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Centaur: Robust End-to-End Autonomous Driving with Test-Time Training
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How can we rely on an end-to-end autonomous vehicle's complex decision-making system during deployment? One common solution is to have a ``fallback layer'' that checks the planned trajectory for rule violations and replaces it with a pre-defined safe action if necessary. Another approach involves adjusting the planner's decisions to minimize a pre-defined ``cost function'' using additional system predictions such as road layouts and detected obstacles. However, these pre-programmed rules or cost functions cannot learn and improve with new training data, often resulting in overly conservative behaviors. In this work, we propose Centaur (Cluster Entropy for Test-time trAining using Uncertainty) which updates a planner's behavior via test-time training, without relying on hand-engineered rules or cost functions. Instead, we measure and minimize the uncertainty in the planner's decisions. For this, we develop a novel uncertainty measure, called Cluster Entropy, which is simple, interpretable, and compatible with state-of-the-art planning algorithms. Using data collected at prior test-time time-steps, we perform an update to the model's parameters using a gradient that minimizes the Cluster Entropy. With only this sole gradient update prior to inference, Centaur exhibits significant improvements, ranking first on the navtest leaderboard with notable gains in safety-critical metrics such as time to collision. To provide detailed insights on a per-scenario basis, we also introduce navsafe, a challenging new benchmark, which highlights previously undiscovered failure modes of driving models.
Forward citations
Cited by 7 Pith papers
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Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning
TTCov curates training data for deployment by building an LLM-generated atomic-proposition atlas of the test distribution and greedily selecting clips that match it.
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Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer
A reward-only offline RL method for trajectory planning in end-to-end autonomous driving achieves state-of-the-art on Navhard and competitive closed-loop HUGSIM performance without imitation learning.
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Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
R2SE refines pretrained end-to-end driving policies on hard cases via residual LoRA reinforcement learning and switches between specialist and generalist policies using GPD-based uncertainty.
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ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models
An asynchronous dual-system architecture forecasts large-model features into the current frame and adds a small-model update to drive in near real time, scoring 69.53 on Bench2Drive at 50 ms.
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HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving
Hierarchical diffusion plus polar structure-preserving expansion and metric-decoupled RL yields SOTA open- and closed-loop planning scores on NAVSIM and HUGSIM.
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Generalized Trajectory Scoring for End-to-end Multimodal Planning
GTRS combines super-dense vocabulary training, dropout, sensor augmentation, and diffusion proposals to reach 49.4 EPDMS on the Navhard benchmark, approaching the privileged PDM-Closed method.
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HMAD: Advancing E2E Driving with Anchored Offset Proposals and Simulation-Supervised Multi-target Scoring
HMAD integrates BEVFormer, DiffusionDrive-style anchor offsets, and a Hydra-MDP-style scoring network to achieve 65.94 EPDMS on the NAVSIM warmup benchmark and 44.5% on the CVPR 2025 private test set.
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