Technology has undergone major changes in the last 20 years. Two of the major techs that are quickly advancing today are machine learning and autonomous technology, offering convenience to people and businesses alike.
Below are some of the amazing developments made in machine learning and autonomous technology in recent years.

Unsupervised Algorithms

Unsupervised algorithms are used in machine learning in order to make predictions from particular datasets when only the input data is available without output variables. It is closely connected with true AI, where a machine can learn to identify complicated patterns and processes without any human intervention.

When algorithms are left alone to search and present interesting patterns in a particular dataset, hidden grouping or patterns can be discovered. In the coming years, we will see more improvements in unsupervised machine learning algorithms which can result in more accurate and faster machine learning predictions.


Car parking is not really a new development. The arrival of automated parallel parking is probably one of the earliest artificial intelligence exploits in autonomous driving tech. But, the concept has greatly evolved in the past few years.


smart parking

Now, parking, particularly in bigger cities, is a major headache since it increases stress and greenhouse gas emissions, as well as wasting productivity and time. Tech companies such as have developed and are continuously developing smart AI-based systems that offer data on available parking slots, parking times and locations.

Cars can even park itself without any accidents. Whenever the vehicle is on the move, it will receive information about the availability of parking at places that are closest to its GPS location. The data of parking spaces is sent to multiple cloud servers from vehicles that are then sent redistributed so that you can learn of parking space availability.

Enhanced Personalization

The personalization algorithms of machine learning are used to offer recommendations to users and then encourage them to complete particular actions. With such algorithms, you can synthesize data and make appropriate conclusions, like an individual’s interests.
For instance, algorithms can deduce from an individual’s browsing activity on an online retail website and learn that they are interested in purchasing a garden mower.

Without such insight, a buyer can leave the website without even making a purchase. However, some of the current recommendations are annoying and inaccurate which takes away from the experience of the user. In the future, however, personalization algorithms are more likely to be fine-tuned which will lead to a more successful and beneficial experience.

Smart Delivery Vehicles

You’ve seen delivery vehicles driven by humans to deliver packages. Now, you could see the very same task without humans, thanks to driverless vehicles offering higher efficiency and swiftness.
For instance, the world’s biggest mail and Logistics Company, DPDHL or Deutsche Post DHL Group, the leading computer graphics provider, Nvidia, and an automotive provider, ZF, have teamed up in order to deploy driverless electric light trucks that will transport and deliver packages to homes.

Kroger Supermarket Chain Will Test Driverless Delivery This Fall

These driverless trucks can deliver packages from a central point to many destinations. It is trained to assess its environment accurately for variables such as pedestrian behaviors, parking spot identifications, parking and traffic conditions.

The trucks are powered by the ProAI self-driving system from ZF, which is powered by the DRIVE PX palm-size supercomputer with LIDAR, cameras, sensors, and radar from Nvidia which feeds data into the system.
And the obvious benefit of excellent accuracy including no data fatigue, which the tech promises, includes the potential of extensive cost savings since the delivery process from the central point to the destination is the most expensive part for logistics companies.

Cars with Peripheral Visions

Knowledge of vehicles, objects, or pedestrians around a blind corner is a crucial factor for safe driving. Road blind spots have been the major culprit for most accidents.

However, a new AI tech allows cars to view and assess the speed and distance of vehicles, objects, and pedestrians around a blind corner.

An AI initiative called CornerCameras, by MIT researchers at the CSAIL or Computer Science and Artificial Intelligence Laboratory allows driverless cars to identify objects or people situated in blind corners of the roads.
Such tech does not actually see people or objects but uses light reflections. From the data received, it can direct the car for a better driving experience.

By Christopher Austin is the writer of this article. He is a regular contributor at many sites and mainly focuses on business, cars and auto-related topics. He also writes for a site advanceaccess offering car park payment systems and other entrance systems for businesses.  

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