Core Capabilities
3D Cuboid Annotation
Precise 3D bounding boxes for LiDAR point clouds, ensuring accurate object detection and distance estimation.
Sensor Fusion
Synchronized labeling across multiple sensor modalities (Camera + LiDAR + Radar) for robust perception.
Semantic Segmentation
Pixel-level classification of road scenes, distinguishing drivable areas, lane markings, and obstacles.
Lane & Path Tracking
Polyline annotation for complex lane topologies, forks, merges, and intersections.
Traffic Sign Recognition
Detailed classification of traffic lights, signs, and road markings according to local regulations.
Behavior Prediction
Trajectory annotation for pedestrian and vehicle movement to train prediction models.
Data Annotation Use Cases in Autonomous Vehicles
Annotation & labeling of data can serve a variety of purposes within Autonomous Vehicles, from enabling critical computer vision to gaining a better sense of the surroundings for AI initiatives.
The following use cases provide more insight into how our data annotation & labeling expertise can benefit your Autonomous Vehicles operations:
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. To build an effective traffic detection model, plenty of labeled data is needed — something where Coral Mountain Data 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 Coral Mountain Data 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. Coral Mountain Data has experts to deliver high-quality data to deploy AI in Driver and Occupant Monitoring Systems (DMS and OMS).
More Success Stories
Explore how we solve complex data challenges.
Custom Collection of Scripted Utterance Speech Dataset
A leading company working in speech recognition and natural language processing technology approached us with the requirement of collecting a…
Custom Collection of Scripted Utterance Speech Dataset
A leading company working in speech recognition and natural language processing technology approached us with the requirement of collecting a…
Custom Collection of Scripted Utterance Speech Dataset
A leading company working in speech recognition and natural language processing technology approached us with the requirement of collecting a…