# Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets

arXiv: https://arxiv.org/abs/1906.08469 · HTML: https://mlsanity.com/paper/1906.08469/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in World models for autonomous driving, one of 11 directions in World models.
- Builds on 0 of its 50 references that are in the map.
- First posted on arXiv 20 Jun 2019.

## Placed in

- World models / [World models for autonomous driving](https://mlsanity.com/wm/world-models-for-autonomous-driving/)

## Built on by (most cited first, 16 of 16)

- [Understanding World or Predicting Future? A Comprehensive Survey of World Models](https://mlsanity.com/paper/2411.14499/) (2024)
- [GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning](https://mlsanity.com/paper/2204.08770/) (2022)
- [GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting](https://mlsanity.com/paper/2211.02545/) (2022)
- [Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning](https://mlsanity.com/paper/2206.13114/) (2022)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long-Term Trajectory Prediction](https://mlsanity.com/paper/2205.04624/) (2022)
- [Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning](https://mlsanity.com/paper/2103.14718/) (2021)
- [Estimating Uncertainty For Vehicle Motion Prediction on Yandex Shifts Dataset](https://mlsanity.com/paper/2112.08355/) (2021)
- [How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?](https://mlsanity.com/paper/2301.04414/) (2023)
- ["If you could see me through my eyes": Predicting Pedestrian Perception](https://mlsanity.com/paper/2202.13981/) (2022)
- [Motion Prediction Performance Analysis for Autonomous Driving Systems and the Effects of Tracking Noise](https://mlsanity.com/paper/2104.08368/) (2021)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain](https://mlsanity.com/paper/2407.19428/) (2024)
- [C2INet: Realizing Incremental Trajectory Prediction with Prior-Aware Continual Causal Intervention](https://mlsanity.com/paper/2411.12313/) (2024)
- [Collaborative-Distilled Diffusion Models (CDDM) for Accelerated and Lightweight Trajectory Prediction](https://mlsanity.com/paper/2510.00627/) (2025)

## Abstract (arXiv)

Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate safely and efficiently in its environment. This is particularly important when it comes to vulnerable road users (VRUs), such as pedestrians and bicyclists. These actors need to be handled with special care due to an increased risk of injury, as well as the fact that their behavior is less predictable than that of motorized actors. To address this issue, in the current study we present a deep learning-based method for predicting VRU movement, where we rasterize high-definition maps and actor's surroundings into a bird's-eye view image used as an input to deep convolutional networks. In addition, we propose a fast architecture suitable for real-time inference, and perform an ablation study of various rasterization approaches to find the optimal choice for accurate prediction. The results strongly indicate benefits of using the proposed approach for motion prediction of VRUs, both in terms of accuracy and latency.