# Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints

arXiv: https://arxiv.org/abs/2306.01075 · HTML: https://mlsanity.com/paper/2306.01075/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 49 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 30 of 42)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [Domain Generalization by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1903.06864/) (2019)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [On human motion prediction using recurrent neural networks](https://mlsanity.com/paper/1705.02445/) (2017)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [History Repeats Itself: Human Motion Prediction via Motion Attention](https://mlsanity.com/paper/2007.11755/) (2020)
- [Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction](https://mlsanity.com/paper/2003.08802/) (2020)
- [Convolutional Sequence to Sequence Model for Human Dynamics](https://mlsanity.com/paper/1805.00655/) (2018)
- [The Pose Knows: Video Forecasting by Generating Pose Futures](https://mlsanity.com/paper/1705.00053/) (2017)
- [A Neural Temporal Model for Human Motion Prediction](https://mlsanity.com/paper/1809.03036/) (2018)
- [Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice](https://mlsanity.com/paper/1805.11773/) (2018)
- [Conditional Generative Neural System for Probabilistic Trajectory Prediction](https://mlsanity.com/paper/1905.01631/) (2019)
- [Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction](https://mlsanity.com/paper/2002.08945/) (2020)
- [LOKI: Long Term and Key Intentions for Trajectory Prediction](https://mlsanity.com/paper/2108.08236/) (2021)
- [Action-Agnostic Human Pose Forecasting](https://mlsanity.com/paper/1810.09676/) (2018)
- [Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs](https://mlsanity.com/paper/2005.06582/) (2020)
- [Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention](https://mlsanity.com/paper/2104.05485/) (2021)
- [Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving](https://mlsanity.com/paper/2112.12141/) (2021)

## Built on by (most cited first, 12 of 12)

- [UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving](https://mlsanity.com/paper/2503.24381/) (2025)
- [A Novel Benchmarking Paradigm and a Scale- and Motion-Aware Model for Egocentric Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2310.10424/) (2023)
- [Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation](https://mlsanity.com/paper/2311.16091/) (2023)
- [MGTR: Multi-Granular Transformer for Motion Prediction with LiDAR](https://mlsanity.com/paper/2312.02409/) (2023)
- [SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning](https://mlsanity.com/paper/2407.17460/) (2024)
- [Adaptive Prediction Ensemble: Improving Out-of-Distribution Generalization of Motion Forecasting](https://mlsanity.com/paper/2407.09475/) (2024)
- [Towards Generalizable Safety in Crowd Navigation via Conformal Uncertainty Handling](https://mlsanity.com/paper/2508.05634/) (2025)
- [TrajEvo: Trajectory Prediction Heuristics Design via LLM-driven Evolution](https://mlsanity.com/paper/2508.05616/) (2025)
- [Application of Vision-Language Model to Pedestrians Behavior and Scene Understanding in Autonomous Driving](https://mlsanity.com/paper/2501.06680/) (2025)
- [Recent Advances in Deterministic Human Motion Prediction: A Review](https://mlsanity.com/paper/2312.06184/) (2023)
- [Feature Importance in Pedestrian Intention Prediction: A Context-Aware Review](https://mlsanity.com/paper/2409.07645/) (2024)
- [LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration](https://mlsanity.com/paper/2410.15819/) (2024)

## Abstract (arXiv)

Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas. In this work, we aim at identifying crossing pedestrians and predicting their future trajectories. To achieve these goals, we not only need the context information of road geometry and other traffic participants but also need fine-grained information of the human pose, motion and activity, which can be inferred from human keypoints. In this paper, we propose a novel multi-task learning framework for pedestrian crossing action recognition and trajectory prediction, which utilizes 3D human keypoints extracted from raw sensor data to capture rich information on human pose and activity. Moreover, we propose to apply two auxiliary tasks and contrastive learning to enable auxiliary supervisions to improve the learned keypoints representation, which further enhances the performance of major tasks. We validate our approach on a large-scale in-house dataset, as well as a public benchmark dataset, and show that our approach achieves state-of-the-art performance on a wide range of evaluation metrics. The effectiveness of each model component is validated in a detailed ablation study.