The dual approaches to navigating autonomous vehicles in complex environments

Technology · Published 21 October 2026 Swipe more like this →

In short: Researchers reviewing the current landscape of autonomous driving technology found that motion planning generally falls into two categories: pipeline planning and end-to-end planning. While pipeline methods break driving tasks into distinct steps like selection and optimization, end-to-end methods use training approaches to map inputs directly to driving actions.

This review highlights how these different architectures offer unique trade-offs for system-level design. Pipeline methods allow for more granular control over specific operations, whereas end-to-end methods focus on holistic training across various validation scenarios. The study evaluates existing experimental platforms to help designers choose the best strategy for specific tasks.

The review is limited by its nature as a survey of existing literature and current technologies rather than a new experimental trial. It identifies current challenges and future perspectives but does not test a new specific motion planning algorithm.

Why it matters: Understanding the trade-offs between modular pipeline planning and holistic end-to-end learning is key to designing safer autonomous systems.

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