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Session

Case Study

Friday, June 28

08:30 AM - 09:00 AM

Live in San Francisco

Less Details

Radars are crucial for achieving safe and reliable autonomous driving because of their high sensitivity to detecting moving objects, whether occluded or at long ranges, and their robustness in adverse weather conditions. However, radars also face challenges such as low spatial resolution and high noise levels in the data, which limits the performance of radar-based perception AI models. So, how can we best utilize radars in autonomous vehicles?

This talk explores the current state-of-the-art in radar-based AI for autonomy and highlights promising new directions that could transform the use of radars in autonomous vehicles.

  • Discover how vital radars are for safe, reliable autonomous driving due to their sensitivity in detecting moving objects at long ranges and in adverse weather
  • Get a better understanding of the struggle radars face with low spatial resolution and high noise, limiting their effectiveness in perception AI models
  • Learn more about the cutting-edge advancements and new approaches to improve radar utilization in autonomous vehicles
Presentation

Speaker

Arvind Srivastav

Software Engineer Perception, Zoox

I lead research and development of AI perception models for autonomous vehicles. I apply my expertise in AI to develop novel transformers cross-attention based early sensor fusion models.
During my masters at Stanford, I focused on radar perception methods and AI models development. Before joining Zoox, I co-founded Werewolf AI, a B2B startup that created realistic rare-class datasets for autonomous companies. I am passionate about innovating and advocating for a safer and more sustainable future of our cities supported by autonomous vehicles.

Company

Zoox

Zoox is transforming mobility-as-a-service by developing a fully autonomous, purpose-built fleet designed for AI to drive and humans to enjoy.

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