Data to Turbocharge AI for Autonomous Vehicles
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Successful Infusion of AI in Self-driving Vehicles Starts with Accurate Data
A hub of in-house experts to enrich data to enable self-driving cars’ computer vision to recognize, interpret, and interact with real-time objects on the road. Cogito offers extensive data annotation services to train ML algorithms for accurately infusing AI in self-driving cars.
Data Annotation for Autonomous Vehicles
Bounding Boxes Annotation for Object Detection
An autonomous vehicle relies on its ability to perceive its surroundings for safe driving. Here, experts can annotate bounding boxes to enable a car’s computer vision system to accurately measure the distance and dimensions of objects on the road and sideways.
3D Cuboid Annotation
The ability of a car’s computer vision system to detect objects may be compromised by high-resolution data containing millions of data points. Employ 3D cuboid annotation in data to discern the driving environment to avoid collisions as accurately as possible.
3D Point Cloud for LiDARs Sensing
Using 3D Point Cloud to enhance LiDAR sensors’ 3D image-sensing capabilities to help autonomous vehicles understand their surroundings, create accurate maps of objects placed on the premises, and detect other objects’/vehicles’ movements for risk-free autonomous driving
Polygon for Irregular Shapes Detection
Get experts on hand to draw precise polygons around oddly shaped objects. This way, computer vision can allow automated vehicles to recognize all visible objects on the road, including motorcycles, bicycles, cars, animals, etc., to promote safe driving avoiding collisions.
Semantic Segmentation for Object Classification
Semantic segmentation gives self-driving cars a competitive edge in object detection by treating multiple objects of the same class as a single entity. An image is semantically segmented by classifying all its pixels by their semantic content, such as all the people or all the cars.
Polyline Annotation for Lane Detection
Assist autonomous vehicles in identifying bicycle tracks, directions, divergences, and contrary-direction traffic by using polylines annotation expertise, enabling the vehicle to perceive its surroundings in order to enable safe and hassle-free driving on city streets and highways
Data Annotation Use Cases in Transportation
Annotation & labeling of data can serve a variety of purposes within the transportation system, from enabling cars’ computer vision to gaining a better sense of the surrounding for safe autonomous driving to AI-enabled parking management.
The following use cases can provide you with more insight into how our data annotation & labeling expertise can benefit your autonomous driving initiatives:
Vehicles with autonomous capabilities need properly annotated & labeled data as it allows them to achieve higher levels of object detection accuracy. Cogito’s data expertise can assist in developing well-functional computer vision for self-driving vehicles.
Traffic Detection (and Traffic Signs)
In addition to improving safety, AI can reduce human error and speed up the process of detecting and responding to accidents. In order to build a traffic detection model that is effective, plenty of labeled data is needed — something where Cogito excels.
Lane & Road Marking
Get all the data support needed to develop lane and road edge detection models, which help human drivers stay on track. Train autonomous vehicles to recognize other road markings, like arrows, STOP signs, and vertical landmarks, for safe driving.
Traffic Flow Analysis
Streamline road safety with pathway analysis, people counting, dwell time analysis, and more. Employ our AI training data to develop an automated traffic flow analysis application/system to analyze real-time vehicles, buses, and train counts.
AI-Powered Parking Management
The IoT tools, part of AI-driven smart parking management systems, are used to count parked vehicles and empty parking spaces in parking lots. Incorporating sensors and camera data into an AI-powered parking management system is what Cogito can help with.
Road Condition Monitoring (RCM)
A major challenge for maintaining a large network of transport infrastructures has been monitoring road conditions (RCM). Bank on our data for developing AI-powered in-vehicle optical road monitoring systems that classify and monitor road conditions in real time.
Automatic Traffic Incident Detection
Road networks, intersections, tunnels, and bridges can be monitored using video surveillance. With the help of our annotated and labeled data, the Automatic Incident Detection system will be able to detect traffic incidents faster and more accurately.
Automated License Plate Recognition
A vehicle’s license plate numbers can be automatically read using Auto Number Plate Recognition, or ANPR. Developing optical character recognition mechanisms for such models can be greatly aided by our natural language processing expertise.
In-Cabin/Driver or Occupant Monitoring
AI development for self-driving cars can be accelerated by collaborating with a data partner with extensive experience in autonomous vehicles. Cogito has experts to deliver high-quality data to deploy AI in Driver and Occupant Monitoring Systems (DMS and OMS).
Your Data Partner for Autonomous Vehicles
Engaging a data partner with extensive experience in autonomous vehicles is imperative to accelerating the AI development process for self-driving cars.
As a premier AI training data specialist for autonomous vehicles with over a decade of industry exposure, Cogito is a reliable name to partner with. Our AI training data practice is driven by an ethical agenda that includes GDPR, CCPA, the Fair Pay pledge, diversity, and inclusion.