Data annotation & labeling blog.

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Data annotation

Data annotation

Data annotation is one of the important parts of machine learning, especially in AI example. To know more about this and get better knowledge on Data Annotation, we need to know ALL the basics! So, we will be putting up a blog on "Data Annotation" [AIN-103] with A - Z coverage which will help you in understanding different parts related to Annotation of data.

Labeling High-Resolution Radar Point Clouds for AV

Labeling High-Resolution Radar Point Clouds for AV

While cameras depend on lighting and LiDAR loses effectiveness due to beam scattering in water droplets or steam particles, high-resolution radars ensure stable operation in the most difficult meteorological conditions. The ability of radio waves to pass through fog, rain, and snow makes this sensor indispensable for safe movement in

Abstract of Multimodal Sensor Data Fusion

Abstract of Multimodal Sensor Data Fusion

Multimodal data fusion from sensors such as cameras, LiDAR, and radar is a component of a modern autonomous perception system. Each sensor has its own advantages. Cameras provide detailed visual and semantic information, LiDAR generates accurate 3D scene geometry, and radar displays objects in difficult weather conditions and at long

Time-Series Data Labeling for Self-Driving Cars

Time-Series Data Labeling for Self-Driving Cars

For an autonomous car to move safely through a city, simply recognizing objects around it is not enough. It must understand time and the sequence of events. In the world of autonomous driving, data does not exist as a set of random photos – it consists of continuous information streams where

Pedestrian Trajectory Prediction: Forecasting Crossing Behavior & Intent

Pedestrian Trajectory Prediction: Forecasting Crossing Behavior & Intent

In the modern world, transportation systems are developing rapidly, and with them, the complexity of interaction between different traffic participants is increasing. Every year, road traffic incidents result in significant losses, making it crucial to develop effective approaches for analyzing and predicting pedestrian behavior. The ability of systems to predict

Interactive Trajectory Planning: Modeling Dynamic Agent Interactions for Automotive Prediction

Interactive Trajectory Planning: Modeling Dynamic Agent Interactions for Automotive Prediction

Unlike traditional trajectory prediction, which considers each vehicle or pedestrian independently, interactive trajectory planning explicitly models the interdependencies between agents, capturing how the behavior of one participant affects the others. Game theory trajectory approaches are often used here to model strategic interactions and anticipate mutual influences among agents. This approach

Multi-Agent Trajectory Prediction in Autonomous Driving

Multi-Agent Trajectory Prediction in Autonomous Driving

Trajectory prediction is one of the fundamental and most complex tasks in the autonomous driving system. It involves calculating the probabilistic distribution of the future positions of all dynamic road users over a specific time horizon. The task of prediction goes beyond simple extrapolation of speed and direction because the

Web3 and Decentralized AI Training Platforms

Web3 and Decentralized AI Training Platforms

The majority of modern machine learning models are controlled by fewer than ten corporations. Such a concentration of power raises serious questions about fairness, innovation, and who truly benefits from the development of artificial intelligence. A new approach is now emerging that challenges this status quo by combining advanced systems