# Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving

arXiv: https://arxiv.org/abs/2409.18031 · HTML: https://mlsanity.com/paper/2409.18031/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2024.

## Built on by (most cited first, 27 of 27)

- [Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modeling](https://mlsanity.com/paper/2510.11083/) (2025)
- [Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling](https://mlsanity.com/paper/2505.17659/) (2025)
- [Multi-modal Knowledge Distillation-based Human Trajectory Forecasting](https://mlsanity.com/paper/2503.22201/) (2025)
- [SEPT: Standard-Definition Map Enhanced Scene Perception and Topology Reasoning for Autonomous Driving](https://mlsanity.com/paper/2505.12246/) (2025)
- [TrajFlow: Multi-modal Motion Prediction via Flow Matching](https://mlsanity.com/paper/2506.08541/) (2025)
- [Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectives](https://mlsanity.com/paper/2505.09074/) (2025)
- [Hierarchical Multi-Agent Reinforcement Learning with Control Barrier Functions for Safety-Critical Autonomous Systems](https://mlsanity.com/paper/2507.14850/) (2025)
- [CoPlanner: An Interactive Motion Planner with Contingency-Aware Diffusion for Autonomous Driving](https://mlsanity.com/paper/2509.17080/) (2025)
- [MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving](https://mlsanity.com/paper/2512.03795/) (2025)
- [RetroMotion: Retrocausal Motion Forecasting Models are Instructable](https://mlsanity.com/paper/2505.20414/) (2025)
- [From Static to Dynamic: a Survey of Topology-Aware Perception in Autonomous Driving](https://mlsanity.com/paper/2509.23641/) (2025)
- [Recover to Predict: Progressive Retrospective Learning for Variable-Length Trajectory Prediction](https://mlsanity.com/paper/2603.10597/) (2026)
- [AGI-Elo: How Far Are We From Mastering A Task?](https://mlsanity.com/paper/2505.12844/) (2025)
- [Improving Consistency in Vehicle Trajectory Prediction Through Preference Optimization](https://mlsanity.com/paper/2507.02406/) (2025)
- [HeLoFusion: An Efficient and Scalable Encoder for Modeling Heterogeneous and Multi-Scale Interactions in Trajectory Prediction](https://mlsanity.com/paper/2509.11719/) (2025)
- [PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agents](https://mlsanity.com/paper/2512.14735/) (2025)
- [TSC: Topology-Conditioned Stackelberg Coordination for Multi-Agent Reinforcement Learning in Interactive Driving](https://mlsanity.com/paper/2602.23896/) (2026)
- [CarPLAN: Context-Adaptive and Robust Planning with Dynamic Scene Awareness for Autonomous Driving](https://mlsanity.com/paper/2603.12607/) (2026)
- [Future-Interactions-Aware Trajectory Prediction via Braid Theory](https://mlsanity.com/paper/2603.22035/) (2026)
- [A Semantic Observer Layer for Autonomous Vehicles: Pre-Deployment Feasibility Study of VLMs for Low-Latency Anomaly Detection](https://mlsanity.com/paper/2603.28888/) (2026)
- [Super Agents and Confounders: Influence of surrounding agents on vehicle trajectory prediction](https://mlsanity.com/paper/2604.03463/) (2026)
- [Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic](https://mlsanity.com/paper/2604.12857/) (2026)
- [FlowS: One-Step Motion Prediction via Local Transport Conditioning](https://mlsanity.com/paper/2604.26065/) (2026)
- [Mode-as-Sequence: Translating Multimodal Motion Prediction into Unified Sequential Mode Modeling](https://mlsanity.com/paper/2605.24037/) (2026)
- [Agent-driven Long-tail Simulation for Autonomous Driving](https://mlsanity.com/paper/2607.04331/) (2026)
- [VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation](https://mlsanity.com/paper/2609.02462/) (2026)
- [Escaping Local Views: Discovering Latent Concepts for Interpretable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2609.34459/) (2026)

## Abstract (arXiv)

Autonomous driving system aims for safe and social-consistent driving through the behavioral integration among interactive agents. However, challenges remain due to multi-agent scene uncertainty and heterogeneous interaction. Current dense and sparse behavioral representations struggle with inefficiency and inconsistency in multi-agent modeling, leading to instability of collective behavioral patterns when integrating prediction and planning (IPP). To address this, we initiate a topological formation that serves as a compliant behavioral foreground to guide downstream trajectory generations. Specifically, we introduce Behavioral Topology (BeTop), a pivotal topological formulation that explicitly represents the consensual behavioral pattern among multi-agent future. BeTop is derived from braid theory to distill compliant interactive topology from multi-agent future trajectories. A synergistic learning framework (BeTopNet) supervised by BeTop facilitates the consistency of behavior prediction and planning within the predicted topology priors. Through imitative contingency learning, BeTop also effectively manages behavioral uncertainty for prediction and planning. Extensive verification on large-scale real-world datasets, including nuPlan and WOMD, demonstrates that BeTop achieves state-of-the-art performance in both prediction and planning tasks. Further validations on the proposed interactive scenario benchmark showcase planning compliance in interactive cases.