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

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arxiv 2306.10508 v1 pith:HMO5WPWP submitted 2023-06-18 cs.CV cs.RO

Zikang Zhou , Zihao Wen , Jianping Wang , Yung-Hui Li , Yu-Kai Huang This is my paper · ORCID
classification cs.CVcs.RO
keywords multi-agentpredictionjointtrajectoryinvariancetimeagentsargoverse
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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.

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Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Long-term Traffic Scene Prediction via Polynomial Representations in Autonomous Driving

    cs.AI 2026-08 conditional novelty 6.0 of 10

    Polynomial representations of trajectories and maps yield competitive prediction accuracy while substantially improving cross-dataset generalization and computational efficiency in autonomous driving.

  2. Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving

    cs.AI 2025-09 conditional novelty 6.0 of 10

    On Waymo Sim Agents, LLM-style tokenization, positional embeddings, pretraining, RL post-training, and test-time search can be adapted to improve motion generation, but not all transfer without domain-specific changes.

  3. ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture

    cs.CV 2025-07 conditional novelty 6.0 of 10

    ILNet reports top INTERACTION joint metrics and strong Argoverse marginal metrics using inverse temporal attention plus learned dynamic anchor refinement.

  4. Improving Consistency in Vehicle Trajectory Prediction Through Preference Optimization

    cs.LG 2025-07 conditional novelty 6.0 of 10

    Adapting SimPO preference optimization for multi-agent trajectory prediction lowers collision rates on Argoverse 2, Interaction, and Waymo Open Motion Dataset with minimal accuracy loss.

  5. Autoregressive Meta-Actions for Unified Controllable Trajectory Generation

    cs.RO 2025-05 conditional novelty 6.0 of 10

    Frame-level meta-actions, predicted and injected at every time step in an autoregressive trajectory model, improve alignment between high-level driving decisions and generated motion.

  6. IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction

    cs.RO 2026-07 conditional novelty 5.0 of 10

    Mode-world weighted regression and an iterative decoder yield state-of-the-art multi-agent trajectory forecasts on Argoverse 2 by reducing mode collapse while raising ranking and top-1 confidence.

  7. DeMo++: Motion Decoupling for Autonomous Driving

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    A decoupled mode/state query representation with hybrid Attention+Mamba and cross-scene interaction achieves top results on Argoverse 2, nuScenes, nuPlan, and NAVSIM, but the Argoverse 2 and nuPlan evaluations use a r...

  8. CooperRisk: A Driving Risk Quantification Pipeline with Multi-Agent Cooperative Perception and Prediction

    cs.RO 2025-06 reject novelty 5.0 of 10

    CooperRisk couples V2X cooperative perception with scene-consistent multi-agent trajectory prediction to produce temporal risk maps that reduce planned conflicts by about half on the V2XPnP dataset.

  9. JAM: Keypoint-Guided Joint Prediction after Classification-Aware Marginal Proposal for Multi-Agent Interaction

    cs.RO 2025-07 conditional novelty 4.0 of 10

    A two-stage joint trajectory prediction model that uses trajectory-type classification in a marginal proposal stage and keypoint-guided joint refinement beats prior methods on Waymo interaction metrics for position error.

  10. What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies

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    A taxonomy-driven survey of vision benchmarks for road-safety relevant scene elements, covering 78 datasets and 40 tasks.

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