Modelling communication-enabled traffic interactions

A major challenge for autonomous vehicles is handling interactions with human-driven vehicles—for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume...

Feasible Action-Space Reduction as a Metric of Causal Responsibility in Multi-Agent Spatial Interactions

Modelling causal responsibility in multi-agent spatial interactions is crucial for safety and efficiency of interactions of humans with autonomous agents. However, current formal metrics and models of responsibility either lack grounding in ethical and philosophical concepts of responsibility, or cannot be applied to spatial interactions. In...

Response times in drivers' gap acceptance decisions during overtaking

Overtaking on two-lane roads can lead to increased collision risks due to drivers' errors in evaluating whether or not to accept the gap to the vehicle in the opposite lane. Understanding these gap acceptance decisions can help mitigate the risks associated with overtaking. Previous research on overtaking has focused on the factors...

Benchmarking Behavior Prediction Models in Gap Acceptance Scenarios

Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction models enabling more efficient trajectory planning could make autonomous vehicles more assertive in such interactions. However, the evaluation of such models is...

MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning

Inferring reward functions from demonstrations and pairwise preferences are auspicious approaches for aligning Reinforcement Learning (RL) agents with human intentions. However, state-of-the art methods typically focus on learning a single reward model, thus rendering it difficult to trade off different reward functions from multiple experts. We...

Interactive Merging Behavior in a Coupled Driving Simulator: Experimental Framework and Case Study

Human highway-merging behavior is an important aspect when developing autonomous vehicles (AVs) that can safely and successfully interact with other road users. To design safe and acceptable human-AV interactions, the underlying mechanisms in human-human interactive behavior need to be understood. Exposing and understanding these mechanisms can...

Meaningful human control: actionable properties for AI system development

How can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of...

A Human Factors Approach to Validating Driver Models for Interaction-aware Automated Vehicles

A major challenge for autonomous vehicles is interacting with other traffic participants safely and smoothly. A promising approach to handle such traffic interactions is equipping autonomous vehicles with interaction-aware controllers (IACs). These controllers predict how surrounding human drivers will respond to the autonomous vehicle’s actions...

Modeling Human Behavior in Human-Robot Interactions

This interdisciplinary workshop aims to break boundaries between the researchers who develop human models (e.g., from the fields of human factors, cognitive psychology, and computational neuroscience) and roboticists who use human models in different human-robot interaction (HRI) contexts. The keynote talks, contributed submissions, and...

Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers

Objective: We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers. Background: Understanding decisions of human drivers is essential for the development of safe and...

Cognitive processing of miscommunication in interactive listening: An evaluation of listener indecision and cognitive effort

Purpose: The purpose of the current study was to evaluate the social and cognitive underpinnings of miscommunication during an interactive listening task. Method: An eye and computer mouse-tracking visualworld paradigm was used to investigate how a listener’s cognitive effort (local and global) and decision-making processes were affected by a...

Optimality and Limitations of Audio-Visual Integration for Cognitive Systems

Multimodal integration is an important process in perceptual decision-making. In humans, this process has often been shown to be statistically optimal, or near optimal: sensory information is combined in a fashion that minimizes the average error in perceptual representation of stimuli. However, sometimes there are costs that come with the...