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Found 9 papers, 1 papers with code
Cross-cultural analysis of pedestrian group behaviour influence on crossing decisions in interactions with autonomous vehicles
Understanding cultural backgrounds is crucial for the seamless integration of autonomous driving into daily life as it ensures that systems are attuned to diverse societal norms and behaviours, enhancing acceptance and safety in varied cultural contexts.
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.
Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs
First, we comprehensively analyse the evaluation metrics, identify the main gaps of current benchmarks, and propose a new holistic evaluation framework.
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.
Model Guided Road Intersection Classification
Understanding complex scenarios from in-vehicle cameras is essential for safely operating autonomous driving systems in densely populated areas.
SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs
Autonomous vehicles navigate in dynamically changing environments under a wide variety of conditions, being continuously influenced by surrounding objects.
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.
