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FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving
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Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving. However, leveraging such data from multiple sensors to jointly optimize the prediction and planning tasks remains largely unexplored. In this paper, we present FusionAD, to the best of our knowledge, the first unified framework that fuse the information from two most critical sensors, camera and LiDAR, goes beyond perception task. Concretely, we first build a transformer based multi-modality fusion network to effectively produce fusion based features. In constrast to camera-based end-to-end method UniAD, we then establish a fusion aided modality-aware prediction and status-aware planning modules, dubbed FMSPnP that take advantages of multi-modality features. We conduct extensive experiments on commonly used benchmark nuScenes dataset, our FusionAD achieves state-of-the-art performance and surpassing baselines on average 15% on perception tasks like detection and tracking, 10% on occupancy prediction accuracy, reducing prediction error from 0.708 to 0.389 in ADE score and reduces the collision rate from 0.31% to only 0.12%.
Forward citations
Cited by 7 Pith papers
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PixelPilot: Scalable Vision-Language-Action Models for End-to-End Autonomous Driving
Decoupling sensor-agnostic 2D trajectory planning from deterministic 3D lifting, plus dense GRPO rewards on perception-to-planning, yields competitive open- and closed-loop driving VLA results.
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To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Software
A taxonomy that classifies unified perception methods in autonomous driving into Early, Late, and Full Unified Perception based on task integration, tracking formulation, and representation flow.
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MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection
A camera-LiDAR 3D detector built around a hybrid local-global Mamba block with height-fidelity LiDAR encoding reports 75.0 NDS on nuScenes validation, outperforming prior transformer-based fusion methods.
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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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LADY: Linear Attention for Autonomous Driving Efficiency without Transformers
LADY shows that an end-to-end driving model using only linear attention can match transformer-based planners on NAVSIM/Bench2Drive while fusing arbitrary-length historical sensor frames at constant per-frame cost.
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LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving
LeAD adds a low-frequency large-language-model planner that takes over when a high-frequency end-to-end driving model gets stuck, and reports improved CARLA benchmark scores.
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A Survey on Vision-Language-Action Models for Autonomous Driving
A survey organizes vision-language-action models for autonomous driving into four stages, compares over 20 systems, and catalogs datasets, benchmarks, and open challenges.
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