Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking
Not on the map. Why?
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.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Feb 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 3; 2023: 3; 2024: 3; 2025: 1.
13 papers.
Sort:
By area:
- 2023Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learningcited by 19
- 2024MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusioncited by 15
- 2024Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyondcited by 5
- 2022Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognitioncited by 4
- 2021Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned modelcited by 3
- 2022Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?cited by 3
- 2025Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarioscited by 2
- 2021Single Shot Multitask Pedestrian Detection and Behavior Predictioncited by 1
- 2021P2P-Loc: Point to Point Tiny Person Localizationcited by 0
- 2022OpenPodcar: an Open Source Vehicle for Self-Driving Car Researchcited by 0
- 2023Attention Map Guided Transformer Pruning for Edge Devicecited by 0
- 2023Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehiclecited by 0
1 more
- 2025Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios2 Apr 2025
- 2024MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion16 Sep 2024
- 2024Realtime Dynamic Gaze Target Tracking and Depth-Level Estimation9 Jun 2024
- 2024Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond5 Jun 2024
- 2023Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle12 Sep 2023
- 2023Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning8 Aug 2023
- 2023Attention Map Guided Transformer Pruning for Edge Device4 Apr 2023
- 2022Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognition7 Sep 2022
- 2022Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?22 Jun 2022
- 2022OpenPodcar: an Open Source Vehicle for Self-Driving Car Research9 May 2022
- 2021P2P-Loc: Point to Point Tiny Person Localization31 Dec 2021
- 2021Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned model21 Apr 2021
1 more
- 2023Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learningcited by 19
- 2024Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyondcited by 5
- 2022Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognitioncited by 4
- 2021Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned modelcited by 3
- 2022Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?cited by 3
- 2021Single Shot Multitask Pedestrian Detection and Behavior Predictioncited by 1
- 2021P2P-Loc: Point to Point Tiny Person Localizationcited by 0
- 2022OpenPodcar: an Open Source Vehicle for Self-Driving Car Researchcited by 0
- 2023Attention Map Guided Transformer Pruning for Edge Devicecited by 0
- 2023Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehiclecited by 0
- 2024Realtime Dynamic Gaze Target Tracking and Depth-Level Estimationcited by 0
- 2024Realtime Dynamic Gaze Target Tracking and Depth-Level Estimation9 Jun 2024
- 2024Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond5 Jun 2024
- 2023Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle12 Sep 2023
- 2023Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning8 Aug 2023
- 2023Attention Map Guided Transformer Pruning for Edge Device4 Apr 2023
- 2022Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognition7 Sep 2022
- 2022Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?22 Jun 2022
- 2022OpenPodcar: an Open Source Vehicle for Self-Driving Car Research9 May 2022
- 2021P2P-Loc: Point to Point Tiny Person Localization31 Dec 2021
- 2021Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned model21 Apr 2021
- 2021Single Shot Multitask Pedestrian Detection and Behavior Prediction6 Jan 2021