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Sisällön tarjoaa Jayesh Jagasia. Jayesh Jagasia tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.
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AI in Automotive - #402 - Ben Rathaus - VP AI and Perception, Arbe Robotics

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Manage episode 380590059 series 2793161
Sisällön tarjoaa Jayesh Jagasia. Jayesh Jagasia tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.

Radars have been evolving at a really rapid clip, helped in no small part by innovative companies like Arbe Robotics. On today’s episode of the AI in Automotive Podcast, I am talking to Ben Rathaus, VP of AI and Perception at Arbe.

Ben talks us through the history of radars, and how and why they found their way onto cars. We discuss how Arbe’s silicon and software is creating an order of magnitude improvement in the resolution and performance of automotive grade radars. We talk about the composition of radars, and their output - a mapping of the free space around the vehicle - an absolutely key building block of AD and ADAS algorithms.

Ben and I started at cosmology and ended up at what the humble radar might look like in the future! Just another fascinating conversation that allowed me to understand the past and future of radars a lot better, as well as the very important role they play in making our cars smarter and safer. I think of them as the invisible, unsung heroes - working away diligently in the background, making everything around them work a lot better.

If you have ever wondered whether future radars can wholly replace cameras on the car… well, you will find out at the end of my chat with Ben. So go have a listen, and if you like what you hear, do share the AI in Automotive Podcast with a friend or colleague.

#ai #automotive #mobility #technology #podcast #radar #sensors #sensorfusion

AI in Automotive Podcast

  continue reading

40 jaksoa

Artwork
iconJaa
 
Manage episode 380590059 series 2793161
Sisällön tarjoaa Jayesh Jagasia. Jayesh Jagasia tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.

Radars have been evolving at a really rapid clip, helped in no small part by innovative companies like Arbe Robotics. On today’s episode of the AI in Automotive Podcast, I am talking to Ben Rathaus, VP of AI and Perception at Arbe.

Ben talks us through the history of radars, and how and why they found their way onto cars. We discuss how Arbe’s silicon and software is creating an order of magnitude improvement in the resolution and performance of automotive grade radars. We talk about the composition of radars, and their output - a mapping of the free space around the vehicle - an absolutely key building block of AD and ADAS algorithms.

Ben and I started at cosmology and ended up at what the humble radar might look like in the future! Just another fascinating conversation that allowed me to understand the past and future of radars a lot better, as well as the very important role they play in making our cars smarter and safer. I think of them as the invisible, unsung heroes - working away diligently in the background, making everything around them work a lot better.

If you have ever wondered whether future radars can wholly replace cameras on the car… well, you will find out at the end of my chat with Ben. So go have a listen, and if you like what you hear, do share the AI in Automotive Podcast with a friend or colleague.

#ai #automotive #mobility #technology #podcast #radar #sensors #sensorfusion

AI in Automotive Podcast

  continue reading

40 jaksoa

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