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AVs are usually discussed in the context of ‘smart cities’ but how do we ensure that the safety & societal benefits of autonomous vehicles reach rural communities?
Take a look at the real-world applications for automotive AI, understand the nuances of AI, machine learning, deep learning, etc so you know what best applies to your projects.
Autonomous vehicles require highly accurate, up-to-date maps for a safe and smooth driving experience. Explore how AVs can access real-time maps with minimal latency.
Automated Mobility on Demand (AMoD) is the brains that will drive the revolution towards self-driving cars.
Camera, ultrasonic, radar, LiDAR, infra-red. With such an overabundance of sensors now available, it’s time to look at what capabilities can be brought to the vehicle.
ClimaCell® are on a mission to map all of the weather data in the world. They're building the ultimate weather engine by pioneering the use of new weather sensing technologies and combining them with the best existing sources of data. This presentation will show why weather is critical in the development of Autonomous Vehicles.
Neural networks are becoming ubiquitous in solving the perception problems in ADAS and self-driving cars. In addition to the classical use of the neural networks for sign recognition, they are increasingly used in other areas, such as semantic segmentation, traffic lane recognition, etc.
Consumer research shows that safety is one of the biggest, if not the biggest concern about autonomous vehicles. While ADAS will most likely play a big role in overcoming those concerns, it will take a lot more than that to convince consumers of the safety and benefits of autonomous driving. Together with a panel of millennials, we will explore the concerns and the role that they see for ADAS in overcoming them.