Training a new driver is straightforward and makes them practice until they can master basic skills well enough to pass a driver's license exam. But there are no such tests for Self Driving Cars, leaving it up to developers to decide when their technology is safe enough to deploy.
This means that for a vehicle to be truly capable of driving without human control or even with limited human intervention, autonomous systems must essentially be taught to understand the stimuli presented in real-time, requiring many different neural networks to work together, performing many different perception tasks that our brain is doing seamlessly.
The process of generating these many datasets is human labor intensive process and the human involvement is crucial in guiding the machine through the many cases and corners every Autonomous vehicle has thousands of humans working on the backend to annotate and decipher hundreds of thousands of images with high complexity and 99.5% accuracy, but this rate of accuracy can only be ensured through rigorous testing and validation.
Collection of Training Data for Self Driving Cars
Collecting this data requires using data other innovators make available or doing it yourself and scaling the process. Larger autonomous car developers deploy fleets of test vehicles to capture data. Unfortunately, collecting all this data in-house using a limited fleet of test vehicles isn’t sufficient. Test vehicles typically operate in limited geographies, such as a small group of cities, to start. For self-driving cars to become more widely available, their AI systems must be trained using globalized data sets. This includes paying attention to factors like differences in rules, road signs, area wildlife, unusual obstacles, and weather conditions.
Self Driving vehicles begin and end with data from the moment a vehicle’s sensors capture an image, a sound, or even a tactile sensation, a complex process of recognition, action determination and response occurs. And the ability for a vehicle to simply obtain this incoming information capturing sights, sounds and feelings on the road is not enough. All that data must be recognized, verified, and validated in a manner that is fast enough and smart enough to ensure that all safety and technical requirements are met.
Pixel-Perfect Data Annotation for Training Self-Driving Cars
Annotation is the process of labeling the object of interest in the image or video to help AI or Machine Learning models understand and recognize the objects detected by sensors.
In the Self Driving development process, a high volume of data is acquired from the test fleet through the cameras, ultrasonic sensors, radar, LiDAR, and GPS, which is then ingested from vehicle to the data lake. This ingested data is labeled and processed to build a testing suite for simulation, validation and verification of ADAS models. In order to get autonomous vehicles quickly on public roads, huge training data is required, and the current shortage of it is the biggest challenge.
Types of Annotations needed for Smart Training
For powering the AI behind Self Driving Cars, multiple types of annotations are required. These include but are not limited to:
Bounding boxes
Semantic segmentation
Object tracking
Video classification
Sensor fusion
Point cloud segmentation
These annotations feed the machine learning models processing training data from the multiple systems. Cameras record 2-D and 3-D images and videos. Radar is used for long, medium, and short-range distances using radio waves. Light detection and ranging (LiDAR) technology is used to map distance to surrounding objects. To properly train the multiple models and neural networks, a variety of annotations are required to identify and contextual things like road lines, traffic signals, road signs, objects in and near the road, depth and distance, pedestrians, and all the other relevant information on the road.
This annotated data assists in training and validating the perception and prediction models with precision. For autonomous vehicles, ground truth labeling helps in annotating urban scenarios, highway environments, road markings and sign boards, and different weather conditions that enables to efficiently train and detect moving objects. Manual labeling of these huge datasets requires significant resources, time and money. Several automation software tools and labeling apps that have evolved recently provide frameworks to create algorithms to automate the labeling process, ensuring the same precision and safety.
TagX - Data Annotation Service Provider
At TagX, we combine technology with human care to provide image annotations and video annotations with pixel-level accuracy. Our data labelers maintain quality while processing & labeling the image data which can be used efficiently for various AI and ML initiatives.
Since data annotation is very important for the overall success of your AI projects, you should carefully choose your service provider. 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.