Predictive Collision Avoidance in Connected Car Industry

Predictive Collision Avoidance in Connected Car Industry

Predictive Collision Avoidance in Connected Cars

Technology proposes drivers many features in ADAS (Advanced Driver Assistance Systems) and AD (Autonomous Driving AKA Driverless car) developments.  Among them, perhaps, predictive collision avoidance system is most cherished. With progressive sensors, big and fast data, and car-to-car connectivity, predictive analytics technology solutions will one day come to zero auto accidents.

Before proceeding to read further, make sure you have read introduction and details of predictive analytics in connected car industry in this post.

Predictive Collision Avoidance (PCA) Example:

A comparatively low-tech specimen of a predictive collision avoidance (PCA) method is Nissan’s Predictive Forward Collision Warning feature. By using sensors on the forward-facing side of the car, the system can examine the speed and distance to the car wandering ahead of the Nissan, as well as that of the subsequent prior vehicle that is typically outside the driver’s field of interpretation. When either of the proceeding two vehicles act in a fashion that could force the Nissan driver to brake quickly, the system alarms the driver with a visual alert and audible signal. A signal is also sent to shortly lock the seat belts in case of influence.

Nissan’s novel work signifies the most basic instance of a predictive collision avoidance system. As developers generate systems that upsurge communications between connected cars, more compound and more operative collision avoidance systems will develop based on predicting drivers’ performances, patterns and behaviors.

Connected Car Data Management

Generally, almost every connected car analytics application signify specimens of data management. Whatever application it maybe, such as using predictive data to enhance the efficiency of maintenance, promotion, safety, or extra actions connected to the connected car business, the data should be handled using a tactic that makes it valuable for the planned resolution.

Nevertheless, the necessity occurs to handle the data to and from connected cars from an entire-structure aspect. This necessity will become progressively ostensible as more and more onboard applications send and receive data through the Internet.

Assuming that every connected car produces around 25 gigabytes per hour data that more than 250 million connected cars are likely to be on the street by 2020, and you commence to view a concern. Even with low-price cloud storage, merely storing such gigantic amounts of data, even in the cloud, is not a convenient choice. Additionally, there is not a data plan in the world that can manage the essential bandwidth without touching drivers’ wallets in a big way.

The key to the data surplus is to develop smart data management solutions that can handle data proficiently both in the vehicle and in the cloud. Only by engaging predictive analysis based solutions, and perhaps deep learning algorithm based solutions, connected car can handle big data powerfully.

Whether resolutions are implemented as stand-alone applications or if they are incorporated into several platforms, the market potential is massive for creators who can revolutionize in this zone. By smart analysis of data streams to and from connected cars, data management applications will allow only data that is required to be replaced, and only when it can be used. Rather than examining stored data, actual resolutions will accomplish implementations that handle data in real-time, making resourceful use of the connected cars and available tools.

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