Technically Speaking: Transitioning from Rule-Based to ML-Powered Motion Planning¶
Authors: Alexander Hu
Published: 2025 (Blog Post)
Source: Motional Blog
Summary¶
Overview of Motional's approach to motion planning: a scene encoder-generator-ranker architecture with joint prediction and planning, closed-loop reinforcement learning training, and data mining for real-world scalability.
Abstract¶
The future of autonomous vehicles (AVs) lies in scalability, adaptability, and human-like driving behavior. At Motional, we are pioneering the next evolution of AV technology by transitioning from traditional rule-based planning systems to an end-to-end machine learning (ML) powered motion planning system. This shift allows us to address some of the key limitations of legacy AV architectures and embrace a future where AVs rapidly learn, adapt, and improve with every mile driven. Traditional AV stacks follow a sequential modular pipeline: Sensors -> Perception → Tracking → Prediction → Rule-based Planning. While this approach offers modularity, interpretability and structured debugging, it also introduces significant drawbacks. By embracing an ML-first approach, Motional is building the foundation for the future: a fully end-to-end ML-based AV system. In this blog post, we will introduce our initial steps in this direction - developing an ML-powered Motion Planning system that integrates prediction and planning into a unified framework.
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Tags¶
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Autonomous driving
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Motion planning
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Machine learning
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Reinforcement learning
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End-to-end learning