Enabling Connected Intelligent Vehicles with The Fusion Project Machine Learning-Based Data

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Enabling Connected Intelligent Vehicles with The Fusion Project Machine Learning-Based Data

Enabling Connected Intelligent Vehicles with The Fusion Project Machine Learning-Based Data

There are many challenges in the automotive industry today to cost-effectively and widely access and the massive amount of data available in new vehicles. Vehicle data from dozens of sensors like camera, LiDAR, radar and inertial motion, along with operational data from vehicle ECUS can provide valuable real-time insights into vehicle driving environments and vehicle performance. Key vehicle data can be pre-processed on the edge in the vehicle and compressed up to 98% while maintaining high fidelity for advanced analytics and training/deploying machine learning models to continuously improve vehicles from design through their production lifecycle. In this session, we will identify the industry challenges and how a consortium of five industry companies formed The Fusion Project to provide a complete vehicle-to-edge data lifecycle that integrates machine learning training and deployment in a cost-effective way that reduces training time by 10x.

Presenter:

Brian Carlson, Director, Global Product and Solutions Marketing, Vehicle Control and Networking Solutions, NXP

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Last update:
‎04-09-2021 01:53 PM
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