# Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

arXiv: https://arxiv.org/abs/2003.11959 · HTML: https://mlsanity.com/paper/2003.11959/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2020.

## Built on by (most cited first, 23 of 23)

- [A Survey on Simulators for Testing Self-Driving Cars](https://mlsanity.com/paper/2101.05337/) (2021)
- [Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review](https://mlsanity.com/paper/2308.06419/) (2023)
- [Spatiotemporal Deformable Scene Graphs for Complex Activity Detection](https://mlsanity.com/paper/2104.08194/) (2021)
- [Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios](https://mlsanity.com/paper/2211.05455/) (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)
- [Modeling human road crossing decisions as reward maximization with visual perception limitations](https://mlsanity.com/paper/2301.11737/) (2023)
- [VILLS -- Video-Image Learning to Learn Semantics for Person Re-Identification](https://mlsanity.com/paper/2311.17074/) (2023)
- [Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception](https://mlsanity.com/paper/2402.04370/) (2024)
- [A Quantitative Evaluation of the Expressivity of BMI, Pose and Gender in Body Embeddings for Recognition and Identification](https://mlsanity.com/paper/2503.06451/) (2025)
- [Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios](https://mlsanity.com/paper/2504.01409/) (2025)
- [Smooth-Trajectron++: Augmenting the Trajectron++ behaviour prediction model with smooth attention](https://mlsanity.com/paper/2305.19678/) (2023)
- [Text2Traj2Text: Learning-by-Synthesis Framework for Contextual Captioning of Human Movement Trajectories](https://mlsanity.com/paper/2409.12670/) (2024)
- [TPK: Trustworthy Trajectory Prediction Integrating Prior Knowledge For Interpretability and Kinematic Feasibility](https://mlsanity.com/paper/2505.06743/) (2025)
- [Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model](https://mlsanity.com/paper/2604.19838/) (2026)
- [A Utility Maximization Model of Pedestrian and Driver Interactions](https://mlsanity.com/paper/2110.11015/) (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)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [Real-Time Learning of Predictive Dynamic Obstacle Models for Robotic Motion Planning](https://mlsanity.com/paper/2511.00814/) (2025)
- [Realistic adversarial scenario generation via human-like pedestrian model for autonomous vehicle control parameter optimisation](https://mlsanity.com/paper/2601.02082/) (2026)
- [Evaluating Few-Shot Temporal Reasoning of LLMs for Human Activity Prediction in Smart Environments](https://mlsanity.com/paper/2602.11176/) (2026)
- [AttriBE: Quantifying Attribute Expressivity in Body Embeddings for Recognition and Identification](https://mlsanity.com/paper/2604.27218/) (2026)

## 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 II 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 psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behaviour, but much work is still needed to translate them into quantitative algorithms for practical AV control.