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

Abstract

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.

Accepted for publication in the IEEE Transactions on Intelligent Transportation Systems

References in corpus (7)

• MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
• INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps
• Microscopic Pedestrian Flow Characteristics: Development of an Image Processing Data Collection and Simulation Model
• Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans
• Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking
• FieldSAFE: Dataset for Obstacle Detection in Agriculture
• Intent Communication between Autonomous Vehicles and Pedestrians

Cited by in corpus (11)

• Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review
• Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking
• Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation
• Potential Game-Based Decision-Making for Autonomous Driving
• Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?
• Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios
• Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned model
• Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios
• A Survey on Simulators for Testing Self-Driving Cars
• Optimal Behavior Planning for Implicit Communication using a Probabilistic Vehicle-Pedestrian Interaction Model
• A Utility Maximization Model of Pedestrian and Driver Interactions