Planning-oriented Autonomous Driving

Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. We instantiate UniAD on the challenging nuScenes benchmark.

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• 1 In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads.
• 2 However, they might suffer from accumulative errors or deficient task coordination.
• 3 Instead, we argue that a favorable framework should be devised and optimized in pursuit of the ultimate goal, i.e., planning of the self-driving car.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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