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Challenges and Solutions in Annotating Data for Driver Assistance Systems

The idea of self-driving and automated acres is certainly a reality and is being imported into several places around the world. The Teslas driving down your road are certainly a testimony to that. However, that is not the full picture. As we know, several issues crop up when using data annotation for driver assistance, some of which can pose safety risks for the rivers and passengers involved. This article explores some of these challenges which may be remedied by DataEntryOutsourced.

AI doesn’t have common sense

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 have disastrous consequences. For instance,  if there’s a newspaper flying in front, it may unnecessarily stop and cause a collision. This can also be the case with birds, where I don’t understand that the car can move as they fly ahead. 

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.

Infrastructure

A fully automated vehicle has to be supported by the right kind of traffic and road infrastructure. For instance, I 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.

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, rendering camera readings obsolete.

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 leaked or people being a bit not tracking your location through your consent. It’s also about the use of data being transmitted without their consent. Therefore, robust security protocols should be developed to safeguard the car manufacturer’s data processed inside the vehicle and transmitted via cloud-based communication platforms.

Wrapping up

This brings us to a close on some of the glaring challenges of data annotation for driver assistance. As the technology develops, there are certain 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. However, companies need to be transparent about their data usage so that user privacy is maintained throughout.