Real-time Data Annotation: Innovations in Autonomous Vehicle Development

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The idea of self-driving and autonomous vehicles is certainly a reality and is being imported in several places around the world. The Tesla and their autopilot features driving down your road are certainly proof of that. However, it doesn’t just end with that. As we know, several issues crop up when using data annotation for vehicle development, some of which can pose safety risks for the rivers and passengers involved. This is where we need innovations in this field to mitigate some of the existing challenges.  This article explores some of these innovations that we can look forward to.

Infrastructural development

A fully automated vehicle has to be supported by the right kind of road and traffic infrastructure. For instance, it should be able to determine the speed limit by detecting traffic signs. However, in some places, traffic signs may be absent altogether, or there may not be any lane markers. This is where innovation can thrive.

Additionally, 5G needs to be used for a more connected vehicle infrastructure that is fit to move on its own on the roads without jeopardizing anyone; even in the absence of roadside traffic signs, driverless cars will behave safely if traffic signals or nearby vehicles transmit information.

Cybersecurity

Data privacy is an immense concern in this new age of technology and machine learning. In the case of mobility, this deals with things like your live location being used or leaked without your knowledge or consent. This makes tracking and stalking quite challenging. Therefore, innovations in robust security protocols should be developed to safeguard the car manufacturer’s data processed inside the vehicle and transmitted via cloud-based communication platforms.

Improving recognition and annotations

The artificial intelligence software in any self-driving car makes use of neural networks. The machine learning algorithm is used to detect any movement or objects on the road, such as road signs and traffic signals.

However, if AI fails to comprehend real-world situations, it can pretty much mean disaster. For instance,  if there’s a shopping bag flying in the wind in front of you, it may unnecessarily stop and cause a collision. This is where accurate annotation and image labeling are absolutely indispensable.

Also, human drivers deal with several complex social interactions. For example, detecting someone’s hand movements to indicate which direction they’ll be moving in or asking someone to stop

Wrapping up

This brings us closer to some of the areas of innovation when it comes to real-time data annotation for vehicle development. As the technology develops, there are certainly new solutions that we can look forward to. For instance, with more accurate data representation and professionals monitoring the annotations, the quality can certainly improve. This way, we can move towards safer driving solutions.

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