313 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Volvo (Sweden)SE32 papers
- Chalmers University of TechnologySE21 papers
- University of GothenburgSE9 papers
- Scania (Sweden)SE3 papers
- Fraunhofer Chalmers Research Centre for Industrial MathematicsSE2 papers
- Lund UniversitySE2 papers
- Malmö UniversitySE2 papers
- Politecnico di MilanoIT2 papers
- Association for Computing MachineryUS1 paper
- Carl von Ossietzky Universität OldenburgDE1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- École PolytechniqueFR1 paper
33 papers
Understanding Conversational Patterns in Multi-agent Programming: A Case Study on Fibonacci Game Development
Srijita Basu, Viktor Kjellberg, Simin Sun +5
Large Language Models (LLMs) are increasingly applied to software engineering (SE), yet their potential for autonomous, role-oriented collaboration remains largely underexplored. U…
Welcome Your New AI Teammate: On Safety Analysis by Leashing Large Language Models
Ali Nouri, Beatriz Cabrero-Daniel, Fredrik Törner +2
DevOps is a necessity in many industries, including the development of Autonomous Vehicles. In those settings, there are iterative activities that reduce the speed of SafetyOps cyc…
On STPA for Distributed Development of Safe Autonomous Driving: An Interview Study
Ali Nouri, Christian Berger, Fredrik Törner
Safety analysis is used to identify hazards and build knowledge during the design phase of safety-relevant functions. This is especially true for complex AI-enabled and software in…
An Industrial Experience Report about Challenges from Continuous Monitoring, Improvement, and Deployment for Autonomous Driving Features
Ali Nouri, Christian Berger, Fredrik Torner
Using continuous development, deployment, and monitoring (CDDM) to understand and improve applications in a customer's context is widely used for non-safety applications such as sm…
Designing deep neural networks for driver intention recognition
Koen Vellenga, H. Joe Steinhauer, Alexander Karlsson +3
Driver intention recognition studies increasingly rely on deep neural networks. Deep neural networks have achieved top performance for many different tasks, but it is not a common…
Automatic cable harness layout routing in a customizable 3D environment
T. Karlsson, E. Åblad, T. Hermansson +2
Designing cable harnesses can be time-consuming and complex due to many design and manufacturing aspects and rules. Automating the design process can help to fulfil these rules, sp…
Modeling Lead-vehicle Kinematics For Rear-end Crash Scenario Generation
Jian Wu, Carol Flannagan, Ulrich Sander +1
The use of virtual safety assessment as the primary method for evaluating vehicle safety technologies has emphasized the importance of crash scenario generation. One of the most co…
Overview of Computer Vision Techniques in Robotized Wire Harness Assembly: Current State and Future Opportunities
Hao Wang, Omkar Salunkhe, Walter Quadrini +4
Wire harnesses are essential hardware for electronic systems in modern automotive vehicles. With a shift in the automotive industry towards electrification and autonomous driving,…
A Systematic Literature Review of Computer Vision Applications in Robotized Wire Harness Assembly
Hao Wang, Omkar Salunkhe, Walter Quadrini +6
This article provides a systematic literature review of computer vision applications in robotized wire harness assembly.
Online Learning Models for Vehicle Usage Prediction During COVID-19
Tobias Lindroth, Axel Svensson, Niklas Åkerblom +2
Today, there is an ongoing transition to more sustainable transportation, for which an essential part is the switch from combustion engine vehicles to battery electric vehicles (BE…
Passive and Active Learning of Driver Behavior from Electric Vehicles
Federica Comuni, Christopher Mészáros, Niklas Åkerblom +1
Modeling driver behavior provides several advantages in the automotive industry, including prediction of electric vehicle energy consumption. Studies have shown that aggressive dri…
Online Learning of Energy Consumption for Navigation of Electric Vehicles
Niklas Åkerblom, Yuxin Chen, Morteza Haghir Chehreghani
Energy efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consum…
Bayesian propensity score matching in automotive embedded software engineering
Yuchu Liu, David Issa Mattos, Jan Bosch +2
Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating the value that new software brings to customers. However, running randomised fiel…
Online Learning of Network Bottlenecks via Minimax Paths
Niklas Åkerblom, Fazeleh Sadat Hoseini, Morteza Haghir Chehreghani
In this paper, we study bottleneck identification in networks via extracting minimax paths. Many real-world networks have stochastic weights for which full knowledge is not availab…
Active Learning of Driving Scenario Trajectories
Sanna Jarl, Linus Aronsson, Sadegh Rahrovani +1
Annotated driving scenario trajectories are crucial for verification and validation of autonomous vehicles. However, annotation of such trajectories based only on explicit rules (i…
An architecture for enabling A/B experiments in automotive embedded software
Yuchu Liu, Jan Bosch, Helena Holmström Olsson +1
A/B experimentation is a known technique for data-driven product development and has demonstrated its value in web-facing businesses. With the digitalisation of the automotive indu…
Size matters? Or not: A/B testing with limited sample in automotive embedded software
Yuchu Liu, David Issa Mattos, Jan Bosch +2
A/B testing is gaining attention in the automotive sector as a promising tool to measure causal effects from software changes. Different from the web-facing businesses, where A/B t…
Managing Traceability Information Models: Not such a simple task after all?
Salome Maro, Jan-Philipp Steghöfer, Eric Knauss +7
Practitioners are poorly supported by the scientific literature when managing traceability information models (TIMs), which capture the structure and semantics of trace links. In p…
River: machine learning for streaming data in Python
Jacob Montiel, Max Halford, Saulo Martiello Mastelini +8
River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performan…
Understanding Work Practices of Autonomous Agile Teams: A Social-psychological Review
Lucas Gren
The purpose of this paper is to suggest additional aspects of social psychology that could help when making sense of autonomous agile teams. To make use of well-tested theories in…
A Generic Framework for Clustering Vehicle Motion Trajectories
Fazeleh S. Hoseini, Sadegh Rahrovani, Morteza Haghir Chehreghani
The development of autonomous vehicles requires having access to a large amount of data in the concerning driving scenarios. However, manual annotation of such driving scenarios is…
A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories
Andreas Demetriou, Henrik Alfsvåg, Sadegh Rahrovani +1
We propose a unified deep learning framework for the generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. To model and gene…
What an Agile Leader Does: The Group Dynamics Perspective
Lucas Gren, Magdalena Lindman
When large industrial organizations change to(or start with) an agile approach to operations, managers and some employees are supposed to be "agile leaders" often without being giv…
Security Assurance Cases for Road Vehicles: an Industry Perspective
Mazen Mohamad, Alexander Åström, Örjan Askerdal +2
Assurance cases are structured arguments that are commonly used to reason about the safety of a product or service. Currently, there is an ongoing push towards using assurance case…