# Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features

arXiv: https://arxiv.org/abs/2012.08418 · HTML: https://mlsanity.com/paper/2012.08418/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 11 of 11)

- [Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review](https://mlsanity.com/paper/2308.06419/) (2023)
- [PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions](https://mlsanity.com/paper/2112.02604/) (2021)
- [On-Board Pedestrian Trajectory Prediction Using Behavioral Features](https://mlsanity.com/paper/2210.11999/) (2022)
- [Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems](https://mlsanity.com/paper/2305.01773/) (2023)
- [Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?](https://mlsanity.com/paper/2206.11110/) (2022)
- [Context-aware Multi-task Learning for Pedestrian Intent and Trajectory Prediction](https://mlsanity.com/paper/2407.17162/) (2024)
- [Diving Deeper Into Pedestrian Behavior Understanding: Intention Estimation, Action Prediction, and Event Risk Assessment](https://mlsanity.com/paper/2407.00446/) (2024)
- [Eye Gaze-Informed and Context-Aware Pedestrian Trajectory Prediction in Shared Spaces with Automated Shuttles: A Virtual Reality Study](https://mlsanity.com/paper/2603.19812/) (2026)
- [Pedestrian Environment Model for Automated Driving](https://mlsanity.com/paper/2308.09080/) (2023)
- [Evaluating Few-Shot Temporal Reasoning of LLMs for Human Activity Prediction in Smart Environments](https://mlsanity.com/paper/2602.11176/) (2026)
- [Scenario-Independent Criticality Assessment and Prediction for Vulnerable Road Users in Autonomous Driving](https://mlsanity.com/paper/2609.11947/) (2026)

## Abstract (arXiv)

Automated vehicles require a comprehensive understanding of traffic situations to ensure safe and anticipatory driving. In this context, the prediction of pedestrians is particularly challenging as pedestrian behavior can be influenced by multiple factors. In this paper, we thoroughly analyze the requirements on pedestrian behavior prediction for automated driving via a system-level approach. To this end we investigate real-world pedestrian-vehicle interactions with human drivers. Based on human driving behavior we then derive appropriate reaction patterns of an automated vehicle and determine requirements for the prediction of pedestrians. This includes a novel metric tailored to measure prediction performance from a system-level perspective. The proposed metric is evaluated on a large-scale dataset comprising thousands of real-world pedestrian-vehicle interactions. We furthermore conduct an ablation study to evaluate the importance of different contextual cues and compare these results to ones obtained using established performance metrics for pedestrian prediction. Our results highlight the importance of a system-level approach to pedestrian behavior prediction.