The paper introduces UniAD, a planning-oriented framework for autonomous driving that focuses on integrating perception, prediction, and planning tasks to optimize for safe and efficient driving. UniAD outperforms existing state-of-the-art methods in motion forecasting, occupancy prediction, and planning, showcasing the benefits of joint optimization and query-based communication between modules. Key challenges for future research include addressing computational complexity, handling long-tail scenarios, and exploring additional tasks like depth estimation and behavior prediction.

Read full paper: https://arxiv.org/abs/2212.10156

Tags: Autonomous Driving, Artificial Intelligence, Machine Learning

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