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Found 12 papers, 3 papers with code
Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
This study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and the impact on testing procedures, focusing on some of the essential requirements for trustworthy AI.
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).
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 Explainable Motion Prediction using Heterogeneous Graph Representations
In this work, we aim to improve the explainability of motion prediction systems by using different approaches.
Liability regimes in the age of AI: a use-case driven analysis of the burden of proof
New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better.
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.
Testing predictive automated driving systems: lessons learned and future recommendations
Conventional vehicles are certified through classical approaches, where different physical certification tests are set up on test tracks to assess required safety levels.
Data-driven vehicle speed detection from synthetic driving simulator images
In this paper we explore, for the first time, the use of synthetic images generated from a driving simulator (e. g., CARLA) to address vehicle speed detection using a learning-based approach.
From driving automation systems to autonomous vehicles: clarifying the terminology
The terminological landscape is rather cluttered when referring to autonomous driving or vehicles.
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.
Vision-based Vehicle Speed Estimation: A Survey
The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons.
