# GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving

arXiv: https://arxiv.org/abs/2403.19098 · HTML: https://mlsanity.com/paper/2403.19098/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in Generative simulation for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 39 of its 48 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Placed in

- End-to-end autonomous driving / [Generative simulation for driving](https://mlsanity.com/driving/generative-simulation-for-driving/)

## Builds on (most cited first, 30 of 39)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://mlsanity.com/paper/2112.11790/) (2021)
- [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023)
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://mlsanity.com/paper/2206.10092/) (2022)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://mlsanity.com/paper/2104.09224/) (2021)
- [ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning](https://mlsanity.com/paper/2207.07601/) (2022)
- [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023)
- [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://mlsanity.com/paper/2003.01251/) (2020)
- [MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2208.14437/) (2022)
- [Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline](https://mlsanity.com/paper/2206.08129/) (2022)
- [End-to-end Interpretable Neural Motion Planner](https://mlsanity.com/paper/2101.06679/) (2021)
- [FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras](https://mlsanity.com/paper/2104.10490/) (2021)
- [MP3: A Unified Model to Map, Perceive, Predict and Plan](https://mlsanity.com/paper/2101.06806/) (2021)
- [Scene as Occupancy](https://mlsanity.com/paper/2306.02851/) (2023)
- [End-to-End Urban Driving by Imitating a Reinforcement Learning Coach](https://mlsanity.com/paper/2108.08265/) (2021)
- [Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving](https://mlsanity.com/paper/2305.06242/) (2023)
- [A Language Agent for Autonomous Driving](https://mlsanity.com/paper/2311.10813/) (2023)
- [PnPNet: End-to-End Perception and Prediction with Tracking in the Loop](https://mlsanity.com/paper/2005.14711/) (2020)
- [Learning from All Vehicles](https://mlsanity.com/paper/2203.11934/) (2022)

## Built on by (most cited first, 25 of 25)

- [SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation](https://mlsanity.com/paper/2405.19620/) (2024)
- [VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision](https://mlsanity.com/paper/2412.14446/) (2024)
- [Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving](https://mlsanity.com/paper/2503.03125/) (2025)
- [A Survey on Vision-Language-Action Models for Autonomous Driving](https://mlsanity.com/paper/2506.24044/) (2025)
- [Driving in the Occupancy World: Vision-Centric 4D Occupancy Forecasting and Planning via World Models for Autonomous Driving](https://mlsanity.com/paper/2408.14197/) (2024)
- [iPad: Iterative Proposal-centric End-to-End Autonomous Driving](https://mlsanity.com/paper/2505.15111/) (2025)
- [Universal Scene Graph Generation](https://mlsanity.com/paper/2503.15005/) (2025)
- [MomaGraph: State-Aware Unified Scene Graphs with Vision-Language Model for Embodied Task Planning](https://mlsanity.com/paper/2512.16909/) (2025)
- [DriveMamba: Task-Centric Scalable State Space Model for Efficient End-to-End Autonomous Driving](https://mlsanity.com/paper/2602.13301/) (2026)
- [GraphPilot: Grounded Scene Graph Conditioning for Language-Based Autonomous Driving](https://mlsanity.com/paper/2511.11266/) (2025)
- [FocalAD: Local Motion Planning for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.11419/) (2025)
- [VR-Drive: Viewpoint-Robust End-to-End Driving with Feed-Forward 3D Gaussian Splatting](https://mlsanity.com/paper/2510.23205/) (2025)
- [LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation](https://mlsanity.com/paper/2411.14927/) (2024)
- [EgoFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving](https://mlsanity.com/paper/2409.09777/) (2024)
- [Robo-SGG: Exploiting Layout-Oriented Normalization and Restitution Can Improve Robust Scene Graph Generation](https://mlsanity.com/paper/2504.12606/) (2025)
- [Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back](https://mlsanity.com/paper/2505.18945/) (2025)
- [Vision-Centric 4D Occupancy Forecasting and Planning via Implicit Residual World Models](https://mlsanity.com/paper/2510.16729/) (2025)
- [Bridging Structure and Language: Graph-Based Visual Reasoning for Autonomous Road Understanding](https://mlsanity.com/paper/2605.20942/) (2026)
- [SUPER-AD: Semantic Uncertainty-aware Planning for End-to-End Robust Autonomous Driving](https://mlsanity.com/paper/2511.22865/) (2025)
- [2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model](https://mlsanity.com/paper/2509.02659/) (2025)
- [CURVE: Learning Causality-Inspired Invariant Representations for Robust Scene Understanding via Uncertainty-Guided Regularization](https://mlsanity.com/paper/2601.20355/) (2026)
- [MOSA: Motion-Guided Semantic Alignment for Dynamic Scene Graph Generation](https://mlsanity.com/paper/2604.19631/) (2026)
- [NoPA: Non-Parametric Online 3D Scene Graph Generation](https://mlsanity.com/paper/2607.00529/) (2026)
- [Geometric 2D Scene Graph Generation](https://mlsanity.com/paper/2607.22325/) (2026)
- [VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation](https://mlsanity.com/paper/2609.02462/) (2026)

## Abstract (arXiv)

Modeling complicated interactions among the ego-vehicle, road agents, and map elements has been a crucial part for safety-critical autonomous driving. Previous works on end-to-end autonomous driving rely on the attention mechanism for handling heterogeneous interactions, which fails to capture the geometric priors and is also computationally intensive. In this paper, we propose the Interaction Scene Graph (ISG) as a unified method to model the interactions among the ego-vehicle, road agents, and map elements. With the representation of the ISG, the driving agents aggregate essential information from the most influential elements, including the road agents with potential collisions and the map elements to follow. Since a mass of unnecessary interactions are omitted, the more efficient scene-graph-based framework is able to focus on indispensable connections and leads to better performance. We evaluate the proposed method for end-to-end autonomous driving on the nuScenes dataset. Compared with strong baselines, our method significantly outperforms in the full-stack driving tasks, including perception, prediction, and planning. Code will be released at https://github.com/zhangyp15/GraphAD.