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Kharrazi, S., Nielsen, L. & Frisk, E. (2023). Generation of Mission-Based Driving Cycles Using Behavioral Models Parameterized for Different Driver Categories. SAE technical paper series
Open this publication in new window or tab >>Generation of Mission-Based Driving Cycles Using Behavioral Models Parameterized for Different Driver Categories
2023 (English)In: SAE technical paper series, ISSN 0148-7191, , p. 11Article in journal (Refereed) Published
Abstract [en]

A methodology for the generation of representative driving cycles is proposed and evaluated. The proposed method combines traffic simulation and driving behavior modeling to generate mission-based driving cycles. Extensions to the existing behavioral model in a traffic simulation tool are suggested and parameterized for different driver categories to capture the effects of road geometry and variances between drivers. The evaluation results illustrate that the developed extensions significantly improve the match between driving data and the driving cycles generated by traffic simulation. Using model extensions parameterized for different driver categories, instead of only one average driver, provides the possibility to represent different driving behaviors and further improve the realism of the resulting driving cycles.

Place, publisher, year, edition, pages
SAE International, 2023. p. 11
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:liu:diva-194735 (URN)10.4271/2023-01-5033 (DOI)
Note

Thea rticle is a non-event SAE technical paper

Available from: 2023-06-09 Created: 2023-06-09 Last updated: 2023-09-13Bibliographically approved
Hashemniya, F., Frisk, E. & Krysander, M. (2023). Hierarchical Diagnosis Algorithm for Component-Based Multi-Mode Systems. In: Hideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita (Ed.), 22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023: . Paper presented at 22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023 (pp. 11317-11323). , 56(2)
Open this publication in new window or tab >>Hierarchical Diagnosis Algorithm for Component-Based Multi-Mode Systems
2023 (English)In: 22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023 / [ed] Hideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita, 2023, Vol. 56, no 2, p. 11317-11323Conference paper, Published paper (Refereed)
Abstract [en]

This paper is focused on fault detection and isolation of component-based multi-mode systems, i.e., systems that can be operated in different continuous modes. As the system mode changes, the structure of the system also changes which impacts diagnosability analysis and synthesis. To meet this challenge, diagnosis based on a structural approach is modified to also detect and isolate faults when modes change. Here, definitions for some important diagnosis concepts are extended to cover also multi-mode systems. Then, a method for hierarchical diagnosis of component-based systems is proposed. The method is exemplified on a Li-ion battery pack to show its effectiveness.

Series
IFAC papersonline, E-ISSN 2405-8963
Keywords
Fault isolation, multi-mode systems, structural analysis, hierarchical, local diagnosis
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-201133 (URN)2-s2.0-85183665045 (Scopus ID)
Conference
22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023
Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-02-22
Frisk, E., Jarmolowitz, F., Jung, D. & Krysander, M. (2022). Fault Diagnosis Using Data, Models, or Both – An Electrical Motor Use-Case. In: : . Paper presented at 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2022. Pafos, Cyprus, 8-10 June 2022 (pp. 533-538). Elsevier, 55(6)
Open this publication in new window or tab >>Fault Diagnosis Using Data, Models, or Both – An Electrical Motor Use-Case
2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

With trends as IoT and increased connectivity, the availability of data is consistently increasing and its automated processing with, e.g., machine learning becomes more important. This is certainly true for the area of fault diagnostics and prognostics. However, for rare events like faults, the availability of meaningful data will stay inherently sparse making a pure data-driven approach more difficult. In this paper, the question when to use model-based, data-driven techniques, or a combined approach for fault diagnosis is discussed using real-world data of a permanent magnet synchronous machine. Key properties of the different approaches are discussed in a diagnosis context, performance quantified, and benefits of a combined approach are demonstrated.

Place, publisher, year, edition, pages
Elsevier, 2022
Series
IFAC papers online, E-ISSN 2405-8963
Keywords
fault diagnosis, model-based diagnosis, data-driven diagnosis, sparse data
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-188246 (URN)10.1016/j.ifacol.2022.07.183 (DOI)000884499400003 ()
Conference
11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2022. Pafos, Cyprus, 8-10 June 2022
Available from: 2022-09-07 Created: 2022-09-07 Last updated: 2022-12-06
Fors, V., Olofsson, B. & Frisk, E. (2022). Resilient Branching MPC for Multi-Vehicle Traffic Scenarios Using Adversarial Disturbance Sequences. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 7(4), 838-848
Open this publication in new window or tab >>Resilient Branching MPC for Multi-Vehicle Traffic Scenarios Using Adversarial Disturbance Sequences
2022 (English)In: IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, ISSN 2379-8858, Vol. 7, no 4, p. 838-848Article in journal (Refereed) Published
Abstract [en]

An approach to resilient planning and control of autonomous vehicles in multi-vehicle traffic scenarios is proposed. The proposed method is based on model predictive control (MPC), where alternative predictions of the surrounding traffic are determined automatically such that they are intentionally adversarial to the ego vehicle. This provides robustness against the inherent uncertainty in traffic predictions. To reduce conservatism, an assumption that other agents are of no ill intent is formalized. Simulation results from highway driving scenarios show that the proposed method in real-time negotiates traffic situations out of scope for a nominal MPC approach and performs favorably to state-of-the-art reinforcement-learning approaches without requiring prior training. The results also show that the proposed method performs effectively, with the ability to prune disturbance sequences with a lower risk for the ego vehicle.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022
Keywords
Autonomous vehicles; Nonlinear systems; Decision making; Autonomous driving; tactical decision making; uncertain systems; predictive control for nonlinear systems
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:liu:diva-191649 (URN)10.1109/TIV.2022.3168772 (DOI)000906805200005 ()
Note

Funding Agencies|Excellence Center at Linkoeping-Lund in Information Technology (ELLIIT); Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Available from: 2023-02-07 Created: 2023-02-07 Last updated: 2023-03-18
Westny, T., Olofsson, B. & Frisk, E. (2022). Uncertainties in Robust Planning and Control of Autonomous Tractor-Trailer Vehicles. In: : . Paper presented at AVEC'22 The 15th International Symposium on Advanced Vehicle Control, Sept. 12-16, 2022.
Open this publication in new window or tab >>Uncertainties in Robust Planning and Control of Autonomous Tractor-Trailer Vehicles
2022 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

To study the effects of uncertainty in autonomous motion planning and control, an 8-DOF model of a tractor-semitrailer is implemented and analyzed. The implications of uncertainties in the model are then quantified and presented using sensitivity analysis and closed-loop simulations. The study shows that different model parameters are more or less critical depending on the investigated scenario.- Using sampling-based closed-loop predictions, uncertainty bounds on state variable trajectories are determined. Our findings suggest the potential for the inclusion of our method within a robust predictive controller or as a driver-assistance system for rollover or lane departure warning.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-187799 (URN)
Conference
AVEC'22 The 15th International Symposium on Advanced Vehicle Control, Sept. 12-16, 2022
Available from: 2022-08-25 Created: 2022-08-25 Last updated: 2022-08-31
Mohseni, F., Frisk, E. & Nielsen, L. (2021). Distributed Cooperative MPC for Autonomous Driving in Different Traffic Scenarios. IEEE Transactions on Intelligent Vehicles, 6(2), 299-309
Open this publication in new window or tab >>Distributed Cooperative MPC for Autonomous Driving in Different Traffic Scenarios
2021 (English)In: IEEE Transactions on Intelligent Vehicles, ISSN 2379-8904, Vol. 6, no 2, p. 299-309Article in journal (Refereed) Published
Abstract [en]

A cooperative control approach for autonomous vehicles is developed in order to perform different complex traffic maneuvers, e.g., double lane-switching or intersection situations. The problem is formulated as a distributed optimal control problem for a system of multiple autonomous vehicles and then solved using a nonlinear Model Predictive Control (MPC) technique, where the distributed approach is used to make the problem computationally feasible in real-time. To provide safety, a collision avoidance constraint is introduced, also in a distributed way. In the proposed method, each vehicle computes its own control inputs using estimated states of neighboring vehicles. In addition, a compatibility constraint is defined that takes collision avoidance into account but also ensures that each vehicle does not deviate significantly from what is expected by neighboring vehicles. The method allows us to construct a cost function for several different traffic scenarios. The asymptotic convergence of the system to the desired destination is proven, in the absence of uncertainty and disturbances, for a sufficiently small MPC control horizon. Simulation results show that the distributed algorithm scales well with increasing number of vehicles.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021
Keywords
Autonomous vehicles, Collision avoidance, Trajectory, Safety, Convergence, Control design, Optimal control, Cooperative Vehicle Systems, Model Predictive Control
National Category
Control Engineering Vehicle Engineering
Identifiers
urn:nbn:se:liu:diva-172226 (URN)10.1109/TIV.2020.3025484 (DOI)000710540200014 ()
Note

Funding: Linnaeus Center CADICS - Swedish Research Council; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation

Available from: 2020-12-28 Created: 2020-12-28 Last updated: 2021-12-28
Morsali, M., Frisk, E. & Åslund, J. (2021). Spatio-Temporal Planning in Multi-Vehicle Scenarios for Autonomous Vehicle Using Support Vector Machines. IEEE Transactions on Intelligent Vehicles, 6(4), 611-621
Open this publication in new window or tab >>Spatio-Temporal Planning in Multi-Vehicle Scenarios for Autonomous Vehicle Using Support Vector Machines
2021 (English)In: IEEE Transactions on Intelligent Vehicles, ISSN 2379-8858, Vol. 6, no 4, p. 611-621Article in journal (Refereed) Published
Abstract [en]

Efficient trajectory planning of autonomous vehiclesin complex traffic scenarios is of interest both academically andin automotive industry. Time efficiency and safety are of keyimportance and here a two-step procedure is proposed. First, aconvex optimization problem is solved, formulated as a supportvector machine (SVM), in order to represent the surroundingenvironment of the ego vehicle and classify the search spaceas obstacles or obstacle free. This gives a reduced complexitysearch space and an A* algorithm is used in a state space latticein 4 dimensions including position, heading angle and velocityfor simultaneous path and velocity planning. Further, a heuristicderived from the SVM formulation is used in the A* search anda pruning technique is introduced to significantly improve searchefficiency. Solutions from the proposed planner is compared tooptimal solutions computed using optimal control techniques.Three traffic scenarios, a roundabout scenario and two complextakeover maneuvers, with multiple moving obstacles, are used toillustrate the general applicability of the proposed method.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2021
National Category
Robotics
Identifiers
urn:nbn:se:liu:diva-173934 (URN)10.1109/TIV.2020.3042087 (DOI)000722000500004 ()2-s2.0-85097387297 (Scopus ID)
Available from: 2021-03-10 Created: 2021-03-10 Last updated: 2021-12-28Bibliographically approved
Westny, T., Frisk, E. & Olofsson, B. (2021). Vehicle Behavior Prediction and Generalization Using Imbalanced Learning Techniques. In: 24th IEEE International Intelligent Transportation Systems Conference (ITSC), 19-22 Sept. 2021: . Paper presented at IEEE International Conference on Intelligent Transportation Systems - ITSC2021, Indianapolis, IN, USA, 19-22 Sept. 2021 (pp. 2003-2010). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Vehicle Behavior Prediction and Generalization Using Imbalanced Learning Techniques
2021 (English)In: 24th IEEE International Intelligent Transportation Systems Conference (ITSC), 19-22 Sept. 2021, Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 2003-2010Conference paper, Published paper (Refereed)
Abstract [en]

The use of learning-based methods for vehicle behavior prediction is a promising research topic. However, many publicly available data sets suffer from class distribution skews which limits learning performance if not addressed. This paper proposes an interaction-aware prediction model consisting of an LSTM autoencoder and SVM classifier. Additionally, an imbalanced learning technique, the multiclass balancing ensemble is proposed. Evaluations show that the method enhances model performance, resulting in improved classification accuracy. Good generalization properties of learned models are important and therefore a generalization study is done where models are evaluated on unseen traffic data with dissimilar traffic behavior stemming from different road configurations. This is realized by using two distinct highway traffic recordings, the publicly available NGSIM US-101 and I80 data sets. Moreover, methods for encoding structural and static features into the learning process for improved generalization are evaluated. The resulting methods show substantial improvements in classification as well as generalization performance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2021
Keywords
Behavior Prediction, Machine Learning, Autonomous Vehicles, Robotics
National Category
Computer Sciences Robotics
Identifiers
urn:nbn:se:liu:diva-180481 (URN)10.1109/ITSC48978.2021.9564948 (DOI)000841862502003 ()2-s2.0-85118442722 (Scopus ID)9781728191423 (ISBN)9781728191430 (ISBN)
Conference
IEEE International Conference on Intelligent Transportation Systems - ITSC2021, Indianapolis, IN, USA, 19-22 Sept. 2021
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications, 101456
Note

Funding: Strategic Reseach Area at Linkoping-Lund in Information Technology (ELLIIT); Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Available from: 2022-01-19 Created: 2022-01-19 Last updated: 2023-04-13
Jakobsson, E., Pettersson, R., Frisk, E. & Krysander, M. (2020). Fatigue Damage Monitoring for Mining Vehicles using Data Driven Models. International Journal of Prognostics and Health Management, 11(1), Article ID 004.
Open this publication in new window or tab >>Fatigue Damage Monitoring for Mining Vehicles using Data Driven Models
2020 (English)In: International Journal of Prognostics and Health Management, E-ISSN 2153-2648, Vol. 11, no 1, article id 004Article in journal (Refereed) Published
Abstract [en]

The life and condition of a mine truck frame are related to how the machine is used. Damage from stress cycles is accumulated over time, and measurements throughout the life of the machine are needed to monitor the condition. This results in high demands on the durability of sensors, especially in a harsh mining application. To make a monitoring system cheap and robust, sensors already available on the vehicles are preferred rather than additional strain gauges. The main question in this work is whether the existing on-board sensors can give the required information to estimate stress signals and calculate accumulated damage of the frame. Model complexity requirements and sensors selection are also considered. A final question is whether the accumulated damage can be used for prognostics and to increase reliability. The investigation is performed using a large data set from two vehicles operating in real mine applications. Coherence analysis, ARX-models, and rain flow counting are techniques used. The results show that a low number of available on-board sensors like load cells, damper cylinder positions, and angle transducers can give enough information to recreate some of the stress signals measured. The models are also used to show significant differences in usage by different operators, and its effect on the accumulated damage.

Place, publisher, year, edition, pages
Rochester, NY, United States: Prognostics and Health Management Society, 2020
Keywords
Fatigue damage, System identification, Damage accumulation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-165753 (URN)10.36001/ijphm.2020.v11i1.2595 (DOI)000594760700004 ()
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2020-05-19 Created: 2020-05-19 Last updated: 2023-07-24Bibliographically approved
Voronov, S., Krysander, M. & Frisk, E. (2020). Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational Data. International Journal of Prognostics and Health Management, 11(1)
Open this publication in new window or tab >>Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational Data
2020 (English)In: International Journal of Prognostics and Health Management, E-ISSN 2153-2648, Vol. 11, no 1Article in journal (Refereed) Published
Abstract [en]

Predictive maintenance aims to predict failures in components of a system, a heavy-duty vehicle in this work, and do maintenance before any actual fault occurs. Predictive maintenance is increasingly important in the automotive industry due to the development of new services and autonomous vehicles with no driver who can notice first signs of a component problem. The lead-acid battery in a heavy vehicle is mostly used during engine starts, but also for heating and cooling the cockpit, and is an important part of the electrical system that is essential for reliable operation. This paper develops and evaluates two machine-learning based methods for battery prognostics, one based on Long Short-Term Memory (LSTM) neural networks and one on Random Survival Forest (RSF). The objective is to estimate time of battery failure based on sparse and non-equidistant vehicle operational data, obtained from workshop visits or over-the-air readouts. The dataset has three characteristics: 1) no sensor measurements are directly related to battery health, 2) the number of data readouts vary from one vehicle to another, and 3) readouts are collected at different time periods. Missing data is common and is addressed by comparing different imputation techniques. RSF- and LSTM-based models are proposed and evaluated for the case of sparse multiple-readouts. How to measure model performance is discussed and how the amount of vehicle information influences performance.

Place, publisher, year, edition, pages
Rochester, NY, United States: PHM SOCIETY, 2020
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:liu:diva-172105 (URN)000594760700008 ()
Note

Funding Agencies|FFI (Vehicle Strategic Research and Innovation); Scania CV

Available from: 2020-12-28 Created: 2020-12-28 Last updated: 2023-07-24Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7349-1937

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