# QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction

arXiv: https://arxiv.org/abs/2306.10508 · HTML: https://mlsanity.com/paper/2306.10508/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 18 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [Scene Transformer: A unified architecture for predicting multiple agent trajectories](https://mlsanity.com/paper/2106.08417/) (2021)

## Built on by (most cited first, 30 of 31)

- [AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning](https://mlsanity.com/paper/2506.13757/) (2025)
- [SMART: Scalable Multi-agent Real-time Motion Generation via Next-token Prediction](https://mlsanity.com/paper/2405.15677/) (2024)
- [Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future](https://mlsanity.com/paper/2312.03408/) (2023)
- [Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation](https://mlsanity.com/paper/2408.00766/) (2024)
- [KiGRAS: Kinematic-Driven Generative Model for Realistic Agent Simulation](https://mlsanity.com/paper/2407.12940/) (2024)
- [Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Driving](https://mlsanity.com/paper/2502.16589/) (2025)
- [Advancing Multi-agent Traffic Simulation via R1-Style Reinforcement Fine-Tuning](https://mlsanity.com/paper/2509.23993/) (2025)
- [AutoDrive-P3: Unified Chain of Perception-Prediction-Planning Thought via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2603.28116/) (2026)
- [AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving](https://mlsanity.com/paper/2403.13331/) (2024)
- [DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modeling](https://mlsanity.com/paper/2503.15029/) (2025)
- [Motion Forecasting for Autonomous Vehicles: A Survey](https://mlsanity.com/paper/2502.08664/) (2025)
- [JointMotion: Joint Self-Supervision for Joint Motion Prediction](https://mlsanity.com/paper/2403.05489/) (2024)
- [MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems](https://mlsanity.com/paper/2605.10904/) (2026)
- [CooperRisk: A Driving Risk Quantification Pipeline with Multi-Agent Cooperative Perception and Prediction](https://mlsanity.com/paper/2506.15868/) (2025)
- [Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving](https://mlsanity.com/paper/2509.02754/) (2025)
- [RetroMotion: Retrocausal Motion Forecasting Models are Instructable](https://mlsanity.com/paper/2505.20414/) (2025)
- [Autoregressive Meta-Actions for Unified Controllable Trajectory Generation](https://mlsanity.com/paper/2505.23612/) (2025)
- [SCORP: Scene-Consistent Multi-agent Diffusion Planning with Stable Online Reinforcement Post-Training for Cooperative Driving](https://mlsanity.com/paper/2604.11734/) (2026)
- [TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles](https://mlsanity.com/paper/2408.15538/) (2024)
- [EP-Diffuser: An Efficient Diffusion Model for Traffic Scene Generation and Prediction via Polynomial Representations](https://mlsanity.com/paper/2504.05422/) (2025)
- [DeMo++: Motion Decoupling for Autonomous Driving](https://mlsanity.com/paper/2507.17342/) (2025)
- [Streaming Real-Time Trajectory Prediction Using Endpoint-Aware Modeling](https://mlsanity.com/paper/2603.01864/) (2026)
- [Teaching Vision-Language-Action Models What to See and Where to Look](https://mlsanity.com/paper/2607.01658/) (2026)
- [Improving Consistency in Vehicle Trajectory Prediction Through Preference Optimization](https://mlsanity.com/paper/2507.02406/) (2025)
- [ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture](https://mlsanity.com/paper/2507.06531/) (2025)
- [V2X-RECT: An Efficient V2X Trajectory Prediction Framework via Redundant Interaction Filtering and Tracking Error Correction](https://mlsanity.com/paper/2511.17941/) (2025)
- [Future-Interactions-Aware Trajectory Prediction via Braid Theory](https://mlsanity.com/paper/2603.22035/) (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)
- [ECTraj: Enhanced Consistency Training for Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2605.08572/) (2026)

## Abstract (arXiv)

Estimating the joint distribution of on-road agents' future trajectories is essential for autonomous driving. In this technical report, we propose a next-generation framework for joint multi-agent trajectory prediction called QCNeXt. First, we adopt the query-centric encoding paradigm for the task of joint multi-agent trajectory prediction. Powered by this encoding scheme, our scene encoder is equipped with permutation equivariance on the set elements, roto-translation invariance in the space dimension, and translation invariance in the time dimension. These invariance properties not only enable accurate multi-agent forecasting fundamentally but also empower the encoder with the capability of streaming processing. Second, we propose a multi-agent DETR-like decoder, which facilitates joint multi-agent trajectory prediction by modeling agents' interactions at future time steps. For the first time, we show that a joint prediction model can outperform marginal prediction models even on the marginal metrics, which opens up new research opportunities in trajectory prediction. Our approach ranks 1st on the Argoverse 2 multi-agent motion forecasting benchmark, winning the championship of the Argoverse Challenge at the CVPR 2023 Workshop on Autonomous Driving.