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).
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
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.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Not recorded. The single citation of this work carries no recorded support check.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:70d782d22cb8 · see in the full table