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Publications (10 of 43) Show all publications
Varytimidis, D., Alonso-Fernandez, F., Englund, C. & Duran, B. (2018). Action and intention recognition of pedestrians in urban traffic. In: 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS): . Paper presented at The 14th International Conference on Signal Image Technology & Internet Based Systems (SITIS), Hotel Reina Isabel, Las Palmas de Gran Canaria, Spain, 26-29 November, 2018. Piscataway, N.J.: IEEE
Open this publication in new window or tab >>Action and intention recognition of pedestrians in urban traffic
2018 (English)In: 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Piscataway, N.J.: IEEE, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Action and intention recognition of pedestrians in urban settings are challenging problems for Advanced Driver Assistance Systems as well as future autonomous vehicles to maintain smooth and safe traffic. This work investigates a number of feature extraction methods in combination with several machine learning algorithms to build knowledge on how to automatically detect the action and intention of pedestrians in urban traffic. We focus on the motion and head orientation to predict whether the pedestrian is about to cross the street or not. The work is based on the Joint Attention for Autonomous Driving (JAAD) dataset, which contains 346 videoclips of various traffic scenarios captured with cameras mounted in the windshield of a car. An accuracy of 72% for head orientation estimation and 85% for motion detection is obtained in our experiments.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE, 2018
Keywords
Action Recognition, Intention Recognition, Pedestrian, Traffic, Driver Assistance
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-38504 (URN)
Conference
The 14th International Conference on Signal Image Technology & Internet Based Systems (SITIS), Hotel Reina Isabel, Las Palmas de Gran Canaria, Spain, 26-29 November, 2018
Projects
SIDUS AIR
Funder
Knowledge Foundation, 20140220Swedish Research CouncilVINNOVA
Note

Funding: This work is financed by the SIDUS AIR project of the Swedish Knowledge Foundation under the grant agreement number 20140220. Author F. A.-F. also thanks the Swedish Research Council (VR), and the Sweden’s innovation agency (VINNOVA).

Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-01-11Bibliographically approved
Henriksson, J., Borg, M. & Englund, C. (2018). Automotive safety and machine learning: Initial results from a study on how to adapt the ISO 26262 safety standard. In: 2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS): . Paper presented at 1st ACM/IEEE International Workshop on Software Engineering for AI in Autonomous Systems, SEFAIAS 2018 (ICSE 2018), Gothenburg, Sweden, 28 May, 2018 (pp. 47-49). New York, NY: ACM Publications
Open this publication in new window or tab >>Automotive safety and machine learning: Initial results from a study on how to adapt the ISO 26262 safety standard
2018 (English)In: 2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS), New York, NY: ACM Publications, 2018, p. 47-49Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning (ML) applications generate a continuous stream of success stories from various domains. ML enables many novel applications, also in safety-critical contexts. However, the functional safety standards such as ISO 26262 did not evolve to cover ML. We conduct an exploratory study on which parts of ISO 26262 represent the most critical gaps between safety engineering and ML development. While this paper only reports the first steps toward a larger research endeavor, we report three adaptations that are critically needed to allow ISO 26262 compliant engineering, and related suggestions on how to evolve the standard. © 2018 ACM.

Place, publisher, year, edition, pages
New York, NY: ACM Publications, 2018
Keywords
Computing methodologies, Machine learning, Software and its engineering, Software safety
National Category
Embedded Systems
Identifiers
urn:nbn:se:hh:diva-37754 (URN)10.1145/3194085.3194090 (DOI)2-s2.0-85051137851 (Scopus ID)978-1-4503-5739-5 (ISBN)978-1-5386-6261-8 (ISBN)
Conference
1st ACM/IEEE International Workshop on Software Engineering for AI in Autonomous Systems, SEFAIAS 2018 (ICSE 2018), Gothenburg, Sweden, 28 May, 2018
Projects
SMILE II
Funder
VINNOVA
Note

Funding: Vinnova/FFI and partially by the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP)

Available from: 2018-08-21 Created: 2018-08-21 Last updated: 2019-01-03Bibliographically approved
Ploeg, J., Semsar-Kazerooni, E., Morales Medina, A. I., de Jongh, J. F. C., van de Sluis, J., Voronov, A., . . . van de Wouw, N. (2018). Cooperative Automated Maneuvering at the 2016 Grand Cooperative Driving Challenge. IEEE transactions on intelligent transportation systems (Print), 19(4), 1213-1226
Open this publication in new window or tab >>Cooperative Automated Maneuvering at the 2016 Grand Cooperative Driving Challenge
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2018 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 4, p. 1213-1226Article in journal (Refereed) Published
Abstract [en]

Cooperative adaptive cruise control and platooning are well-known applications in the field of cooperative automated driving. However, extension toward maneuvering is desired to accommodate common highway maneuvers, such as merging, and to enable urban applications. To this end, a layered control architecture is adopted. In this architecture, the tactical layer hosts the interaction protocols, describing the wireless information exchange to initiate the vehicle maneuvers, supported by a novel wireless message set, whereas the operational layer involves the vehicle controllers to realize the desired maneuvers. This hierarchical approach was the basis for the Grand Cooperative Driving Challenge (GCDC), which was held in May 2016 in The Netherlands. The GCDC provided the opportunity for participating teams to cooperatively execute a highway lane-reduction scenario and an urban intersection-crossing scenario. The GCDC was set up as a competition and, hence, also involving assessment of the teams' individual performance in a cooperative setting. As a result, the hierarchical architecture proved to be a viable approach, whereas the GCDC appeared to be an effective instrument to advance the field of cooperative automated driving. © Copyright 2017 IEEE - All rights reserved.

Place, publisher, year, edition, pages
Piscataway: IEEE Press, 2018
Keywords
Cooperative driving, interaction protocol, controller design, vehicle platoons, wireless communications
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-35490 (URN)10.1109/TITS.2017.2765669 (DOI)2-s2.0-85035089916 (Scopus ID)
Projects
i-GAME
Available from: 2017-11-27 Created: 2017-11-27 Last updated: 2018-04-17Bibliographically approved
Pettersson, S., Bjärsvik, S., Englund, C., Eriksson, R., Koponen, V., Kristiansson, U., . . . Wedlin, J. (2018). Driving style comparison of plug-in hybrids and fossil fueled vehicles based on data collection of fast sampled signals. In: : . Paper presented at 31st International Electric Vehicles Symposium & Exhibition (EVS 31) & International Electric Vehicle Technology Conference 2018 (EVTeC 2018), 1-3 October 2018, Kobe, Japan.
Open this publication in new window or tab >>Driving style comparison of plug-in hybrids and fossil fueled vehicles based on data collection of fast sampled signals
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2018 (English)Conference paper, Published paper (Refereed)
National Category
Other Engineering and Technologies Signal Processing
Identifiers
urn:nbn:se:hh:diva-37752 (URN)
Conference
31st International Electric Vehicles Symposium & Exhibition (EVS 31) & International Electric Vehicle Technology Conference 2018 (EVTeC 2018), 1-3 October 2018, Kobe, Japan
Available from: 2018-08-21 Created: 2018-08-21 Last updated: 2019-01-08Bibliographically approved
Aramrattana, M., Patel, R. H., Englund, C., Härri, J., Jansson, J. & Bonnet, C. (2018). Evaluating Model Mismatch Impacting CACC Controllers in Mixed. In: 2018 IEEE Intelligent Vehicles Symposium (IV): . Paper presented at 2018 IEEE Intelligent Vehicles Symposium, IV 2018, Changshu, China, 26-30 September, 2018 (pp. 1867-1872). IEEE
Open this publication in new window or tab >>Evaluating Model Mismatch Impacting CACC Controllers in Mixed
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2018 (English)In: 2018 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2018, p. 1867-1872Conference paper, Published paper (Refereed)
Abstract [en]

At early market penetration, automated vehicles will share the road with legacy vehicles. For a safe transportation system, automated vehicle controllers therefore need to estimate the behavior of the legacy vehicles. However, mismatches between the estimated and real human behaviors can lead to inefficient control inputs, and even collisions in the worst case. In this paper, we propose a framework for evaluating the impact of model mismatch by interfacing a controller under test with a driving simulator. As a proof- of-concept, an algorithm based on Model Predictive Control (MPC) is evaluated in a braking scenario. We show how model mismatch between estimated and real human behavior can lead to a decrease in avoided collisions by almost 46%, and an increase in discomfort by almost 91%. Model mismatch is therefore non-negligible and the proposed framework is a unique method to evaluate them. © 2018 IEEE.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Behavioral research, Intelligent vehicle highway systems, Model predictive control, Predictive control systems, Vehicles, Automated vehicles, Control inputs, Driving simulator, Evaluating models, Human behaviors, Market penetration, Proof of concept, Transportation system, Controllers
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:hh:diva-38740 (URN)10.1109/IVS.2018.8500479 (DOI)2-s2.0-85056772722 (Scopus ID)978-1-5386-4452-2 (ISBN)978-1-5386-4451-5 (ISBN)978-1-5386-4453-9 (ISBN)
Conference
2018 IEEE Intelligent Vehicles Symposium, IV 2018, Changshu, China, 26-30 September, 2018
Note

Funding: Raj Haresh Patel is a recipient of a PhD Grant from the Graduate School of the University Pierre Marie Curie (UPMC), Paris. EURECOM acknowledges the support of its industrial members, namely BMW Group, IABG, Monaco Telecom, Orange, SAP and Symantec.

Available from: 2019-01-10 Created: 2019-01-10 Last updated: 2019-01-10Bibliographically approved
Chen, L. & Englund, C. (2018). Every Second Counts: Integrating Edge Computing and Service Oriented Architecture for Automatic Emergency Management. Journal of Advanced Transportation, 13, Article ID 7592926.
Open this publication in new window or tab >>Every Second Counts: Integrating Edge Computing and Service Oriented Architecture for Automatic Emergency Management
2018 (English)In: Journal of Advanced Transportation, ISSN 0197-6729, E-ISSN 2042-3195, p. 13-, article id 7592926Article in journal (Refereed) Published
Abstract [en]

Emergency management has long been recognized as a social challenge due to the criticality of the response time. In emergency situations such as severe traffic accidents, minimizing the response time, which requires close collaborations between all stakeholders involved and distributed intelligence support, leads to greater survival chance of the injured. However, the current response system is far from efficient, despite the rapid development of information and communication technologies. This paper presents an automated collaboration framework for emergency management that coordinates all stakeholders within the emergency response system and fully automates the rescue process. Applying the concept of multiaccess edge computing architecture, as well as choreography of the service oriented architecture, the system allows seamless coordination between multiple organizations in a distributed way through standard web services. A service choreography is designed to globally model the emergency management process from the time an accident occurs until the rescue is finished. The choreography can be synthesized to generate detailed specification on peer-to-peer interaction logic, and then the specification can be enacted and deployed on cloud infrastructures. © 2018 Lei Chen and Cristofer Englund.

Place, publisher, year, edition, pages
London: Hindawi Publishing Corporation, 2018
Keywords
Accidents, Architecture, Civil defense, Computation theory, Computer architecture, Disasters, Distributed computer systems, Edge computing, Emergency services, Information services, Response time (computer systems), Risk management, Specifications, Web services, Collaboration framework, Computing architecture, Distributed intelligence, Emergency response systems, Information and Communication Technologies, Multiple organizations, Peer-to-peer interaction, Service choreographies, Service oriented architecture (SOA)
National Category
Computer and Information Sciences Other Computer and Information Science
Identifiers
urn:nbn:se:hh:diva-38725 (URN)10.1155/2018/7592926 (DOI)000425437700001 ()2-s2.0-85042465118 (Scopus ID)
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-01-08Bibliographically approved
Alonso-Fernandez, F., Bigun, J. & Englund, C. (2018). Expression Recognition Using the Periocular Region: A Feasibility Study. In: : . Paper presented at The 14th International Conference on Signal Image Technology & Internet Based Systems, SITIS 2018, Las Palmas de Gran Canaria, Spain, 26-29 November, 2018.
Open this publication in new window or tab >>Expression Recognition Using the Periocular Region: A Feasibility Study
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates the feasibility of using the periocular region for expression recognition. Most works have tried to solve this by analyzing the whole face. Periocular is the facial region in the immediate vicinity of the eye. It has the advantage of being available over a wide range of distances and under partial face occlusion, thus making it suitable for unconstrained or uncooperative scenarios. We evaluate five different image descriptors on a dataset of 1,574 images from 118 subjects. The experimental results show an average/overall accuracy of 67.0%/78.0% by fusion of several descriptors. While this accuracy is still behind that attained with full-face methods, it is noteworthy to mention that our initial approach employs only one frame to predict the expression, in contraposition to state of the art, exploiting several order more data comprising spatial-temporal data which is often not available.

Keywords
Expression Recognition, Emotion Recognition, Periocular Analysis, Periocular Descriptor
National Category
Signal Processing Computer Vision and Robotics (Autonomous Systems) Medical Image Processing
Identifiers
urn:nbn:se:hh:diva-38505 (URN)
Conference
The 14th International Conference on Signal Image Technology & Internet Based Systems, SITIS 2018, Las Palmas de Gran Canaria, Spain, 26-29 November, 2018
Projects
SIDUS-AIR
Funder
Swedish Research CouncilKnowledge Foundation
Note

Funding: Author F. A.-F. thanks the Swedish Research Council for funding his research. Authors acknowledge the CAISR program and the SIDUS-AIR project of the Swedish Knowledge Foundation.

Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-01-11Bibliographically approved
Ploeg, J., Englund, C., Nijmeijer, H., Semsar-Kazerooni, E., Shladover, S. E., Voronov, A. & van de Wouw, N. (2018). Guest Editorial Introduction to the Special Issue on the 2016 Grand Cooperative Driving Challenge. IEEE transactions on intelligent transportation systems (Print), 19(4), 1208-1212
Open this publication in new window or tab >>Guest Editorial Introduction to the Special Issue on the 2016 Grand Cooperative Driving Challenge
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2018 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 4, p. 1208-1212Article in journal, Editorial material (Refereed) Published
Abstract [en]

Cooperative driving is based on wireless communications between vehicles and between vehicles and roadside infrastructure, aiming for increased traffic flow and traffic safety, while decreasing fuel consumption and emissions. To support and accelerate the introduction of cooperative vehicles in everyday traffic, in 2011, nine international teams joined the Grand Cooperative Driving Challenge (GCDC). The challenge was to perform platooning, in which vehicles drive in road trains with short intervehicle distances. The results were reported in a Special Issue of IEEE Transactions on Intelligent Transportation Systems, published in September 2012 [item 1 in the Appendix]. © 2000-2011 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2018
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-36630 (URN)10.1109/TITS.2018.2815103 (DOI)2-s2.0-85045020434 (Scopus ID)
Available from: 2018-04-18 Created: 2018-04-18 Last updated: 2018-04-18Bibliographically approved
Englund, C., Engdahl, H., Habibi, S., Pettersson, S., Sprei, F., Voronov, A. & Wedlin, J. (2018). Method for prediction of Utilization Rate of Electric Vehicle Free-Floating Car Sharing Services using Data Mining. In: 31st International Electric Vehicles Symposium & Exhibition (EVS 31) & International Electric Vehicle Technology Conference 2018 (EVTeC 2018): . Paper presented at 31st International Electric Vehicles Symposium & Exhibition (EVS 31) & International Electric Vehicle Technology Conference 2018 (EVTeC 2018), Sept. 30 - Oct. 3, 2018, Kobe, Japan.
Open this publication in new window or tab >>Method for prediction of Utilization Rate of Electric Vehicle Free-Floating Car Sharing Services using Data Mining
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2018 (English)In: 31st International Electric Vehicles Symposium & Exhibition (EVS 31) & International Electric Vehicle Technology Conference 2018 (EVTeC 2018), 2018Conference paper, Oral presentation only (Refereed)
Abstract [en]

Free-floating car sharing is a form of car rental used by people for short periods of time where the cars can be picked up and returned anywhere within a given area. In this paper, we have collected free-floating car sharing data, for electric as well as fossil fueled cars, and data regarding e.g. size of the city, number of cars in the service, etc. The utilization rates of the free-floating car sharing services vary much between the cities, greatly influencing the success of the services. This paper presents the most important factors influencing the utilization rate, and also a methodology to predict the utilization rate for new cities, using data mining based on Random Forests.© EVS 31 & EVTeC 2018.

National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-37751 (URN)
Conference
31st International Electric Vehicles Symposium & Exhibition (EVS 31) & International Electric Vehicle Technology Conference 2018 (EVTeC 2018), Sept. 30 - Oct. 3, 2018, Kobe, Japan
Available from: 2018-08-21 Created: 2018-08-21 Last updated: 2018-10-31Bibliographically approved
Rosenstatter, T. & Englund, C. (2018). Modelling the Level of Trust in a Cooperative Automated Vehicle Control System. IEEE transactions on intelligent transportation systems (Print), 19(4), 1237-1247
Open this publication in new window or tab >>Modelling the Level of Trust in a Cooperative Automated Vehicle Control System
2018 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 4, p. 1237-1247Article in journal (Refereed) Published
Abstract [en]

Vehicle-to-vehicle communication is a key technology for achieving increased perception for automated vehicles, where the communication enables virtual sensing by means of sensors in other vehicles. In addition, this technology also allows detection and recognition of objects that are out-of-sight. This paper presents a trust system that allows a cooperative and automated vehicle to make more reliable and safe decisions. The system evaluates the current situation and generates a trust index indicating the level of trust in the environment, the ego vehicle, and the surrounding vehicles. This research goes beyond secure communication and concerns the verification of the received data on a system level. The results show that the proposed method is capable of correctly identifying various traffic situations and how the trust index is used while manoeuvring in a platoon merge scenario. © Copyright 2017 IEEE - All rights reserved.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2018
Keywords
GCDC 2016, autonomous driving, cooperative driving, vehicle-to-vehicle communication, trust, reliability
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-35483 (URN)10.1109/TITS.2017.2749962 (DOI)2-s2.0-85030752579 (Scopus ID)
Available from: 2017-11-27 Created: 2017-11-27 Last updated: 2018-04-03Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1043-8773

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