UniUncer is a plug-and-play uncertainty framework that jointly models static and dynamic scene uncertainty inside end-to-end planners, cutting L2 trajectory error 7% on nuScenes and raising EPDMS 10.8% on NavsimV2.
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Hydra-mdp++: Advancing end-to-end driving via expert-guided hydra-distillation
Baseline reference. 67% of citing Pith papers use this work as a benchmark or comparison.
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Pith papers citing it
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67% of classified citations
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representative citing papers
DriveTeach-VLA adds Driving-aware Vision Distillation pretraining and 2D Trajectory-Guided Prompts to VLA models, then reports state-of-the-art results on NAVSIM and nuScenes.
Derail adversarial perturbations hijack the scoring head in generative E2E driving planners, flipping safe to unsafe trajectory selection with 39-80% score drops and up to 50% collision rates.
LWDrive uses future-frame supervision on VLMs to create world-model features that a multi-layer Foresight Cascade Planner refines into final trajectories, reporting 92.0 on NAVSIM and 89.6 on NAVSIM-v2.
UniTeD unifies perception and planning in autonomous driving via shared temporal diffusion with TTM and ARS modules, reporting SOTA results on benchmarks.
FlowR2A learns reward-conditioned action distributions via flow-matching decoder to unify dense reward supervision with dynamic proposal generation for multimodal driving planning.
D³-MoE disentangles style and physical axes with diffusion and self-supervised MoE experts to produce style-controllable trajectories, reporting SOTA 88.2 PDMS on NAVSIM.
IDOL uses inverse dynamics on adjacent predicted latent futures to extract planning-relevant motion deltas, then optimizes trajectories with a closed-loop refinement step, reporting SOTA results on NAVSIM v1 and v2.
NTR adds a self-distillation masked latent reconstruction objective that uses only scene tokens to reconstruct masked patch features, improving visual representation quality and planning performance in end-to-end autonomous driving.
ChainFlow-VLA unifies autoregressive causal trajectory modes with VLM-conditioned diffusion refinement to reach 94.85 on NAVSIM v1, matching human performance.
DAWN couples a world predictor with a world-conditioned action denoiser in latent space so that each refines the other recursively, yielding strong planning and safety results on autonomous driving benchmarks.
The primary OL-CL gap in end-to-end autonomous driving arises from objective mismatch creating structural inability to model reactive behaviors, which a test-time adaptation method can mitigate.
Orion-Lite uses latent feature distillation and trajectory supervision to create a vision-only model that surpasses its LLM-based teacher on closed-loop Bench2Drive evaluation, achieving a new SOTA driving score of 80.6.
DVGT-2 is a streaming vision-geometry-action model that jointly reconstructs dense 3D geometry and plans trajectories online, achieving better reconstruction than prior batch methods while transferring directly to planning benchmarks without fine-tuning.
SimScale synthesizes unseen driving states from real logs via neural rendering and reactive environments, generates pseudo-expert trajectories, and shows that co-training on real plus simulated data improves planning robustness and generalization on real benchmarks, with gains scaling by simulation
PriorEye augments end-to-end driving models with a dual-memory architecture that stores and gates geospatial visual priors to improve performance and robustness to sensor corruption on NAVSIM-v2.
A representation- and geometry-guided discrete tokenizer for driving scenes improves token quality for world models and planning on NAVSIM.
CoPhy is a new RL framework that distills VLM cognition into BEV encoders, adds an auto-regressive BEV world model for action-conditioned future prediction, and optimizes policies via GRPO with dual physical-cognitive rewards, claiming SOTA on NAVSIM v1/v2.
CaAD adds ego-centric joint-causal modeling and causality-aware policy alignment to end-to-end driving, reporting Driving Score 87.53 and PDMS 91.1 on Bench2Drive and NAVSIM.
RAD-2 uses a diffusion generator and RL discriminator to cut collision rates by 56% in closed-loop autonomous driving planning.
Dense future RGB/depth world modeling both supervises a VLA planner and supplies safety-gated uncertainty rewards that, optimized with GRPO, reach 93.7 PDMS on NAVSIM.
EvoDriveVLA uses collaborative perception-planning distillation with self-anchor and future-aware teachers to fix perception degradation and long-term instability in driving VLA models, reaching SOTA on nuScenes and NAVSIM.
IRR-Drive adds an adaptive multimodal reflection step (text intention plus predicted future BEV) that lets a VLA model self-correct its trajectory plan according to scene complexity and reports SOTA on NAVSIM.
StandardE2E standardizes preprocessing and data loading for six driving datasets under one shared schema and PyTorch interface.
citing papers explorer
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UniUncer: Unified Dynamic Static Uncertainty for End to End Driving
UniUncer is a plug-and-play uncertainty framework that jointly models static and dynamic scene uncertainty inside end-to-end planners, cutting L2 trajectory error 7% on nuScenes and raising EPDMS 10.8% on NavsimV2.
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Teaching Vision-Language-Action Models What to See and Where to Look
DriveTeach-VLA adds Driving-aware Vision Distillation pretraining and 2D Trajectory-Guided Prompts to VLA models, then reports state-of-the-art results on NAVSIM and nuScenes.
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Off the Rails: Hijacking the Scoring Head in Generative End-to-End Driving Planners with Safety-Violating Adversarial Perturbations
Derail adversarial perturbations hijack the scoring head in generative E2E driving planners, flipping safe to unsafe trajectory selection with 39-80% score drops and up to 50% collision rates.
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LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving
LWDrive uses future-frame supervision on VLMs to create world-model features that a multi-layer Foresight Cascade Planner refines into final trajectories, reporting 92.0 on NAVSIM and 89.6 on NAVSIM-v2.
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UniTeD: Unified Temporal Diffusion for Joint Perception and Planning in Autonomous Driving
UniTeD unifies perception and planning in autonomous driving via shared temporal diffusion with TTM and ARS modules, reporting SOTA results on benchmarks.
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FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning
FlowR2A learns reward-conditioned action distributions via flow-matching decoder to unify dense reward supervision with dynamic proposal generation for multimodal driving planning.
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D$^3$-MoE:Dual Disentangled Diffusion Mixture-of-Experts for Style-Controllable End-to-End Autonomous Driving
D³-MoE disentangles style and physical axes with diffusion and self-supervised MoE experts to produce style-controllable trajectories, reporting SOTA 88.2 PDMS on NAVSIM.
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IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving
IDOL uses inverse dynamics on adjacent predicted latent futures to extract planning-relevant motion deltas, then optimizes trajectories with a closed-loop refinement step, reporting SOTA results on NAVSIM v1 and v2.
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NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Driving
NTR adds a self-distillation masked latent reconstruction objective that uses only scene tokens to reconstruct masked patch features, improving visual representation quality and planning performance in end-to-end autonomous driving.
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ChainFlow-VLA: Causal Flow Planning with Vision-Language Models
ChainFlow-VLA unifies autoregressive causal trajectory modes with VLM-conditioned diffusion refinement to reach 94.85 on NAVSIM v1, matching human performance.
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The DAWN of World-Action Interactive Models
DAWN couples a world predictor with a world-conditioned action denoiser in latent space so that each refines the other recursively, yielding strong planning and safety results on autonomous driving benchmarks.
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BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving
The primary OL-CL gap in end-to-end autonomous driving arises from objective mismatch creating structural inability to model reactive behaviors, which a test-time adaptation method can mitigate.
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Orion-Lite: Distilling LLM Reasoning into Efficient Vision-Only Driving Models
Orion-Lite uses latent feature distillation and trajectory supervision to create a vision-only model that surpasses its LLM-based teacher on closed-loop Bench2Drive evaluation, achieving a new SOTA driving score of 80.6.
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DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scale
DVGT-2 is a streaming vision-geometry-action model that jointly reconstructs dense 3D geometry and plans trajectories online, achieving better reconstruction than prior batch methods while transferring directly to planning benchmarks without fine-tuning.
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SimScale: Learning to Drive via Real-World Simulation at Scale
SimScale synthesizes unseen driving states from real logs via neural rendering and reactive environments, generates pseudo-expert trajectories, and shows that co-training on real plus simulated data improves planning robustness and generalization on real benchmarks, with gains scaling by simulation
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PriorEye: Geospatial Visual Priors for End-to-End Autonomous Driving
PriorEye augments end-to-end driving models with a dual-memory architecture that stores and gates geospatial visual priors to improve performance and robustness to sensor corruption on NAVSIM-v2.
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Unified Driving Tokens: Representation- and Geometry-Guided Discrete Tokenizer for Driving World Models and Planning
A representation- and geometry-guided discrete tokenizer for driving scenes improves token quality for world models and planning on NAVSIM.
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Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving
CoPhy is a new RL framework that distills VLM cognition into BEV encoders, adds an auto-regressive BEV world model for action-conditioned future prediction, and optimizes policies via GRPO with dual physical-cognitive rewards, claiming SOTA on NAVSIM v1/v2.
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Causality-Aware End-to-End Autonomous Driving via Ego-Centric Joint Scene Modeling
CaAD adds ego-centric joint-causal modeling and causality-aware policy alignment to end-to-end driving, reporting Driving Score 87.53 and PDMS 91.1 on Bench2Drive and NAVSIM.
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RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework
RAD-2 uses a diffusion generator and RL discriminator to cut collision rates by 56% in closed-loop autonomous driving planning.
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ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving
Dense future RGB/depth world modeling both supervises a VLA planner and supplies safety-gated uncertainty rewards that, optimized with GRPO, reach 93.7 PDMS on NAVSIM.
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EvoDriveVLA: Evolving Driving VLA Models via Collaborative Perception-Planning Distillation
EvoDriveVLA uses collaborative perception-planning distillation with self-anchor and future-aware teachers to fix perception degradation and long-term instability in driving VLA models, reaching SOTA on nuScenes and NAVSIM.
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Intend, Reflect, Refine: An Adaptive Multimodal Reflection Framework for Autonomous Driving
IRR-Drive adds an adaptive multimodal reflection step (text intention plus predicted future BEV) that lets a VLA model self-correct its trajectory plan according to scene complexity and reports SOTA on NAVSIM.
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StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets
StandardE2E standardizes preprocessing and data loading for six driving datasets under one shared schema and PyTorch interface.