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DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving
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DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving
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End-to-end autonomous driving (E2E-AD) has rapidly emerged as a promising approach toward achieving full autonomy. However, existing E2E-AD systems typically adopt a traditional multi-task framework, addressing perception, prediction, and planning tasks through separate task-specific heads. Despite being trained in a fully differentiable manner, they still encounter issues with task coordination, and the system complexity remains high. In this work, we introduce DiffAD, a novel diffusion probabilistic model that redefines autonomous driving as a conditional image generation task. By rasterizing heterogeneous targets onto a unified bird's-eye view (BEV) and modeling their latent distribution, DiffAD unifies various driving objectives and jointly optimizes all driving tasks in a single framework, significantly reducing system complexity and harmonizing task coordination. The reverse process iteratively refines the generated BEV image, resulting in more robust and realistic driving behaviors. Closed-loop evaluations in Carla demonstrate the superiority of the proposed method, achieving a new state-of-the-art Success Rate and Driving Score.
Forward citations
Cited by 12 Pith papers
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ScenarioControl: Vision-Language Controllable Vectorized Latent Scenario Generation
ScenarioControl introduces the first vision-language controllable generator for realistic vectorized 3D driving scenarios with temporal consistency across actor views.
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Deconfounded Lifelong Learning for Autonomous Driving via Dynamic Knowledge Spaces
DeLL combines DPMM dual knowledge spaces with front-door causal adjustment and a non-autoregressive evolutionary decoder to reduce catastrophic forgetting and spurious correlations in lifelong end-to-end autonomous driving.
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MOJITO: Modal Joint Learning for Unified End-to-End Autonomous Driving
Block-wise Modal Joint Attention over image, LiDAR, and diffusion action tokens yields 88.9 PDMS / 88.4 EPDMS on NAVSIM without anchors or auxiliary supervision.
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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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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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LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving
LMGenDrive unifies LLM-based multimodal understanding with generative world models to output both future driving videos and control signals for end-to-end closed-loop autonomous driving.
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AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
Conditioning speed planning on the predicted path and relabeling synthetic cut-ins yields SOTA Bench2Drive scores (DS 89.07, SR 73.18%).
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AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
A cascaded end-to-end driving model conditions longitudinal planning on the lateral path via anchor-based regression and path-conditioned 1D displacement prediction, achieving SOTA driving score of 89.07 and 73.18% su...
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DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving
DriveMoE applies scene-specialized Vision MoE and skill-specialized Action MoE to a VLA baseline to achieve SOTA closed-loop performance on Bench2Drive.
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MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learning
An autonomous-driving vision-language model that uses online RL over discrete language actions, with a separate action expert mapping decisions to trajectories, reports DS 78.04 and SR 55.09% on Bench2Drive.
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DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving
DIVER uses RL-guided diffusion to produce diverse feasible trajectories from one ground-truth path, addressing mode collapse in imitation learning for autonomous driving.
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OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model
OWMDrive combines multi-step 3D occupancy forecasting with diffusion planning to produce more foresighted trajectories in autonomous driving.
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