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Next-gen solutions for autonomous vehicles
The client is one of the leading technology companies that conceptualizes and develops next-generation solutions for autonomous vehicles. Their objective was to improve and enhance the functionalities of self-driving cars. This was made possible by integrating Machine Learning technology into autonomous cars enabling them to better sense the environment around them improving safety and reliability, requiring very little or no human intervention.
Engagement
The client wanted to improve the world's access to mobility while saving thousands of lives that are lost to traffic crashes. GlobalLogic partnered with the client to support their vision of building the world’s most experienced driver -- to make it safe and easy for people and things to get to their destinations.
Process
As part of the engagement, GlobalLogic applied Artificial Intelligence and Machine Learning (AI/ML) to create several datasets and neural networks to make autonomous cars navigate more safely. We trained classifiers for object detection and understood complex driving environments, learned to navigate through busy city streets with pedestrians, road work, traffic lights.
We leveraged human labellers and automation processes in training the car on navigating through all sorts of situations and environments.
We leveraged human labellers and automation processes in training the car on navigating through all sorts of situations and environments.
- Video Bounding box: This input enables sensors and software of the car to scan the objects around the vehicle (such as pedestrians, vehicles, cyclists etc)
- Video temporal: This input enables the car to efficiently read the traffic controls, stop signs, crosswalks and other road features to avoid any mishaps
- Image: This enables the software of the car to predict the behaviour of all things around it based on the speed and trajectory, and also helps in choosing other possible paths to avoid risk.
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