Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Proposes Behavioral Topology (BeTop), a braid-theory-derived explicit topological supervision of multi-agent future interactions, and BeTopNet, a Transformer that jointly predicts and plans with topology-guided local attention and imitative contingency learning, reporting SOTA on nuPlan and WOMD.
Problem
Integrated prediction and planning (IPP) for autonomous driving suffers from unstable and inconsistent collective multi-agent behavior under scene uncertainty and heterogeneous interactions, as dense and sparse representations are inefficient and inconsistent.
Approach
Formulate an explicit edge topology over agents' future trajectories using braid theory (intertwine indicators in lateral coordinates) as supervision, reason it jointly with trajectories in a synergistic decoder, use the predicted topology to guide local cross-attention, and plan with contingency branches conditioned on joint predictions.
Key results
- On nuPlan Test14-Hard, BeTopNet reaches average planning score 0.766 versus 0.725 for PlanTF (+7.9% per paper's claim relative to prior learning-based SOTA).
- On WOMD marginal prediction test split, BeTopNet achieves 0.4566 mAP and 0.4678 Soft mAP, the best in the table.
- On WOMD joint prediction test split, BeTopNet achieves 0.2412 mAP, improving over MTR++ at 0.2326.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use BeTopNet instead of the strongest listed learning-based planner | nuPlan Test14 Hard planning · Test14 Hard | Avg. planning score: +7.9% | Helped |
| use BeTopNet instead of previous methods | interactive planning · Test14 Inter | PDMScore: +3.8% planning score | Helped |
| use BeTopNet instead of existing predictors | WOMD joint prediction · WOMD joint prediction | mAP: +4.1% | Helped |
| add BeTop to the base planner | PDM · nuPlan | Avg. planning score: +2.0% | Helped |
| add BeTop to the base predictor | MTR · WOMD | mAP: +1.1% | Helped |
| add BeTop to the base predictor | EDA · WOMD | mAP: +2.4% | Helped |
| add contingency cost learning (ablation) | BeTopNet · nuPlan | CLS: -2.9% CLS | Helped |
| add contingency branching (ablation) | BeTopNet · nuPlan | CLS-NR: -1% CLS-NR | Helped |
| add topology-guided local attention (ablation) | BeTopNet · nuPlan | CLS-NR: -2.8% CLS-NR | Helped |
| increase the number of interactive agents used by local attention up to the peak at K=32 (ablation) | BeTopNet · WOMD prediction | mAP: +3.7% | Helped |
| increase the number of selected interactive agents beyond K=32 (ablation) | BeTopNet · WOMD prediction | mAP: -1.8% | Hurt |
Limitations
- Considers only one-step future topology
- Focuses on prediction and planning without perception integration
Experimental setup
Datasets: nuPlan Waymo Open Motion Dataset (WOMD)
Benchmarks: nuPlan Test14-Hard nuPlan Test14-Random Test14-Inter WOMD marginal leaderboard WOMD joint leaderboard
Metrics: OLS CLS-NR CLS PDMScore mAP Soft mAP minADE minFDE Miss Rate
Baselines: PDM-Closed PDM-Hybrid GameFormer UrbanDriver PlanTF PLUTO MTR MTR++ EDA AMP
Builds on: PlanTF MTR PDM braid theory
Closest papers by meaning
- IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning (ICLR 2025 · similarity 0.86)
- Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving (NeurIPS 2026 · similarity 0.85)
- Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modeling (NeurIPS 2025 · similarity 0.85)
- DriveAgent-R1: Advancing VLM-based Autonomous Driving with Active Perception and Hybrid Thinking (ICLR 2026 · similarity 0.85)
- BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction (NeurIPS 2024 · similarity 0.85)
- Density-Adaptive Model Based on Motif Matrix for Multi-Agent Trajectory Prediction (CVPR 2024 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.