Can Quality Data Annotation Help Make Autonomous Trucks a Reality in the Next Several Years?

Category: AI insights

Published date: 02.02.2022

Read time: 6 min

Five years ago, it was difficult to imagine on the road cars that are driven not by a person, but by a computer. Now artificial intelligence in the transport sector has become a close reality. Unmanned passenger taxis can already be found in cities, and in the very near future, this will also happen with freight transport. Robotic vehicles very quickly arrive on our roads straight from science fiction films. What will the emergence of AI-driven trucks mean for us? What changes await trucking? What future holds for the trucker profession? Today we will take a look at all of these questions and explore the role data annotation plays in the development of such vehicles. 

What is the State of AI in the Transportation Industry? 

Research firm MarketsandMarkets estimates the AI market within the transportation industry will grow at a compound annual growth rate of almost 18% between 2017 and 2030, and its size increase from $1.2 billion in 2017 to $10.3 billion in 2030. Truck manufacturers including Daimler, Volvo, Navistar, Paccar, and others, have already begun developing autonomous truck technology, for example. Waymo, an American autonomous driving technology development company has also installed self-driving technology in semi-trucks and plans to test on haulage routes in New Mexico and Texas. Pittsburgh-based Locomotion, an autonomous trucking technology company, expects to equip at least 1,120 Wilson Logistics tractors with its Autonomous Relay Convoy (ARC) technology starting in 2022.

In addition to autonomous driving, the trucking industry has the potential to reap many benefits from AI technology in accident prevention and safety, fuel efficiency, route optimization, and workflow management.

How Can AI Improve Long-Haul Trucking? 

The first thing that immediately grabs attention is the economic benefit of using AI vehicles in transportation. In this case, the cost of transportation can be reduced significantly. The trucker’s salary is immediately deducted from the costs, in addition, a decrease in fuel consumption will play a role. The driver needs to rest and eat, sleep at night, and the drone will not spend all this time, it will continue to move even at night. In the future, when self-driving trucks become a mass phenomenon, it will be possible to save money on the use of electric motors and hydrogen fuel.

The second great advantage is traffic safety. It is security that has become a key positive point in AI technology, which is noted by all specialists. They draw an analogy with airplane pilots – after all, flights are mostly automatic. At the same time, emergency situations arise exactly when the pilot turns off the automation and takes control of the vessel. The unmanned truck will not violate any traffic rules, will not exceed the speed, the AI ​​will not fall asleep while driving from fatigue, and will not come into conflict on the road.

We also have to consider things like fleet management. AI offers the perfect partner for fleet managers, increasing their effectiveness and helping to streamline and make processes more efficient. For example, these technologies can detect patterns humans might miss, increasing productivity by more accurately pinpointing which drivers to assign certain loads.

How Can Quality Data Annotation Help Make Autonomous Trucks a Reality? 

As the drivers are driving to their destination, they need to be able to recognize all of the objects they might encounter on the road such as cars, street signs, bicyclists, pedestrians, and many other things. They also need to know what to do when they encounter various scenarios. This is where data annotation plays a crucial role. Basically, the raw training data is prepared through various annotation methods that allow the AI system to understand what it needs to learn. For the automotive sector, some of the most common data annotation methods include 3D Point Cloud annotation, video labeling, full scene segmentation. 

If your training data consists of videos, these will also need to be annotated with methods such as 2D/3D bounding boxes, tagging, lines and splines, and many other methods as well. It is worth pointing out that video data annotation can be very time consuming because very often each frame of the video would need to be annotated and if the video resolution is 30 frames per second (fps) or even 60 (fps), you can imagine how much time it would take to annotate all of this data. 

Trust Mindy Support With All of Your Data Annotation Needs

As mentioned earlier, data annotation could be very time-consuming which is why so many companies outsource their data annotation projects to Mindy Support. We are one of the leading European vendors for data annotation and business process outsourcing, trusted by several Fortune 500 and GAFAM companies, as well as innovative startups. With 9 years of experience under our belt and 10 locations in Cyprus, Poland, Ukraine, and other geographies globally, Mindy Support’s team now stands strong with 2000+ professionals helping companies with their most advanced data annotation challenges. Contact us today to learn more about how we can help you. 


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