End-to-end systems offer joint optimization for autonomous vehicle navigation
In short: Researchers reviewing over 270 papers found that end-to-end algorithms are gaining traction in autonomous driving. Unlike traditional modular pipelines that handle tasks like detection and prediction separately, these systems use raw sensor data to generate motion plans directly. This approach allows for joint feature optimization across perception and planning, helping vehicles navigate more complex scenarios.
The shift toward end-to-end frameworks is driven by the availability of large-scale datasets and the need for better performance in difficult environments. However, these systems face significant hurdles, including issues with interpretability, causal confusion, and robustness.
This survey synthesizes existing literature to map out the roadmap for the field. A primary limitation of the current research landscape is the ongoing challenge of making these complex,
Why it matters: Moving from modular to end-to-end systems allows for more holistic optimization but introduces new hurdles in safety and transparency.
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Sources
- Source: Research: Chen et al. (2024). End-to-End Autonomous Driving: Challenges and Frontiers. IEEE Transactions on Pattern Analysis and Machine Intelligence. · cc-by-nc-nd
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