AV2.0

1 review

AV2.0 is Wayve's next-generation autonomous vehicle technology, designed to deliver adaptable and scalable self-driving solutions. By leveraging end-to-end deep learning and a camera-first sensing suite, AV2.0 enables vehicles to learn from vast amounts of driving data, allowing them to quickly and safely adapt to new cities, diverse use cases, and various vehicle types. This approach contrasts with traditional autonomous systems that rely heavily on complex sensor arrays and high-definition maps, offering a more efficient path to widespread deployment.

Wayve AI Driver

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Wayve is revolutionizing the future of automated driving. As the leading developer of Embodied AI for automotive, we equip OEMs and fleet owners with the tools and technology to seamlessly transition from assisted to automated driving. Explore how Wayve can be your optimal AI partner, accelerating the journey towards tomorrow’s vehicles.

Safety 2.0 framework

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The Safety 2.0 framework, developed by Wayve in collaboration with WMG at the University of Warwick, is a comprehensive initiative aimed at ensuring the safe deployment of artificial intelligence (AI) in self-driving vehicles. This framework focuses on creating scalable methodologies and mechanisms to validate the safety of AI systems, addressing a critical need in the autonomous vehicle industry. By combining Wayve's expertise in end-to-end machine learning with WMG's proficiency in safety verification, the Safety 2.0 framework seeks to build public and regulatory confidence in AI-driven transportation solutions.

Fleet learning loop

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Wayve's Fleet Learning Loop is an innovative system designed to enhance autonomous driving capabilities through continuous, real-world data collection and machine learning. By integrating data-collection devices into commercial vehicle fleets, such as delivery vans, Wayve gathers extensive driving data during regular operations. This data is then processed using advanced AI algorithms, enabling the autonomous driving system to learn from human driving behaviors and adapt to diverse driving scenarios. The Fleet Learning Loop emphasizes safety and reliability by incorporating feedback from safety drivers and utilizing computer simulations to handle rare or hazardous situations. This iterative learning process ensures the autonomous system remains robust and responsive to the complexities of real-world driving environments.