Predicting Autonomous Futures
Dragomir explores the complexities of modeling long-term futures in autonomous driving, highlighting the challenge of predicting multiple potential outcomes for each agent in a dynamic environment. He discusses an innovative architecture that generates likely futures for agents, emphasizing the importance of non-deterministic paths and the interplay between decision-making and future predictions. The conversation delves into the implications of these predictions on real-world applications, particularly in the context of the Waymo dataset.In this clip
From this podcast
The Gradient
Drago Anguelov: Waymo and Autonomous Vehicles
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