Search Results for author: Christian Berger

Found 20 papers, 0 papers with code

Semantic-Aware Representation of Multi-Modal Data for Data Ingress: A Literature Review

no code implementations17 Jul 2024 Pierre Lamart, Yinan Yu, Christian Berger

Rather the opposite is important since getting an understanding of the inherent quality and diversity of the underlying data lakes is a growing challenge for application-specific ML as well as for fine-tuning foundation models.

Diversity Information Retrieval

Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossings

no code implementations15 Apr 2024 Chi Zhang, Janis Sprenger, Zhongjun Ni, Christian Berger

Predicting gap selection behavior and the use of zebra crossing enables driving systems to proactively respond and prevent potential conflicts.

On STPA for Distributed Development of Safe Autonomous Driving: An Interview Study

no code implementations14 Mar 2024 Ali Nouri, Christian Berger, Fredrik Törner

Safety analysis is used to identify hazards and build knowledge during the design phase of safety-relevant functions.

Autonomous Driving valid

Evaluation of Out-of-Distribution Detection Performance on Autonomous Driving Datasets

no code implementations30 Jan 2024 Jens Henriksson, Christian Berger, Stig Ursing, Markus Borg

Safety measures need to be systemically investigated to what extent they evaluate the intended performance of Deep Neural Networks (DNNs) for critical applications.

Autonomous Driving Out-of-Distribution Detection +1

Prompt Smells: An Omen for Undesirable Generative AI Outputs

no code implementations23 Jan 2024 Krishna Ronanki, Beatriz Cabrero-Daniel, Christian Berger

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

no code implementations29 May 2023 Krishna Ronanki, Beatriz Cabrero-Daniel, Jennifer Horkoff, Christian Berger

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

no code implementations20 Apr 2023 Christian Berger, Lukas Birkemeyer

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.

Retrieval Time Series

Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings

no code implementations17 Apr 2023 Chi Zhang, Amir Hossein Kalantari, Yue Yang, Zhongjun Ni, Gustav Markkula, Natasha Merat, Christian Berger

Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving.

Model Selection regression

Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems

no code implementations26 Apr 2022 Jens Henriksson, Christian Berger, Stig Ursing

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.

Body Detection object-detection +1

Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks

no code implementations26 Apr 2022 Jens Henriksson, Christian Berger, Markus Borg, Lars Tornberg, Sankar Raman Sathyamoorthy, Cristofer Englund

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.

Outlier Detection Out-of-Distribution Detection

Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Prediction

no code implementations10 Feb 2022 Chi Zhang, Christian Berger

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.

Pedestrian Trajectory Prediction Trajectory Prediction

Are we ready for beyond-application high-volume data? The Reeds robot perception benchmark dataset

no code implementations16 Sep 2021 Ola Benderius, Christian Berger, Krister Blanch

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

no code implementations30 Jun 2021 Christian Berger

Firstly, a baseline of applying YOLO to 1, 026 frames with pedestrian annotations in the KITTI Vision Benchmark Suite has been established.

Decision Making

Social-IWSTCNN: A Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarios

no code implementations26 May 2021 Chi Zhang, Christian Berger, Marco Dozza

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.

Pedestrian Trajectory Prediction Trajectory Prediction

Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks

no code implementations29 Mar 2021 Jens Henriksson, Christian Berger, Markus Borg, Lars Tornberg, Sankar Raman Sathyamoorthy, Cristofer Englund

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.

Outlier Detection Out-of-Distribution Detection

HPM-Frame: A Decision Framework for Executing Software on Heterogeneous Platforms

no code implementations1 Dec 2020 Hugo Andrade, Ola Benderius, Christian Berger, Ivica Crnkovic, Jan Bosch

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

no code implementations4 Mar 2019 Jens Henriksson, Christian Berger, Markus Borg, Lars Tornberg, Cristofer Englund, Sankar Raman Sathyamoorthy, Stig Ursing

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

no code implementations9 May 2017 Alessia Knauss, Jan Schröder, Christian Berger, Henrik Eriksson

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.

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