Premium ADAS data collection and ADAS annotation services with GTS AI

 

The technology for automated collecting data from driving speeds developing multi-sensor automated driving and advanced driver assist system (ADAS). It stores, analyze and replays massive quantities of data in raw form. It’s useful for sensor evaluation and prototyping. It can generate real-world test scenarios and provides information for training algorithms for vision and detection. It aids in the verification of pre-production sequences for hundreds of hours as well as millions of kilometers. This high-speed ADAS data collection inside the vehicle captures an uninterrupted, authentic uncompressed flow of data. The time-synchronous replay of raw data assists in developing machine learning and computer vision models used to drive autonomous vehicles.

The following capabilities are available in ADAS Data Collection.

ADAS Data Capture

You can enjoy an uninterrupted genuine, uncompressed, and authentic stream of data from LiDAR, high-resolution radar cameras, and automotive-standard busses and networks. This ADAS data collection system will design to capture every bit of data with high bandwidth with low latency and manage terabytes of data.

The GTS refers to ADAS Data Replay. It provides a time-synchronized replay of raw information to the Hardware (HIL) and software-in-the-loop (SIL) test systems. It helps create a real-world setting to make the machine learning and computer vision models required to safely and reliably power autonomous vehicles.

To verify the multi-sensor ADAS as well as autonomous driving systems. Allow the time-synchronized display of raw data from various sensors (camera, radar, and 3D sensors) and the corresponding vehicle bus or networks.

Notes for Advanced Driver Assistance Systems (ADAS) in Computer Vision

Advanced driver-assist systems (ADAS) give motorists and cars the latest technology and information to help them become alert to their environment and to manage possible situations with greater efficiency by using semi-automation. GTS ADAS Annotation assists in teaching these applications to detect diverse objects and scenarios while making automated, rapid and swift decisions to ensure safe driving.

What is the reason? ADAS for safe and controlled driving?

GTS ADAS, like self-driving automobiles, utilizes the same technology like radar vision, vision, and combinations of sensors, such as LIDAR, to automate dynamic driving activities such as braking, steering as well as acceleration for vehicles to provide safe and controlled driving.

To integrate these technologies, the ADAS needs labelled data to help train the algorithm.

ADAS data collection. Detect the driver’s diverse body movements and objects — a well-known method to generate the necessary training data to aid in computer vision annotation of images.

Difference between ADAS and self-driving cars?

In autonomous vehicles, the vehicle completely controls the driving, steering, stopping, and so on. There is no requirement to have a driver as it can travel in a specific direction and avoid all kinds of objects without human intervention.

The assistance system is installed within ADAS to aid or warn drivers when they fail to perceive the situation. If there is no driver awareness, all systems function semi-autonomously and will take the appropriate action to ensure safety and ease of driving.

Annotation of ADAS Traffic Detection

We employ the ground-truth labelling process to mark sensors’ data by the anticipated state of the automated driver system’s state. The appropriate mixture of Computer Vision techniques such as pattern recognition learning, feature extraction, learning and following, 3D vision, and other methods are employed to label ADAS traffic labels.

GTS is an acclaimed advanced driver assistance system provider that offers high-quality traffic detection data that can assist you in creating a real-time algorithm that is capable of detecting the traffic pattern in the future ADAS technology.

ADAS Object Detection Annotation

A high-quality labelled dataset is needed to support ADAS object detection, human facial recognition, and body motion detection. Different techniques for image annotation, like bounding boxes, polygons and semantic segmentation, are utilized to make these images.

Like self-driving cars, vehicles with ADAS can also analyze sensory information by separating roads from vehicles like pedestrians and cars. We mark all roadside items, like path lanes, signs, street lights, pedestrians, other vehicles, lane signs, and many more.

Monitoring Drivers ADAS Annotation

Drivers who are exhausted, distracted or sleepy will monitor through The ADAS, the driving monitor. ADAS monitors the driver’s mental workload, behavior as well as the environment of the vehicle. GTS is a way of notating ADAS systems with frames, which assist ADAS in analyzing the driver’s face, behavior and body movements.

Annotation for Facial Visual Analysis using ADAS

Face recognition software uses landmarks, also known as nodal point methods, to recognize faces. GTS AI offers landmark and points annotation services to precisely measure drivers’ distances between their eyes and mouths, ears, and looks. GTS AI has also created an annotation procedure for landmarks to create a 3D face-shaped model that can recognize face pose variations expression, as well as the complexity of the background.

Semantic segmentation

ADAS Semantic Segmentation The labelling and indexing of an object in frames is an annotation segmentation in ADAS. Each is marked with a unique color code without background noise if multiple things are present.

We focus on images with semantic segmentation, which is needed to recognize obligatory and fixed objects. Image segmentation will utilize in computer vision software that ranges from low-level vision, such as 3D reconstruction and motion estimation, to higher-level vision, such as object recognition in Computer Vision, which solves high-level vision problems like scene understating and image parsing.

ADAS data collection

The GTS assists in testing autonomous vehicles on the road. It is a costly process requiring certified, specialized drivers and unique test vehicles equipped with small data centers inside the trunk and numerous sensors. Every day autonomous driving and advanced assistance for drivers (AD/ADAS) collect vast data from testing vehicles. Data collecting is difficult and expensive because of the amount of data and the requirement to comply with customer service level agreements (SLAs) rigorously .makes data collection costly and complicated. Therefore, every test result must be high-quality and reliable data. Because inconsistencies in the data source are the most frequently cited reason behind failing, non-useable data, it is imperative to ensure that the automotive test data is correct at the time of collection.

How GTS can help you?

Global Technology Solutions is a AI based Data Collection and Data Annotation Company understands the need of having high-quality, precise datasets to train, test, and validate your models. As a result, we deliver 100% accurate and quality tested datasets. Image datasets, Speech datasets, Text datasets, ADAS annotation and Video datasets are among the datasets we offer. We offer services in over 200 languages.

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