A monumental shift is on the horizon of the automotive industry and it’s about autonomous cars. The key technology that makes this possible is edge computing. Now cars must collect as much data as possible using sensors and cameras, and then process it. A data-driven approach will allow you to outperform humans, as dozens of factors will be used to make decisions. Not all of them can process the human brain in such a short time. A deluge of real-time data from self-driving vehicles, driver-monitoring systems, and surveillance cameras, fueling artificial intelligence algorithms.
At the same time, self-driving cars rely quite heavily on apps. In addition to the necessary applications, some are created simply for the entertainment of passengers. This means that the number and quality of applications for modern cars will grow.
The Role Of Edge Computing
The domain of edge computing encompasses computing storage, data management, data analysis, and networking technologies. Real-time data processing is facilitated at the edge, empowering applications and devices to swiftly respond to incoming data.
Enhancing safety, efficiency, accident reduction, and traffic congestion alleviation are all achievable targets for autonomous vehicles. Such cars have many sensors that make it possible to make decisions more quickly and thoroughly than a person does.
Machine learning algorithms utilized in self-driving cars extract valuable insights from raw data to ascertain road conditions and make informed decisions accordingly. These insights encompass pedestrian locations, driving conditions, light levels, road conditions, and surrounding objects. The processing of this substantial data volume takes place at the edge.
V2X and V2I
V2X technology reduces the computing demand on autonomous driving edge computing systems. It is defined as a vehicle communication system that primarily focuses on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interaction goals. Unlike conventional autonomous driving systems that require expensive sensors and edge computing equipment onboard the vehicle, V2X takes a different approach by investing in road infrastructure. This approach helps alleviate the computing and sensing costs in vehicles.
With the increasing deployment of edge computing facilities in road infrastructure, autonomous driving applications are leveraging V2X communications to enhance the efficiency of in-vehicle edge computing systems. One notable application is cooperative autonomous driving, where the collaboration between autonomous driving edge computing systems and V2X technology facilitates the development of a safe and efficient autonomous driving system.
Along with the introduction of applications in autonomous vehicles, the question of safety arises. In the same way that you can track location on an iPhone, you can find out the exact location of the car. If this data falls into the wrong hands, it will be extremely unpleasant, and possibly dangerous. If you want to know how to change location, then just use a VPN. In this case, no one from the outside will be able to track you. With VeePN, you can also secure your apps. Also, VeePN will be useful for unblocking access to Netflix or regionally blocked apps.
Autonomous App Navigation
If humanity uses data from car navigation and hardware sensors in the right way, it will be able to achieve autonomous navigation. But it also requires the creation of powerful software that can objectively consider various factors and make better decisions. Now the difficulty lies in learning how to collect all the signals from more complex devices like Lidar, Radar, and cameras. Recognizing objects in a fraction of a second will open up the possibility of making autonomous driving a better alternative than self-driving.
To examine the composition of mobile apps, we can utilize standard app testing frameworks Espresso and UI Automator for Android, and XCTest for iOS. These tools enable automation of app launching on a device, data collection regarding UI construction, and interaction with the UI. However, we still lack an understanding of the purpose and intended effect of these widgets and the overall screen. Plus, there is the issue of security, but it can be solved through a web VPN or other forms of VPN. But this is also worth thinking about in advance.
This is where computer vision becomes invaluable. Similar to how autonomous cars employ cameras to capture images of the environment and identify objects, we can use app screenshots to determine available widgets for interaction at any given time. Computer vision algorithms can answer questions like, “How should I interpret the individual objects on the screen?” Nevertheless, an algorithm working with this data will still need to effectively press buttons, input text, and interact with menus, among other tasks.
Conclusion
Autonomous cars use the data they collect to do their job. Applications can build on this same data and extend the capabilities of vehicles. Moreover, computing power allows you to integrate both entertainment and offline applications into the vehicle’s on-board network.
