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Found 19 papers, 0 papers with code
Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossings
Predicting gap selection behavior and the use of zebra crossing enables driving systems to proactively respond and prevent potential conflicts.
Engineering Safety Requirements for Autonomous Driving with Large Language Models
Changes and updates in the requirement artifacts, which can be frequent in the automotive domain, are a challenge for SafetyOps.
On STPA for Distributed Development of Safe Autonomous Driving: An Interview Study
Safety analysis is used to identify hazards and build knowledge during the design phase of safety-relevant functions.
Welcome Your New AI Teammate: On Safety Analysis by Leashing Large Language Models
DevOps is a necessity in many industries, including the development of Autonomous Vehicles.
Evaluation of Out-of-Distribution Detection Performance on Autonomous Driving Datasets
Safety measures need to be systemically investigated to what extent they evaluate the intended performance of Deep Neural Networks (DNNs) for critical applications.
Prompt Smells: An Omen for Undesirable Generative AI Outputs
Recent Generative Artificial Intelligence (GenAI) trends focus on various applications, including creating stories, illustrations, poems, articles, computer code, music compositions, and videos.
RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems
We then examined the applicability of ethical AI development frameworks for performing effective RE during the development of trustworthy AI systems.
ZEBRA: Z-order Curve-based Event Retrieval Approach to Efficiently Explore Automotive Data
Yet, the data quality from professional test drivers is apparently higher than the one from large fleets where labels are missing, but the non-annotated data set from large vehicle fleets is much more representative for typical, realistic driving scenarios to be handled by automated vehicles.
Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings
Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving.
Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems
As the NN is trained on well annotated images, in this paper we study the variations of confidence levels from the NN when tested on hand-crafted occlusion added to a test set.
Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks
Implementing Deep Neural Networks (DNN) for non-safety related applications have shown remarkable achievements over the past years; however, for using DNNs in safety critical applications, we are missing approaches for verifying the robustness of such models.
Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Prediction
In this paper, we study the interaction between pedestrians and vehicles and propose a novel neural network structure called the Pedestrian-Vehicle Interaction (PVI) extractor for learning the pedestrian-vehicle interaction.
Are we ready for beyond-application high-volume data? The Reeds robot perception benchmark dataset
The spatiotemporal resolution of sensors were maximized in order to provide large variations and flexibility in the data, offering evaluation at a large number of different resolution presets based on the resolution found in other datasets.
A Structured Analysis of the Video Degradation Effects on the Performance of a Machine Learning-enabled Pedestrian Detector
Firstly, a baseline of applying YOLO to 1, 026 frames with pedestrian annotations in the KITTI Vision Benchmark Suite has been established.
Social-IWSTCNN: A Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarios
In this paper, we use the recently released large-scale Waymo Open Dataset in urban traffic scenarios, which includes 374 urban training scenes and 76 urban testing scenes to analyze the performance of our proposed algorithm in comparison to the state-of-the-art (SOTA) models.
Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks
Understanding the relationship between training results and supervisor performance is valuable to improve robustness of the model and indicates where more work has to be done to create generalized models for safety critical applications.
HPM-Frame: A Decision Framework for Executing Software on Heterogeneous Platforms
Heterogeneous computing is one of the most important computational solutions to meet rapidly increasing demands on system performance.
Software Engineering
Towards Structured Evaluation of Deep Neural Network Supervisors
Deep Neural Networks (DNN) have improved the quality of several non-safety related products in the past years.
Paving the Roadway for Safety of Automated Vehicles: An Empirical Study on Testing Challenges
This paper presents current challenges of testing the functionality and safety of automated vehicles derived from conducting focus groups and interviews with 26 participants from five countries having a background related to testing automotive safety-related topics. We provide an overview of the state-of-practice of testing active safety features as well as challenges that needs to be addressed in the future to ensure safety for automated vehicles.
