313 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chalmers University of TechnologySE38 papers
- Volvo Cars (Sweden)32 papers
- University of GothenburgSE15 papers
- Lund UniversitySE4 papers
- QRtech (Sweden)SE3 papers
- RISE Research Institutes of SwedenSE3 papers
- Scania (Sweden)SE3 papers
- Semcon (Sweden)SE3 papers
- Fraunhofer Chalmers Research Centre for Industrial MathematicsSE2 papers
- Malmö UniversitySE2 papers
- Politecnico di MilanoIT2 papers
- Saab (Sweden)SE2 papers
13 papers · 1 filter
Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward
Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani
We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change maneuvers in…
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…
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…
Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks
Jens Henriksson, Christian Berger, Markus Borg +3
Several areas have been improved with Deep Learning during the past years. Implementing Deep Neural Networks (DNN) for non-safety related applications have shown remarkable achieve…
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…
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…
Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks
Jens Henriksson, Christian Berger, Markus Borg +3
Several areas have been improved with Deep Learning during the past years. For non-safety related products adoption of AI and ML is not an issue, whereas in safety critical applica…
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…
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…
Towards Structured Evaluation of Deep Neural Network Supervisors
Jens Henriksson, Christian Berger, Markus Borg +4
Deep Neural Networks (DNN) have improved the quality of several non-safety related products in the past years. However, before DNNs should be deployed to safety-critical applicatio…
Road Friction Estimation for Connected Vehicles using Supervised Machine Learning
Ghazaleh Panahandeh, Erik Ek, Nasser Mohammadiha
In this paper, the problem of road friction prediction from a fleet of connected vehicles is investigated. A framework is proposed to predict the road friction level using both his…