MISTY delivers state-of-the-art closed-loop scores on nuPlan Test14-hard (80.32 non-reactive, 82.21 reactive) at 10.1 ms latency via single-step MLP-Mixer inference and a latent drifting loss that encourages proactive maneuvers.
Nuplan: A closed-loop ml- based planning benchmark for autonomous vehicles
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
dataset 1polarities
use dataset 1representative citing papers
Synergistic Simplex enables bidirectional cooperation between ML components and safety monitors in autonomous systems, preserving safety guarantees while improving performance.
citing papers explorer
-
MISTY: High-Throughput Motion Planning via Mixer-based Single-step Drifting
MISTY delivers state-of-the-art closed-loop scores on nuPlan Test14-hard (80.32 non-reactive, 82.21 reactive) at 10.1 ms latency via single-step MLP-Mixer inference and a latent drifting loss that encourages proactive maneuvers.
-
Synergistic Simplex: Cooperative Runtime Assurance for Safety-Critical Autonomous Systems
Synergistic Simplex enables bidirectional cooperation between ML components and safety monitors in autonomous systems, preserving safety guarantees while improving performance.