Significance of Data Annotation for ADAS applications

Vehicle safety is one of the major areas in which automakers are making considerable investments. Automobile manufacturers have created a number of technologies that can aid in the prevention of traffic accidents over the years. Advanced Driver Assistance Systems are technologies that automate, facilitate, and improve vehicular systems to assist drivers in safe and better driving (ADAS).

What is ADAS Technology?

Advanced driver assistance systems (ADAS) are technological safety measures that help drivers prevent on-road incidents by alerting them to potential risks. This allows the driver to quickly regain control of their vehicle, boosting their capacity to react to road hazards.

Most vehicles nowadays come with standard safety measures pre-installed. Lane Departure Warning Systems and Blind Spot Warning Systems are examples of systems that employ microcontrollers, sensors, and cameras to relay signals of reflected objects ahead, to the side, and to the rear of the vehicle.

Advantages of ADAS include:

• Automated safety system adaptation and enhancement to improve driving among the general public. ADAS is designed to help drivers avoid collisions by employing technology to warn them about potential risks or take control of the vehicle to prevent them.
• Navigational alerts, such as automated lighting, adaptive cruise control, and pedestrian accident avoidance mitigation (PCAM), alert drivers to potential threats like cars in blind spots, lane departures, and more.
• Sensors may be able to self-calibrate in the future to focus on the systems’ inherent safety and dependability.

Data Annotation for ADAS systems

The installation of cameras in the vehicle necessitates the development of a new AI function that uses sensor fusion to recognize and process objects. Sensor fusion combines enormous volumes of data with the help of image recognition software, ultrasonic sensors, lidar, and radar, similar to how the human brain processes information. This technology is capable of reacting physically faster than a human driver. It can evaluate a streaming video in real-time, recognize what it’s showing, and decide how to respond.

Data annotation allows machine learning models for automated vehicles to locate themselves within the large context of the road system. This technique enables the following critical functionalities:

• Lane detection

Lane detection systems alert if the vehicle starts deviating from its lane. This is a core capability for all autonomous vehicles because it keeps them centered in the correct part of the lane. It also gives computer vision models information on where to move next when navigating across multi-lane highways. Lane detection is reliant on polyline annotation to accurately delineate the road markings that are relevant to AI models.

• Avoiding Collision

Forward Collision warning systems are in-vehicle electronic systems that notify the drivers in case of forwarding collision with any other vehicle or object in the roadway. Annotation for obstacles, vehicles, pedestrians, etc is thus crucial for vehicle safety. It can help autonomous vehicles to avoid collisions and obstacles by keeping them safe in designated lanes. Deviating from properly marked areas of the road runs the risk of collisions with median strips or vehicles in stopping lanes.

• Traffic Sign Recognition

Missing out any traffic sign can be a cause of a serious road accident. The real-time traffic sign recognition systems not only help drivers to follow the traffic signals but help them follow the traffic rules. Traffic sign recognition systems in a vehicle are equipped with forward-facing cameras to detect the on-road signs. Real-time feeds from the front cameras with image annotation, computer vision and image recognition algorithms help this system to recognize the traffic signs and display them on the infotainment system to be addressed by the driver.

• Parking Assistance Systems

Parking assistance systems are one of the most commonly used ADAS systems. Parking assistance systems generally use ultrasonic sensors, which are fixed on the front and rear bumpers of the vehicle to detect the obstacles while parking and trigger alarm. Data of these sensors are annotated to train them for assistance. The rear cam is also integrated with the system to provide visual assistance while parking. The system senses the distance between the vehicle and the obstacle

TagX Data Annotation Services

Since data annotation is very important for the overall success of your Automation systems, you should carefully choose your service provider. TagX offers data annotation services for ADAS and autonomous driving applications. Having a diverse pool of accredited professionals, access to the most advanced tools, cutting-edge technologies, and proven operational techniques, we constantly strive to improve the quality of our client’s AI algorithm predictions.

We have experts in the field who understand data and its allied concerns like no other. We could be your ideal partners as we bring to the table competencies like commitment, confidentiality, flexibility, and ownership to each project or collaboration. So, regardless of the type of data you intend to get annotations for, you could find that veteran team in us to meet your demands and goals. Get your AI models optimized for learning with us.