Integrating Algorithmic Sampling-Based Motion Planning with Learning in Autonomous Driving¶
Sun, Y. (2021). Integrating Algorithmic Sampling-Based Motion Planning with Learning in Autonomous Driving. ACM Transactions on Intelligent Systems and Technology, 12(5).
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Primes¶
- Fast-Path / Slow-Path Architecture
- A self-driving stack maps cleanly onto the same dials: a learned end-to-end controller drives the routine long tail at bounded latency (fast path), a sampling-based planner with explicit constraint reasoning arbitrates when an uncertainty estimate spikes or perception flags an unfamiliar object (slow path), and the uncertainty threshold is the trigger.
This sourceCombines a learned component with a sampling-based motion planner for autonomous driving, with learning routing/guiding the more expensive planner, supporting the self-driving fast-path-controller / slow-path-planner split gated by uncertainty.
- A self-driving stack maps cleanly onto the same dials: a learned end-to-end controller drives the routine long tail at bounded latency (fast path), a sampling-based planner with explicit constraint reasoning arbitrates when an uncertainty estimate spikes or perception flags an unfamiliar object (slow path), and the uncertainty threshold is the trigger.
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