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Found 12 papers, 3 papers with code
In the driver's mind: modeling the dynamics of human overtaking decisions in interactions with oncoming automated vehicles
To address these gaps, we conducted a "reverse Wizard-of-Oz" driving simulator experiment with 30 participants who repeatedly interacted with oncoming AVs and HDVs, measuring the drivers' gap acceptance decisions and response times.
Are you sure? Modelling Drivers' Confidence Judgments in Left-Turn Gap Acceptance Decisions
We found that confidence in these decisions depends on the size of the gap to the oncoming vehicle.
Robust Multi-Modal Density Estimation
We compared our approach to state-of-the-art methods for density estimation as well as ablations of ROME, showing that it not only outperforms established methods but is also more robust to a variety of distributions.
Data-driven Semi-supervised Machine Learning with Surrogate Safety Measures for Abnormal Driving Behavior Detection
Moreover, previous ML-based approaches predominantly utilize basic vehicle motion features (such as velocity and acceleration) to label and detect abnormal driving behaviors, while this study seeks to introduce Surrogate Safety Measures (SSMs) as the input features for ML models to improve the detection performance.
A cognitive process approach to modeling gap acceptance in overtaking
In this study, we address this issue by employing a cognitive process approach to describe the dynamic interactions by the oncoming vehicle during overtaking maneuvers.
Smooth-Trajectron++: Augmenting the Trajectron++ behaviour prediction model with smooth attention
Understanding traffic participants' behaviour is crucial for predicting their future trajectories, aiding in developing safe and reliable planning systems for autonomous vehicles.
Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study
The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style.
Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios
Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions.
Uncovering variability in human driving behavior through automatic extraction of similar traffic scenes from large naturalistic datasets
The human responses to the selected scenes exposed the variability on both the tactical and operational levels.
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.
Meaningful human control: actionable properties for AI system development
The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging.
Optimality and limitations of audio-visual integration for cognitive systems
In particular, the same optimal computational model can lead to illusory percepts, and we suggest that more studies should be needed to detect and mitigate these illusions, as artefacts in artificial cognitive systems.
