Mapillary's Street Level Images will Help Train Autonomous Vehicles
References: mapillary & techcrunch
There's little doubt that future roads filled with self-driving cars will benefit both people and the planet in profound ways, but those working on the technology to get us to that point have had trouble in the quest to train autonomous vehicles. Thanks to Mapillary, the Swedish tech company that has accumulated a wealth of mapping images submitted by users online, training might have gotten easier for autonomous vehicle developers with limited resources. The company has announced that it will release 25,000 of its street-level images to help train autonomous vehicles.
The problem with training AI in driving is that the roads are off limits by necessity. The roads are a dangerous place for untrained drivers, and the same goes for their robotic counterparts. As such, a virtual driving space is valuable in teaching AI the rules of the road.
The problem with training AI in driving is that the roads are off limits by necessity. The roads are a dangerous place for untrained drivers, and the same goes for their robotic counterparts. As such, a virtual driving space is valuable in teaching AI the rules of the road.
Trend Themes
1. Crowdsourced Mapping Data - Crowdsourcing street-level mapping data can help provide the valuable information needed to help train autonomous vehicles.
2. AI Training - AI training through virtual driving spaces can help develop self-driving vehicles more safely and efficiently.
3. Autonomous Vehicles - Autonomous vehicles have the potential to revolutionize transportation, making it safer and more efficient.
Industry Implications
1. Transportation - The transportation industry can benefit from using crowdsourced mapping data to develop and train autonomous vehicles.
2. Tech - The tech industry can help develop AI-powered systems and virtual training spaces for the autonomous vehicle industry.
3. Artificial Intelligence - The AI industry can benefit from the increased demand for artificial intelligence and machine learning systems in the development of autonomous vehicles.
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