# High-Order Evolving Graphs for Enhanced Representation of Traffic Dynamics

arXiv: https://arxiv.org/abs/2409.11206 · HTML: https://mlsanity.com/paper/2409.11206/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 21 of 21)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification](https://mlsanity.com/paper/1712.04851/) (2017)
- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [STM: SpatioTemporal and Motion Encoding for Action Recognition](https://mlsanity.com/paper/1908.02486/) (2019)
- [ROAD: The ROad event Awareness Dataset for Autonomous Driving](https://mlsanity.com/paper/2102.11585/) (2021)
- [Large-scale Robustness Analysis of Video Action Recognition Models](https://mlsanity.com/paper/2207.01398/) (2022)
- [Discovering Spatio-Temporal Rationales for Video Question Answering](https://mlsanity.com/paper/2307.12058/) (2023)
- [Learning and Aggregating Lane Graphs for Urban Automated Driving](https://mlsanity.com/paper/2302.06175/) (2023)
- [SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2301.03512/) (2023)
- [Interaction-Based Trajectory Prediction Over a Hybrid Traffic Graph](https://mlsanity.com/paper/2009.12916/) (2020)
- [Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/1909.13582/) (2019)
- [SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition](https://mlsanity.com/paper/2303.12149/) (2023)
- [nuScenes Knowledge Graph -- A comprehensive semantic representation of traffic scenes for trajectory prediction](https://mlsanity.com/paper/2312.09676/) (2023)
- [Dynamic Traffic Scene Classification with Space-Time Coherence](https://mlsanity.com/paper/1905.12708/) (2019)
- [Sensing accident-prone features in urban scenes for proactive driving and accident prevention](https://mlsanity.com/paper/2202.12788/) (2022)
- [Defining Traffic States using Spatio-temporal Traffic Graphs](https://mlsanity.com/paper/2008.00827/) (2020)

## Abstract (arXiv)

We present an innovative framework for traffic dynamics analysis using High-Order Evolving Graphs, designed to improve spatio-temporal representations in autonomous driving contexts. Our approach constructs temporal bidirectional bipartite graphs that effectively model the complex interactions within traffic scenes in real-time. By integrating Graph Neural Networks (GNNs) with high-order multi-aggregation strategies, we significantly enhance the modeling of traffic scene dynamics, providing a more accurate and detailed analysis of these interactions. Additionally, we incorporate inductive learning techniques inspired by the GraphSAGE framework, enabling our model to adapt to new and unseen traffic scenarios without the need for retraining, thus ensuring robust generalization. Through extensive experiments on the ROAD and ROAD Waymo datasets, we establish a comprehensive baseline for further developments, demonstrating the potential of our method in accurately capturing traffic behavior. Our results emphasize the value of high-order statistical moments and feature-gated attention mechanisms in improving traffic behavior analysis, laying the groundwork for advancing autonomous driving technologies. Our source code is available at: https://github.com/Addy-1998/High_Order_Graphs