Six Use Cases of Image Annotation in Autonomous Driving

Six Use Cases of Image Annotation in Autonomous Driving

The world around us is changing at a tremendous rate, largely due to technology. Autonomous driving vehicles were thought to be the future; now, they are here. They have made life easier for people by allowing us to enjoy the scenery and surrounding environment while in our vehicles’ comfort and get us to our destinations on time.

However, in autonomous driving, computerized imagery plays a major role in ensuring all the objects on the road are visible and recognizable to the vehicle. There are various techniques of image annotation used to ensure that the vehicle stays on track and manages to get passengers to their destinations.

The objects on the road are made recognizable and annotated to deep learning and machine learning through these image annotation techniques. They ensure that the surrounding objects and the entire path of the vehicle, including the street lights, road lane, and other cars and objects, are visible to the autonomous driving vehicle in their standard setting.

A different technique for image annotation is used for every object on the road, from the street lights to the road lanes, to ensure the car remains on track and doesn’t deviate from other traffic. Therefore, we will discuss the six different use cases of image annotation in autonomous driving or for self-driving vehicles on the road today.

  1. 3D Cuboid Offers Dimension Detection
    To make other objects on the road, such as oncoming traffic, detectable, dimension detection is vital for self-driving vehicles. A bounding box annotation also known as a 2D image annotation is used, which helps autonomous vehicles create a 2D image of the surrounding traffic and avoid any collisions. It’s one of the most simple but commonly used image annotation techniques to recognize traffic on the road for autonomous vehicles.

    The great success of dimension detection with 3D Cuboid relies on the technology that easily detects vehicles on the road. The second dimension vision that the autonomous vehicle gets allows it to identify and recognize all objects on the road quickly.
  2. LiDARs Sensing Offers 3D Point Cloud
    One of the most vital image annotation technologies used in autonomous driving today is the 3D point cloud for LiDAR Sensing. It creates an accurate positioning of the objects on the road. It helps autonomous vehicles easily detect objects with high accuracy on the road from the farthest distances and allows the car to avoid oncoming traffic with greater ease.

    The technology has potentially made it easier for autonomous driving vehicles on the road to go their way in traffic. The 3D Point Cloud for LiDAR Sensing is a crucial technique for image annotation because it allows accurate detection of the positioning of other objects on the road.
  3. ADAS offers Driver Monitoring Annotation
    The ADAS or Automated Driving Assistance System is already featured in many semi-autonomous vehicles today. It enables cars to detect movements in their surroundings while keeping a watchful eye on the driver’s actions. For instance, if a driver is feeling drowsy or is distracted on the road behind the wheel. This is where image annotation makes sense, as it provides greater road security.

    Drivers can feel safe behind the wheel knowing that their semi-autonomous car keeps a watch on their surroundings and will take over if required. They can be relaxed behind the wheel, knowing that image annotation technology will be used to protect them and avoid an accident.
  4. Classify Objects with Semantic Segmentation
    There are always different kinds of things on the road, and sometimes it can be hard to distinguish between similar objects and different objects for an autonomous vehicle. That’s where semantic segmentation comes into the picture, ensuring that all similar objects on the road can be recognized and classified. It protects the vehicle with image annotation and semantic segmentation, ensuring that there are no objects that remain unclassified.

    Semantic segmentation is a precise image annotation technique that delivers phenomenal results as it classifies all similar objects. That makes it easier for autonomous vehicles to recognize potential threats as different-sized objects appear on the roads. All of that is done to make it easier for the car to avoid different or similar-sized objects on the road.
  5. Lane Detection with Polyline Annotation
    It can be hard to detect which lane you’re driving in an autonomous vehicle, but that is a thing of the past with polyline annotation. It ensures that the car is always moving in the right direction by detecting the lane you’re traveling in. A simple line, Polyline, and Spline annotations are drawn on the roads to help detect the lane and allow autonomous vehicles to drive down the correct path.

    A different technique for annotation will be used for roads with different or multiple lanes, such as single-lane or double-lane roads. It ensures that the autonomous vehicle follows the correct path every single time, and there are no objects that hamper or obstruct its path.
  6. Safe Driving Image Annotation
    The last image annotation technique is used in autonomous vehicles to ensure that the vehicle is driving on the correct path and recognizing all road objects. It is known as the safe driving annotation, as it ensures that the autonomous vehicle is following the rules of the road and recognizes traffic lights and stop signs. This places the driver at ease, knowing that their autonomous vehicle is intelligent or smart enough to follow basic traffic laws.

Conclusion to the Six Use Cases of Image Annotation in Autonomous Driving

Autonomous cars are here, and even though they were predicted to be much more advanced than they are right now, they are still making a difference. Drivers on the road can feel much safer because the image annotation techniques used in autonomous vehicles are of the highest caliber and ensure a safer driving environment with fewer accidents for everyone involved. The six use cases of image annotation in autonomous driving we have shared are just the tip of the iceberg, and the rabbit hole goes much further down.

It is a given that with improvements in technology, there will be much more advancements, and autonomous driving will rule the world. It will become a staple everywhere, and we must thank image annotation techniques for that.

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