# Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking

arXiv: https://arxiv.org/abs/2002.11669 · HTML: https://mlsanity.com/paper/2002.11669/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

## Built on by (most cited first, 13 of 13)

- [Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning](https://mlsanity.com/paper/2308.03975/) (2023)
- [MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion](https://mlsanity.com/paper/2409.10473/) (2024)
- [Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond](https://mlsanity.com/paper/2406.02978/) (2024)
- [Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognition](https://mlsanity.com/paper/2209.02986/) (2022)
- [Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned model](https://mlsanity.com/paper/2104.10496/) (2021)
- [Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?](https://mlsanity.com/paper/2206.11110/) (2022)
- [Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios](https://mlsanity.com/paper/2504.01409/) (2025)
- [Single Shot Multitask Pedestrian Detection and Behavior Prediction](https://mlsanity.com/paper/2101.02232/) (2021)
- [P2P-Loc: Point to Point Tiny Person Localization](https://mlsanity.com/paper/2112.15344/) (2021)
- [OpenPodcar: an Open Source Vehicle for Self-Driving Car Research](https://mlsanity.com/paper/2205.04454/) (2022)
- [Attention Map Guided Transformer Pruning for Edge Device](https://mlsanity.com/paper/2304.01452/) (2023)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [Realtime Dynamic Gaze Target Tracking and Depth-Level Estimation](https://mlsanity.com/paper/2406.18595/) (2024)

## Abstract (arXiv)

Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behaviour modelling, prediction and interaction control.