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Found 7 papers, 1 papers with code
Behavioural gap assessment of human-vehicle interaction in real and virtual reality-based scenarios in autonomous driving
In this paper, we present a first and innovative approach to evaluating what we term the behavioural gap, a concept that captures the disparity in a participant's conduct when engaging in a VR experiment compared to an equivalent real-world situation.
RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models
For that purpose, Knowledge Graph Embeddings (KGE) and Bayesian inference are combined to allow the deployment of a fully inductive reasoning system that enables the issuing of predictions that rely on legacy information contained in the graph as well as on current evidence gathered in real time by onboard sensors.
Pedestrian and Passenger Interaction with Autonomous Vehicles: Field Study in a Crosswalk Scenario
The internal and external HMIs were integrated with implicit communication techniques, incorporating a combination of gentle and aggressive braking maneuvers within the crosswalk.
Attribute Annotation and Bias Evaluation in Visual Datasets for Autonomous Driving
This paper addresses the often overlooked issue of fairness in the autonomous driving domain, particularly in vision-based perception and prediction systems, which play a pivotal role in the overall functioning of Autonomous Vehicles (AVs).
Vehicle Trajectory Prediction on Highways Using Bird Eye View Representations and Deep Learning
The U-net model has been selected as the prediction kernel to generate future visual representations of the scene using an image-to-image regression approach.
WiFiNet: WiFi-based indoor localisation using CNNs
Different technologies have been proposed to provide indoor localisation: magnetic field, bluetooth , WiFi, etc.
Vehicle Ego-Lane Estimation with Sensor Failure Modeling
We present a probabilistic ego-lane estimation algorithm for highway-like scenarios that is designed to increase the accuracy of the ego-lane estimate, which can be obtained relying only on a noisy line detector and tracker.
