# ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals

arXiv: https://arxiv.org/abs/2303.12071 · HTML: https://mlsanity.com/paper/2303.12071/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 32 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [Pay Attention to MLPs](https://mlsanity.com/paper/2105.08050/) (2021)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [Wayformer: Motion Forecasting via Simple & Efficient Attention Networks](https://mlsanity.com/paper/2207.05844/) (2022)
- [CoverNet: Multimodal Behavior Prediction using Trajectory Sets](https://mlsanity.com/paper/1911.10298/) (2019)
- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://mlsanity.com/paper/2111.14973/) (2021)
- [Multimodal Motion Prediction with Stacked Transformers](https://mlsanity.com/paper/2103.11624/) (2021)
- [Multiple Futures Prediction](https://mlsanity.com/paper/1911.00997/) (2019)
- [GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2109.01827/) (2021)
- [LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting](https://mlsanity.com/paper/2101.06653/) (2021)
- [TPCN: Temporal Point Cloud Networks for Motion Forecasting](https://mlsanity.com/paper/2103.03067/) (2021)
- [GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting](https://mlsanity.com/paper/2211.02545/) (2022)
- [Learning to Predict Vehicle Trajectories with Model-based Planning](https://mlsanity.com/paper/2103.04027/) (2021)
- [Exploring Simple 3D Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2108.10312/) (2021)
- [Self-Supervised Pillar Motion Learning for Autonomous Driving](https://mlsanity.com/paper/2104.08683/) (2021)
- [Path-Aware Graph Attention for HD Maps in Motion Prediction](https://mlsanity.com/paper/2202.13772/) (2022)
- [Bootstrap Motion Forecasting With Self-Consistent Constraints](https://mlsanity.com/paper/2204.05859/) (2022)
- [TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction](https://mlsanity.com/paper/2207.00170/) (2022)
- [BANet: Motion Forecasting with Boundary Aware Network](https://mlsanity.com/paper/2206.07934/) (2022)
- [TL-GAN: Improving Traffic Light Recognition via Data Synthesis for Autonomous Driving](https://mlsanity.com/paper/2203.15006/) (2022)

## Built on by (most cited first, 30 of 48)

- [PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds](https://mlsanity.com/paper/2305.04925/) (2023)
- [DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation](https://mlsanity.com/paper/2309.15109/) (2023)
- [HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention](https://mlsanity.com/paper/2404.06351/) (2024)
- [SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction](https://mlsanity.com/paper/2403.11492/) (2024)
- [Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding](https://mlsanity.com/paper/2310.12970/) (2023)
- [SEPT: Towards Efficient Scene Representation Learning for Motion Prediction](https://mlsanity.com/paper/2309.15289/) (2023)
- [GEDepth: Ground Embedding for Monocular Depth Estimation](https://mlsanity.com/paper/2309.09975/) (2023)
- [Learning Cooperative Trajectory Representations for Motion Forecasting](https://mlsanity.com/paper/2311.00371/) (2023)
- [Motion Forecasting in Continuous Driving](https://mlsanity.com/paper/2410.06007/) (2024)
- [Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed](https://mlsanity.com/paper/2409.16154/) (2024)
- [Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous Driving](https://mlsanity.com/paper/2306.07276/) (2023)
- [JointMotion: Joint Self-Supervision for Joint Motion Prediction](https://mlsanity.com/paper/2403.05489/) (2024)
- [SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction](https://mlsanity.com/paper/2410.08669/) (2024)
- [DONUT: A Decoder-Only Model for Trajectory Prediction](https://mlsanity.com/paper/2506.06854/) (2025)
- [RedMotion: Motion Prediction via Redundancy Reduction](https://mlsanity.com/paper/2306.10840/) (2023)
- [PBP: Path-based Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2309.03750/) (2023)
- [FutureNet-LOF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding](https://mlsanity.com/paper/2406.14422/) (2024)
- [Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network](https://mlsanity.com/paper/2407.18551/) (2024)
- [Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions](https://mlsanity.com/paper/2309.15685/) (2023)
- [ForeSight: Multi-View Streaming Joint Object Detection and Trajectory Forecasting](https://mlsanity.com/paper/2508.07089/) (2025)
- [A Survey on Large Language Models from Concept to Implementation](https://mlsanity.com/paper/2403.18969/) (2024)
- [SceneMotion: From Agent-Centric Embeddings to Scene-Wide Forecasts](https://mlsanity.com/paper/2408.01537/) (2024)
- [Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds](https://mlsanity.com/paper/2409.06827/) (2024)
- [Future-Aware Interaction Network For Motion Forecasting](https://mlsanity.com/paper/2503.06565/) (2025)
- [Controllable Diverse Sampling for Diffusion Based Motion Behavior Forecasting](https://mlsanity.com/paper/2402.03981/) (2024)
- [ProIn: Learning to Predict Trajectory Based on Progressive Interactions for Autonomous Driving](https://mlsanity.com/paper/2403.16374/) (2024)
- [Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models](https://mlsanity.com/paper/2407.19564/) (2024)
- [Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments](https://mlsanity.com/paper/2409.01971/) (2024)
- [When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network](https://mlsanity.com/paper/2506.17457/) (2025)
- [ITPNet: Towards Instantaneous Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2412.07369/) (2024)

## Abstract (arXiv)

Motion forecasting is a key module in an autonomous driving system. Due to the heterogeneous nature of multi-sourced input, multimodality in agent behavior, and low latency required by onboard deployment, this task is notoriously challenging. To cope with these difficulties, this paper proposes a novel agent-centric model with anchor-informed proposals for efficient multimodal motion prediction. We design a modality-agnostic strategy to concisely encode the complex input in a unified manner. We generate diverse proposals, fused with anchors bearing goal-oriented scene context, to induce multimodal prediction that covers a wide range of future trajectories. Our network architecture is highly uniform and succinct, leading to an efficient model amenable for real-world driving deployment. Experiments reveal that our agent-centric network compares favorably with the state-of-the-art methods in prediction accuracy, while achieving scene-centric level inference latency.