access to the internet’s wireless ecosystem, can share this access with devices inside and outside the car. These devices work together through APIs and other interfaces such that concepts like monitoring, governance and administration apply to the entire ecosystem. It is powered by Big Data, with self-learning and self-reporting. These cars have numerous benefits like mobility management. This includes functionalities like optimal route finding, fuel consumption optimization and live traffic information. Additionally, it can predict vehicle maintenance, enhance safety and avoid collisions, enable a suite of entertainment options etc. Moreover, the car manufacturer can get data about the performance metrics of the car, insurance companies can learn the driving behavior, infotainment companies can understand the usage patterns, etc.
Given that the connected car can generate 1TB data/per day on average, the question is how to process such massive amount of data in real-time to predict any anomaly or fault before it happens . This is being enabled by efficient data processing systems combined with the power of edge computing.
Self-driving cars employ a suite of technology, including GPS receivers, short-range wireless network interfaces, cameras, and an array of sensors, in an effort to uphold inter-vehicle communication. Vehicles interact with each other, but also communicate with sensors embedded in highway infrastructure and satellites, to help navigate streets, highways, and terrain, down to the nearest inch. It generates nearly 1 Gigabyte every second . It uses all that data to know where to drive and how fast to drive. It can even detect a new cigarette butt thrown on the ground and it infers that a person might appear all of a sudden from behind a corner or car . There are several other data sources, such as telematics or weather data, that can be also used to improve the driving experience .
Research has found that 81%  of car crashes are the result of human error. Whether it’s speeding, failure to comply with road laws, distracted driving, texting, drunk driving, or fatigue, there are clearly many factors that contribute to the number of accidents on the roads. Self-driving cars have even been called “more courteous and defensive than normal drivers” by Google’s director of the self-driving car program. Google’s fleet of autonomous vehicles are currently in testing, and eclipsed the 3-million-mile mark last year .
A major concern around all this is the security of the connected car. With these hundreds of OEM components, there are many opportunities for something to go wrong. In 2016, we saw how any Nissan Leaf could be hacked  from anywhere in the world as long as you had the VIN number of the car. This was due to an unsecured API in the HVAC component shipped by an OEM vendor. Not only device-level security must be addressed, but also data security during transmission and storage.
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