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Träfflista för sökning "WFRF:(Englund Cristofer 1977 ) "

Sökning: WFRF:(Englund Cristofer 1977 )

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3.
  • Abella, J., et al. (författare)
  • SAFEXPLAIN : Safe and Explainable Critical Embedded Systems Based on AI
  • 2023
  • Ingår i: Proceedings -Design, Automation and Test in Europe, DATE. - : Institute of Electrical and Electronics Engineers Inc.. - 9783981926378
  • Konferensbidrag (refereegranskat)abstract
    • Deep Learning (DL) techniques are at the heart of most future advanced software functions in Critical Autonomous AI-based Systems (CAIS), where they also represent a major competitive factor. Hence, the economic success of CAIS industries (e.g., automotive, space, railway) depends on their ability to design, implement, qualify, and certify DL-based software products under bounded effort/cost. However, there is a fundamental gap between Functional Safety (FUSA) requirements on CAIS and the nature of DL solutions. This gap stems from the development process of DL libraries and affects high-level safety concepts such as (1) explainability and traceability, (2) suitability for varying safety requirements, (3) FUSA-compliant implementations, and (4) real-time constraints. As a matter of fact, the data-dependent and stochastic nature of DL algorithms clashes with current FUSA practice, which instead builds on deterministic, verifiable, and pass/fail test-based software. The SAFEXPLAIN project tackles these challenges and targets by providing a flexible approach to allow the certification - hence adoption - of DL-based solutions in CAIS building on: (1) DL solutions that provide end-to-end traceability, with specific approaches to explain whether predictions can be trusted and strategies to reach (and prove) correct operation, in accordance to certification standards; (2) alternative and increasingly sophisticated design safety patterns for DL with varying criticality and fault tolerance requirements; (3) DL library implementations that adhere to safety requirements; and (4) computing platform configurations, to regain determinism, and probabilistic timing analyses, to handle the remaining non-determinism.
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4.
  • Abella, Jaume, et al. (författare)
  • SAFEXPLAIN : Safe and Explainable Critical Embedded Systems Based on AI
  • 2023
  • Ingår i: DATE 23: Design, Automation And Test In Europe. - 9783981926378 ; , s. 1-6
  • Konferensbidrag (refereegranskat)abstract
    • Deep Learning (DL) techniques are at the heart of most future advanced software functions in Critical Autonomous AI-based Systems (CAIS), where they also represent a major competitive factor. Hence, the economic success of CAIS industries (e.g., automotive, space, railway) depends on their ability to design, implement, qualify, and certify DL-based software products under bounded effort/cost. However, there is a fundamental gap between Functional Safety (FUSA) requirements on CAIS and the nature of DL solutions. This gap stems from the development process of DL libraries and affects high-level safety concepts such as (1) explainability and traceability, (2) suitability for varying safety requirements, (3) FUSA-compliant implementations, and (4) real-time constraints. As a matter of fact, the data-dependent and stochastic nature of DL algorithms clashes with current FUSA practice, which instead builds on deterministic, verifiable, and pass/fail test-based software. The SAFEXPLAIN project tackles these challenges and targets by providing a flexible approach to allow the certification - hence adoption - of DL-based solutions in CAIS building on: (1) DL solutions that provide end-to-end traceability, with specific approaches to explain whether predictions can be trusted and strategies to reach (and prove) correct operation, in accordance to certification standards; (2) alternative and increasingly sophisticated design safety patterns for DL with varying criticality and fault tolerance requirements; (3) DL library implementations that adhere to safety requirements; and (4) computing platform configurations, to regain determinism, and probabilistic timing analyses, to handle the remaining non-determinism. © 2023 EDAA.
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5.
  • Andersson, Jonas, et al. (författare)
  • Hello human, can you read my mind?
  • 2017
  • Ingår i: ERCIM News. - Sophia Antipolis Cedex : European Research Consortium for Informatics and Mathematics (ERCIM). - 0926-4981 .- 1564-0094. ; :109, s. 36-37
  • Forskningsöversikt (refereegranskat)abstract
    • For safety reasons, autonomous vehicles should communicate their intent rather than explicitly invite people to act. At RISE Viktoria in Sweden, we believe this simple design principle will impact how autonomous vehicles are experienced in the future.
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6.
  • Andersson, Jonas, et al. (författare)
  • Study of communication needs ininteraction between trucks and surrounding traffic in platooning
  • 2017
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • Platooning will soon likely to be common on Swedish roads and the potential for fuel  savings in the transport sector is high. This pre-study project explores the need for  external signaling in platoons to avoid any cut-ins from surrounding vehicles whose   drivers are unaware that their actions may cause a loss of fuel saving. Interviews with  truck drivers created an understanding of how they experience the behavior of the  surrounding traffic. The scenarios that are highlighted where unaware cut-ins may occur  are mainly on-ramps and while overtaking on highway. Car drivers highlighted that overtaking may be a problem, especially on 2+1 roads. Communication needs elicited in workshops with drivers mainly concerned the movement patterns and properties of vehicles, e.g. speed, direction, gaps and length of the platoon. Barriers that were identified for external signaling is that trailers are constantly rotating between different  tractors. This may require that more trailers than tractors need to be equipped with  communication devices. To evaluate the potential impact of external signaling simulation  could be used, where a driving simulator could be used to evaluate the perception of car- and truck drivers. Different means of communication, behavior, driving close together or lighting could be subject to evaluation. The long-term learning effect and behavioral adaptation to platooning in traffic is also important to study. It was found that there are large regional behavioral differences in traffic. Naturalistic data from the  US, indicate that there are no cut-ins if the distance between trucks is < 30 m. In Europe, the data collected from ETPC indicate that there is up to one cut-in every 15 km on highways. The data from the ETPC is however very sparse compared to the US study. In Sweden, it does not seem to be a specific need for external signaling since very few cut-ins occur. In Europe, more cut-ins occur and external signaling could help to save fuel. It is however unclear what long-term effects external signaling may have. Further studies are suggested to study if short platooning distance (10-20 meters) is sufficient to deter surrounding traffic from cut-ins.
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7.
  • Aramrattana, Maytheewat, 1988-, et al. (författare)
  • A Novel Risk Indicator for Cut-In Situations
  • 2020
  • Ingår i: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020. - Piscataway, NJ : Institute of Electrical and Electronics Engineers Inc.. - 9781728141497 - 9781728141503
  • Konferensbidrag (refereegranskat)abstract
    • Cut-in situations occurs when a vehicle intentionally changes lane and ends up in front of another vehicle or in-between two vehicles. In such situations, having a method to indicate the collision risk prior to making the cut-in maneuver could potentially reduce the number of sideswipe and rear end collisions caused by the cut-in maneuvers. This paper propose a new risk indicator, namely cut-in risk indicator (CRI), as a way to indicate and potentially foresee collision risks in cut-in situations. As an example use case, we applied CRI on data from a driving simulation experiment involving a manually driven vehicle and an automated platoon in a highway merging situation. We then compared the results with time-to-collision (TTC), and suggest that CRI could correctly indicate collision risks in a more effective way. CRI can be computed on all vehicles involved in the cut-in situations, not only for the vehicle that is cutting in. Making it possible for other vehicles to estimate the collision risk, for example if a cut-in from another vehicle occurs, the surrounding vehicles could be warned and have the possibility to react in order to potentially avoid or mitigate accidents. 
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8.
  • Aramrattana, Maytheewat, 1988- (författare)
  • A Simulation-Based Safety Analysis of CACC-Enabled Highway Platooning
  • 2018
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Cooperative Intelligent Transport Systems (C-ITS) enable actors in the transport systems to interact and collaborate by exchanging information via wireless communication networks. There are several challenges to overcome before they can be implemented and deployed on public roads. Among the most important challenges are testing and evaluation in order to ensure the safety of C-ITS applications.This thesis focuses on testing and evaluation of C-ITS applications with regard to their safety using simulation. The main focus is on one C-ITS application, namely platooning, that is enabled by the Cooperative Adaptive Cruise Control (CACC) function. Therefore, this thesis considers two main topics: i) what should be modelled and simulated for testing and evaluation of C-ITS applications? and ii) how should CACC functions be evaluated in order to ensure safety?When C-ITS applications are deployed, we can expect traffic situations which consist of vehicles with different capabilities, in terms of automation and connectivity. We propose that involving human drivers in testing and evaluation is important in such mixed traffic situations. Considering important aspects of C-ITS including human drivers, we propose a simulation framework, which combines driving-, network-, and traffic simulators. The simulation framework has been validated by demonstrating several use cases in the scope of platooning. In particular, it is used to demonstrate and analyse the safety of platooning applications in cut-in situations, where a vehicle driven by a human driver cuts in between vehicles in platoon. To assess the situations, time-to-collision (TTC) and its extensions are used as safety indicators in the analyses.The simulation framework permits future C-ITS research in other fields such as human factors by involving human drivers in a C-ITS context. Results from the safety analyses show that cut-in situations are not always hazardous, and two factors that are the most highly correlated to the collisions are relative speed and distance between vehicles at the moment of cutting in. Moreover, we suggest that to solely rely on CACC functions is not sufficient to handle cut-in situations. Therefore, guidelines and standards are required to address these situations properly.
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9.
  • Aramrattana, Maytheewat, et al. (författare)
  • A Simulation Study on Effects of Platooning Gaps on Drivers of Conventional Vehicles in Highway Merging Situations
  • 2021
  • Ingår i: IEEE transactions on intelligent transportation systems (Print). - Piscataway, NJ : Institute of Electrical and Electronics Engineers Inc.. - 1524-9050 .- 1558-0016.
  • Tidskriftsartikel (refereegranskat)abstract
    • Platooning refers to a group of vehicles that--enabled by wireless vehicle-to-vehicle (V2V) communication and vehicle automation--drives with short inter-vehicular distances. Before its deployment on public roads, several challenging traffic situations need to be handled. Among the challenges are cut-in situations, where a conventional vehicle--a vehicle that has no automation or V2V communication--changes lane and ends up between vehicles in a platoon. This paper presents results from a simulation study of a scenario, where a conventional vehicle, approaching from an on-ramp, merges into a platoon of five cars on a highway. We created the scenario with four platooning gaps: 15, 22.5, 30, and 42.5 meters. During the study, the conventional vehicle was driven by 37 test persons, who experienced all the platooning gaps using a driving simulator. The participants' opinions towards safety, comfort, and ease of driving between the platoon in each gap setting were also collected through a questionnaire. The results suggest that a 15-meter gap prevents most participants from cutting in, while causing potentially dangerous maneuvers and collisions when cut-in occurs. A platooning gap of at least 30 meters yield positive opinions from the participants, and facilitating more smooth cut-in maneuvers while less collisions were observed. 
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10.
  • Aramrattana, Maytheewat, et al. (författare)
  • Dimensions of cooperative driving, ITS and automation
  • 2015
  • Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. - Piscataway, NJ : IEEE Press. - 9781467372664 ; , s. 144-149
  • Konferensbidrag (refereegranskat)abstract
    • Wireless technology supporting vehicle-to-vehicle (V2V), and vehicle-to-infrastructure (V2I) communication, allow vehicles and infrastructures to exchange information, and cooperate. Cooperation among the actors in an intelligent transport system (ITS) can introduce several benefits, for instance, increase safety, comfort, efficiency.Automation has also evolved in vehicle control and active safety functions. Combining cooperation and automation would enable more advanced functions such as automated highway merge and negotiating right-of-way in a cooperative intersection. However, the combination have influences on the structure of the overall transport systems as well as on its behaviour. In order to provide a common understanding of such systems, this paper presents an analysis of cooperative ITS (C-ITS) with regard to dimensions of cooperation. It also presents possible influence on driving behaviour and challenges in deployment and automation of C-ITS.
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11.
  • Aramrattana, Maytheewat, et al. (författare)
  • Evaluating Model Mismatch Impacting CACC Controllers in Mixed
  • 2018
  • Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781538644522 - 9781538644515 - 9781538644539 ; , s. 1867-1872
  • Konferensbidrag (refereegranskat)abstract
    • 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.
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12.
  • Aramrattana, Maytheewat, 1988-, et al. (författare)
  • Evaluating Model Mismatch Impacting CACC Controllers in Mixed Traffic using a Driving Simulator
  • 2018
  • Ingår i: 2018 IEEE Intelligent Vehicles Symposium (IV). - New York, NY : IEEE. - 9781538644522 - 9781538644515 - 9781538644539 ; , s. 1867-1872
  • Konferensbidrag (refereegranskat)abstract
    • 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.
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13.
  • Aramrattana, Maytheewat, et al. (författare)
  • Safety and experience of other drivers while interacting with automated vehicle platoons
  • 2021
  • Ingår i: Transportation Research Interdisciplinary Perspectives. - Oxford : Elsevier Ltd. - 2590-1982. ; 10
  • Tidskriftsartikel (refereegranskat)abstract
    • It is currently unknown how automated vehicle platoons will be perceived by other road users in their vicinity. This study explores how drivers of manually operated passenger cars interact with automated passenger car platoons while merging onto a highway, and how different inter-vehicular gaps between the platooning vehicles affect their experience and safety. The study was conducted in a driving simulator and involved 16 drivers of manually operated cars. Our results show that the drivers found the interactions mentally demanding, unsafe, and uncomfortable. They commonly expected that the platoon would adapt its behavior to accommodate a smooth merge. They also expressed a need for additional information about the platoon to easier anticipate its behavior and avoid cutting-in. This was, however, affected by the gap size; larger gaps (30 and 42.5 m) yielded better experience, more frequent cut-ins, and less crashes than the shorter gaps (15 and 22.5 m). A conclusion is that a short gap as well as external human–machine interfaces (eHMI) might be used to communicate the platoon's intent to “stay together”, which in turn might prevent drivers from cutting-in. On the contrary, if the goal is to facilitate frequent, safe, and pleasant cut-ins, gaps larger than 22.5 m may be suitable. To thoroughly inform such design trade-offs, we urge for more research on this topic. © 2021 The Author(s)
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14.
  • Aramrattana, Maytheewat, 1988-, et al. (författare)
  • Safety Evaluation of Highway Platooning Under a Cut-In Situation Using Simulation
  • Annan publikation (populärvet., debatt m.m.)abstract
    • Platooning refers to an application, where a group of connected and automated vehicles follow a lead vehicle autonomously, with short inter-vehicular distances. At merging points on highways such as on-ramp, platoons could encounter manually driven vehicles, which are merging on to the highways. In some situations, the manually driven vehicles could end up between the platooning vehicles. Such situations are expected and known as “cut-in” situations. This paper presents a simulation study of a cut-in situation, where a platoon of five vehicles encounter a manually driven vehicle at a merging point of a highway. The manually driven vehicle is driven by 37 test persons using a driving simulator. For the platooning vehicles, two longitudinal controllers with four gap settings between the platooning vehicles, i.e. 15 meters, 22.5 meters, 30 meters, and 42.5 meters, are evaluated. Results summarizing cut-in behaviours and how the participants perceived the situation are presented. Furthermore, the situation is assessed using safety indicators based on time-to-collision.
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15.
  • Aramrattana, Maytheewat, 1988-, et al. (författare)
  • Team Halmstad Approach to Cooperative Driving in the Grand Cooperative Driving Challenge 2016
  • 2018
  • Ingår i: IEEE transactions on intelligent transportation systems (Print). - Piscataway, N.J. : Institute of Electrical and Electronics Engineers Inc.. - 1524-9050 .- 1558-0016. ; 19:4, s. 1248-1261
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper is an experience report of team Halmstad from the participation in a competition organised by the i-GAME project, the Grand Cooperative Driving Challenge 2016. The competition was held in Helmond, The Netherlands, during the last weekend of May 2016. We give an overview of our car’s control and communication system that was developed for the competition following the requirements and specifications of the i-GAME project. In particular, we describe our implementation of cooperative adaptive cruise control, our solution to the communication and logging requirements, as well as the high level decision making support. For the actual competition we did not manage to completely reach all of the goals set out by the organizers as well as ourselves. However, this did not prevent us from outperforming the competition. Moreover, the competition allowed us to collect data for further evaluation of our solutions to cooperative driving. Thus, we discuss what we believe were the strong points of our system, and discuss post-competition evaluation of the developments that were not fully integrated into our system during competition time. © 2000-2011 IEEE.
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16.
  • Arvidsson, Moa, et al. (författare)
  • Drone Navigation and License Place Detection for Vehicle Location in Indoor Spaces
  • 2024
  • Ingår i: Lect. Notes Comput. Sci.. - Heidelberg : Springer Science and Business Media Deutschland GmbH. - 9783031495519 - 9783031495526 ; , s. 362-374
  • Konferensbidrag (refereegranskat)abstract
    • Millions of vehicles are transported every year, tightly parked in vessels or boats. To reduce the risks of associated safety issues like fires, knowing the location of vehicles is essential, since different vehicles may need different mitigation measures, e.g. electric cars. This work is aimed at creating a solution based on a nano-drone that navigates across rows of parked vehicles and detects their license plates. We do so via a wall-following algorithm, and a CNN trained to detect license plates. All computations are done in real-time on the drone, which just sends position and detected images that allow the creation of a 2D map with the position of the plates. Our solution is capable of reading all plates across eight test cases (with several rows of plates, different drone speeds, or low light) by aggregation of measurements across several drone journeys. 
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17.
  • Autili, Marco, et al. (författare)
  • Cooperative Intelligent Transport Systems : Choreography-Based Urban Traffic Coordination
  • 2021
  • Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers Inc.. - 1524-9050 .- 1558-0016. ; 22:4, s. 2088-2099
  • Tidskriftsartikel (refereegranskat)abstract
    • With the emerging connected automated vehicles, 5G and Internet of Things (IoT), vehicles and road infrastructure become connected and cooperative, enabling Cooperative Intelligent Transport Systems (C-ITS). C-ITS are transport system of systems that involves many stakeholders from different sectors. While running their own systems and providing services independently, stakeholders cooperate with each other for improving the overall transport performance such as safety, efficiency and sustainability. Massive information on road and traffic is already available and provided through standard services with different protocols. By reusing and composing the available heterogeneous services, novel value-added applications can be developed. This paper introduces a choreography-based service composition platform, i.e. the CHOReVOLUTION Integrated Development and Runtime Environment (IDRE), and it reports on how the IDRE has been successfully exploited to accelerate the reuse-based development of a choreography-based Urban Traffic Coordination (UTC) application. The UTC application takes the shape of eco-driving services that through real-time eco-route evaluation assist the drivers for the most eco-friendly and comfortable driving experience. The eco-driving services are realized through choreography and they are exploited through a mobile app for online navigation. From specification to deployment to execution, the CHOReVOLUTION IDRE has been exploited to support the realization of the UTC application by automatizing the generation of the distributed logic to properly bind, coordinate and adapt the interactions of the involved parties. The benefits brought by CHOReVOLUTION IDRE have been assessed through the evaluation of a set of Key Performance Indicators (KPIs).
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18.
  • Bengtsson, Hoai, et al. (författare)
  • Interaction Protocol for Highway Platoon Merge
  • 2015
  • Ingår i: 2015 IEEE 18th International Conference on Intelligent Transportation Systems. - Los Alamitos : IEEE. - 9781467365963 - 9781467365956 ; , s. 1971-1976
  • Konferensbidrag (refereegranskat)abstract
    • An interaction protocol for cooperative platoon merge on highways is proposed. The interaction protocol facilitates a challenge scenario for the Grand Cooperative Driving Challenge (GCDC) 2016, where two platoons running on separate lanes merge into one platoon due to a roadwork in one of the lanes. Detailed interaction procedures, described with state machines of each vehicle are presented. A communication message set is designed to support platoon controllers to perform safe and efficient manoeuvres. © 2015 IEEE.
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19.
  • Chen, Lei, et al. (författare)
  • Coordinating dangerous goods vehicles : C-ITS applications for safe road tunnels
  • 2015
  • Ingår i: 2015 IEEE Intelligent Vehicles Symposium (IV). - Piscataway, NJ : IEEE. - 9781467372664 - 9781467372657 ; , s. 156-161
  • Konferensbidrag (refereegranskat)abstract
    • Despite the existing regulation efforts and measures, vehicles with dangerous goods still pose significant risks on public safety, especially in road tunnels. Solutions based on cooperative intelligent transportation system (C-ITS) are promising measures, however, they have received limited attention. We propose C-ITS applications that coordinate dangerous goods vehicles to minimize the risk by maintaining safe distances between them in road tunnels. Different mechanisms, including global centralized coordination, global distributed coordination, and local coordination, are proposed and investigated. A preliminary simulation is performed and demonstrates their effectiveness. © 2015 IEEE.
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20.
  • Chen, Lei, et al. (författare)
  • Federated Learning to Enable Automotive Collaborative Ecosystem : Opportunities and Challenges
  • 2020
  • Ingår i: Proceedings of Virtual ITS European Congress.
  • Konferensbidrag (refereegranskat)abstract
    • Despite the strong interests in creating data economy, automotive industries are creating data silos with each stakeholder maintaining its own data cloud. Federated learning (FL), designed for privacy-preserving collaborative Machine Learning (ML), offers a promising method that allows multiple stakeholders to share information through ML models without the exposure of raw data, thus natively protecting privacy. Motivated by the strong need for automotive collaboration and the advancement of FL, this paper investigates how FL could enable privacy-preserving information sharing for automotive industries. We first introduce the statuses and challenges for automotive data sharing, followed by a brief introduction to FL. We then present a comprehensive discussion on potential applications of federated learning to enable an automotive collaborative ecosystem. To illustrate the benefits, we apply FL for driver action classification and demonstrate the potential for collaborative machine learning without data sharing.
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21.
  • Cristofer, Englund, 1977-, et al. (författare)
  • Future Applications of VANETs
  • 2015
  • Ingår i: Vehicular Ad Hoc Networks. - Cham : Springer Publishing Company. - 9783319154961 - 9783319154978 ; , s. 525-544
  • Bokkapitel (refereegranskat)abstract
    • Current transportation systems face great challenges due to the increasing mobility. Traffic accidents, congestion, air pollution, etc., are all calling for new methods to improve the transportation system. With the US legislation in progress over vehicle communications and EU’s finalization of the basic set of standards over cooperative intelligent transportation systems (C-ITS), vehicular ad hoc network (VANET) based applications are expected to address those challenges and provide solutions for a safer, more efficient and sustainable future intelligent transportation systems (ITS). In this chapter, transportation challenges are firstly summarized in respect of safety, efficiency, environmental threat, etc. A brief introduction of the VANET is discussed along with state of the art of VANET-based applications. Based on the current progress and the development trend of VANET, a number of new features of future VANET are identified, together with a set of potential future ITS applications. The on-going research and field operational test projects, which are the major enabling efforts for the future VANET-based C-ITS, are presented. The chapter is of great interest to readers working within ITS for current development status and future trend within the C-ITS area. It is also of interest to general public for an overview of the VANET enabled future transportation system. © Springer International Publishing Switzerland 2015.
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22.
  • Englund, Cristofer, 1977-, et al. (författare)
  • A new method for ground vehicle access control and situation awareness : experiences from a real-life implementation at an airport
  • 2017
  • Konferensbidrag (refereegranskat)abstract
    • To improve safety in complex traffic situations, access control can be applied. This paper presents a generic vehicle access control method for improved situation awareness. The method concerns three main steps (i) zones definition (ii) rules to manage access and (iii) situation awareness based on realtime position monitoring. The proposed system consists of a server where the access zones and rules are stored and mobile units providing position data to the server and information to the driver. At the control center a client control unit is used to provide improved situation awareness by monitoring and visualizing the positions of the clients in the vehicles. The client in the control center is also utilized to give access to the clients in the vehicles that request access. The system has been demonstrated at an airport to grant access for ground vehicles to enter the runway and has since been developed into a commercial product by an industrial supplier. It was introduced at the World ATM Congress in Madrid in March of 2017. The server system is implemented as a cloud service in Microsoft Azure, the control client uses a WACOM CINTIQ touch screen computer for interaction and the vehicle clients are off-the-shelf Samsung Android units paired with Trimble R1GNSS receiver and 4G mobile communication between the server and the clients.
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23.
  • Englund, Cristofer, 1977- (författare)
  • Action intention recognition of cars and bicycles in intersections
  • 2020
  • Ingår i: International Journal of Vehicle Design. - : Inderscience Publishers. - 0143-3369 .- 1741-5314. ; 83:2-4, s. 103-121
  • Tidskriftsartikel (refereegranskat)abstract
    • Copyright © 2020 Inderscience Enterprises Ltd.Action intention recognition is becoming increasingly important in the road vehicle automation domain. Autonomous vehicles must be aware of their surroundings if we are to build safe and efficient transport systems. This paper presents a method for predicting the action intentions of road users based on sensors in the road infrastructure. The scenarios used for demonstration are recorded on two different public road sections. The first scenario includes bicyclists and the second includes cars that are driving in a road approaching an intersection where they are either leaving or continuing straight. A 3D camera-based data acquisition system is used to collect trajectories of the road users that are used as input for models trained to predict the action intention of the road users. The proposed system enables future connected and automated vehicles to receive collision warnings from an infrastructure-based sensor system well in advance to enable better planning.
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24.
  • Englund, Cristofer, 1977-, et al. (författare)
  • AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control
  • 2021
  • Ingår i: Smart Cities. - Basel : MDPI. - 2624-6511. ; 4:2, s. 783-802
  • Tidskriftsartikel (refereegranskat)abstract
    • Smart Cities and Communities (SCC) constitute a new paradigm in urban development. SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with internet of things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, Smart Traffic Control and Driver Modelling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, the availability of data from different stakeholders is need. Further, though AI technologies provide accurate predictions and classifications there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability, while the models have difficulties explaining how they come to a certain conclusions it is difficult for humans to trust it. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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25.
  • Englund, Cristofer, 1977- (författare)
  • Aware and intelligent infrastructure for action intention recognition of cars and bicycles
  • 2020
  • Ingår i: VEHITS 2020 - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems. - : SciTePress. - 9789897584190 ; , s. 281-288
  • Konferensbidrag (refereegranskat)abstract
    • Action intention recognition is becoming increasingly important in the road vehicle automation domain. Autonomous vehicles must be aware of their surroundings if we are to build safe and efficient transport systems. This paper explores methods for predicting the action intentions of road users based on an aware and intelligent 3D camera-based sensor system. The collected data contains trajectories of two different scenarios. The first one includes bicyclists and the second cars that are driving in a road approaching an intersection where they are either turning or continuing straight. The data acquisition system is used to collect trajectories of the road users that are used as input for models trained to predict the action intention of the road users.
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26.
  • Englund, Cristofer, 1977-, et al. (författare)
  • Cooperative speed harmonization for efficient road utilization
  • 2014
  • Ingår i: 2014 7th International Workshop on Communication Technologies for Vehicles, Nets4Cars-Fall 2014. - : Institute of Electrical and Electronics Engineers Inc.. - 9781479952700 ; , s. 19-23
  • Konferensbidrag (refereegranskat)abstract
    • Cooperative speed harmonization based on floating car data aiming at improving manoeuvrability in a highly utilized intersection is presented. Cooperative Intelligent Transportation Systems (C-ITS) aims at gather information about the current traffic situation based on wireless communication and provide aggregated information back to the road users in order to improve e.g. efficiency, safety and/or comfort. Simulations show that the proposed speed harmonization application is capable of lowering the CO2 emissions with up to 11%, increasing the average speed with up to 14% and reducing the travel time with up to 16% for all vehicles in the simulation. It is also found that not only the cooperative vehicles benefit from the application but also the non-equipped vehicles. Furthermore, the cooperative traffic simulator has been shown to be a valuable tool for investigating how C-ITS applications may be utilized to develop future traffic system.
  •  
27.
  • Englund, Cristofer, 1977-, et al. (författare)
  • Enabling Technologies for Road Vehicle Automation
  • 2017
  • Ingår i: Road Vehicle Automation 4. - Leiden : VSP. - 9783319609331 - 9783319609348 ; , s. 177-185, s. 177-185
  • Bokkapitel (refereegranskat)abstract
    • Technology is to a large extent driving the development of road vehicle automation. This Chapter summarizes the general overall trends in the enabling technologies within this field that were discussed during the Enabling technologies for road vehicle automation breakout session at the Automated Vehicle Symposium 2016. With a starting point in six scenarios that have the potential to be deployed at an early stage, five different categories of emerging technologies are described: (a) positioning, localization and mapping (b) algorithms, deep learning techniques, sensor fusion guidance and control (c) hybrid communication (d) sensing and perception and (e) technologies for data ownership and privacy. It is found that reliability and extensive computational power are the two most common challenges within the emerging technologies. Furthermore, cybersecurity binds all technologies together as vehicles will be constantly connected. Connectivity allows both improved local awareness through vehicle-to-vehicle communication and it allows continuous deployment of new software and algorithms that constantly learns new unforeseen objects or scenarios. Finally, while five categories were individually considered, further holistic work to combine them in a systems concept would be the important next step toward implementation. © Springer International Publishing AG 2018
  •  
28.
  • Englund, Cristofer, et al. (författare)
  • Method for prediction of utilization rate of electric vehicle free-floating car sharing services using data mining
  • 2018
  • Ingår i: 31st International Electric Vehicle Symposium and Exhibition, EVS 2018 and International Electric Vehicle Technology Conference 2018, EVTeC 2018.
  • Konferensbidrag (refereegranskat)abstract
    • 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.
  •  
29.
  •  
30.
  • Englund, Cristofer, 1977-, et al. (författare)
  • The Grand Cooperative Driving Challenge 2016 : Boosting the Introduction of Cooperative Automated Vehicles
  • 2016
  • Ingår i: IEEE wireless communications. - Piscataway : IEEE. - 1536-1284 .- 1558-0687. ; 23:4, s. 146-152
  • Tidskriftsartikel (refereegranskat)abstract
    • The Grand Cooperative Driving Challenge (GCDC), with the aim to boost the introduction of cooperative automated vehicles by means of wireless communication, is presented. Experiences from the previous edition of GCDC, which was held in Helmond in the Netherlands in 2011, are summarized, and an overview and expectations of the challenges in the 2016 edition are discussed. Two challenge scenarios, cooperative platoon merge and cooperative intersection passing, are specified and presented. One demonstration scenario for emergency vehicles is designed to showcase the benefits of cooperative driving. Communications closely follow the newly published cooperative intelligent transport system standards, while interaction protocols are designed for each of the scenarios. For the purpose of interoperability testing, an interactive testing tool is designed and presented. A general summary of the requirements on teams for participating in the challenge is also presented.
  •  
31.
  • Habibi, Shiva, 1978, et al. (författare)
  • Comparison of free-floating car-sharing services in cities
  • 2017
  • Ingår i: ECEEE Summer Study, 29 May - 3 June, 2017. - 2001-7960. - 9789198387810 ; 2017
  • Konferensbidrag (refereegranskat)abstract
    • In recent years, free-floating car sharing services (FFCS) have been offered by many organizations as a more flexible option compared to traditional car sharing. FFCS allows users to pick up and return cars anywhere within a specified area of a city. FFCS can provide a high degree of utilization of vehicles and less usage of infrastructure in the form of parking lots and roads and thus has the potential to increase the efficiency of the transport sector. However, there is also a concern that these compete with other efficient modes of transport such as cycling and public transport. The aim of this paper is to better understand how, when and where the vehicles are utilized through logged data of the vehicles’ movements. We have access to data collected on FFCS services in 22 cities in Europe and North America which allows us to compare the usage pattern in different cities and examine whether or not there are similar trends. In this paper, we use the collected data to compare the different cities based on utilization rate, length of trip and time of day that the trip is made. We find that the vehicle utilization rates differ between cities with Madrid and Hamburg having some of the highest utilization levels for the FFCS vehicles. The results form a first step of a better understanding on how these services are being used and can provide valuable input to local policy makers as well as future studies such as simulation models.
  •  
32.
  • Habibi, Shiva, et al. (författare)
  • Success and usage pattern of free-floating car sharing services in cities
  • 2018
  • Ingår i: Transportation Research Board (TRB) Meeting 2018.
  • Konferensbidrag (refereegranskat)abstract
    • Free-floating car sharing services (FFCS) have been offered as a more flexible mobility solution than other car sharing services. FFCS users can pick up and return cars anywhere within a specified area in a city.The objective of this paper is to identify similar usage patterns of FFCS in different cities as well as city characteristics that make these services a viable option. The authors have access to real booking data for 32 cities in Europe and North America. Their study shows the share of daily car trips is negatively correlated to the utilization rate of these services. Also, the higher the congestion and the harder finding a parking lot, the lower the utilization rate of these services is in the cities. Moreover, our results suggest that FFCS services do not compete with public transport but are rather used in combination to it. These services are mainly used during midday and evening peak and the trips taken by these services are mainly chained trips.The clustering analysis shows that the trips are grouped into two or three clusters in different cities. The majority of clusters are the inner city clusters which contain a significantly higher number of trips than the clusters around other points of interest such as airports. © Conference Compass and Transportation Research Board
  •  
33.
  • Habibovic, Azra, et al. (författare)
  • External Vehicle Interfaces for Communication with Other Road Users?
  • 2019
  • Ingår i: Road Vehicle Automation 5. - Cham : Springer. - 9783319948959 - 9783319948966 ; , s. 91-102
  • Bokkapitel (refereegranskat)abstract
    • How to ensure trust and societal acceptance of automated vehicles (AVs) is a widely-discussed topic today. While trust and acceptance could be influenced by a range of factors, one thing is sure: the ability of AVs to safely and smoothly interact with other road users will play a key role. Based on our experiences from a series of studies, this paper elaborates on issues that AVs may face in interactions with other road users and whether external vehicle interfaces could support these interactions. Our overall conclusion is that such interfaces may be beneficial in situations where negotiation is needed. However, these benefits, and potential drawbacks, need to be further explored to create a common language, or standard, for how AVs should communicate with other road users.
  •  
34.
  • Henriksson, Jens, 1991, et al. (författare)
  • Automotive safety and machine learning : Initial results from a study on how to adapt the ISO 26262 safety standard
  • 2018
  • Ingår i: 2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS). - New York, NY : ACM Publications. - 9781450357395 - 9781538662618 ; May 2018, s. 47-49
  • Konferensbidrag (refereegranskat)abstract
    • 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.
  •  
35.
  • Henriksson, Jens, 1991, et al. (författare)
  • Performance analysis of out-of-distribution detection on trained neural networks
  • 2020
  • Ingår i: Information and Software Technology. - : Elsevier B.V.. - 0950-5849 .- 1873-6025.
  • Tidskriftsartikel (refereegranskat)abstract
    • Context: Deep Neural Networks (DNN) have shown great promise in various domains, for example to support pattern recognition in medical imagery. However, DNNs need to be tested for robustness before being deployed in safety critical applications. One common challenge occurs when the model is exposed to data samples outside of the training data domain, which can yield to outputs with high confidence despite no prior knowledge of the given input. Objective: The aim of this paper is to investigate how the performance of detecting out-of-distribution (OOD) samples changes for outlier detection methods (e.g., supervisors) when DNNs become better on training samples. Method: Supervisors are components aiming at detecting out-of-distribution samples for a DNN. The experimental setup in this work compares the performance of supervisors using metrics and datasets that reflect the most common setups in related works. Four different DNNs with three different supervisors are compared during different stages of training, to detect at what point during training the performance of the supervisors begins to deteriorate. Results: Found that the outlier detection performance of the supervisors increased as the accuracy of the underlying DNN improved. However, all supervisors showed a large variation in performance, even for variations of network parameters that marginally changed the model accuracy. The results showed that understanding the relationship between training results and supervisor performance is crucial to improve a model's robustness. Conclusion: Analyzing DNNs for robustness is a challenging task. Results showed that variations in model parameters that have small variations on model predictions can have a large impact on the out-of-distribution detection performance. This kind of behavior needs to be addressed when DNNs are part of a safety critical application and hence, the necessary safety argumentation for such systems need be structured accordingly.
  •  
36.
  • Henriksson, Jens, et al. (författare)
  • Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks
  • 2019
  • Ingår i: Proceedings. 45th Euromicro Conference on Software Engineering and Advanced Applications. SEAA 2019. - Piscataway : IEEE. - 9781728134215 - 9781728134222 - 9781728132853 ; , s. 113-120
  • Konferensbidrag (refereegranskat)abstract
    • 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 applications, robustness of such approaches is still an issue. A common challenge for Deep Neural Networks (DNN) occur when exposed to out-of-distribution samples that are previously unseen, where DNNs can yield high confidence predictions despite no prior knowledge of the input. In this paper we analyse two supervisors on two well-known DNNs with varied setups of training and find that the outlier detection performance improves with the quality of the training procedure. We analyse the performance of the supervisor after each epoch during the training cycle, to investigate supervisor performance as the accuracy converges. 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. © 2019 IEEE
  •  
37.
  • Kianfar, Roozbeh, 1984, et al. (författare)
  • Design and Experimental Validation of a Cooperative Driving System in the Grand Cooperative Driving Challenge
  • 2012
  • Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 13:3, s. 994-1007
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper, we present the Cooperative Adaptive Cruise Control (CACC) architecture, which was proposed and implemented by the team from Chalmers University of Technology, Göteborg, Sweden, that joined the Grand Cooperative Driving Challenge (GCDC) in 2011. The proposed CACC architecture consists of the following three main components, which are described in detail: 1) communication; 2) sensor fusion; and 3) control. Both simulation and experimental results are provided, demonstrating that the proposed CACC system can drive within a vehicle platoon while minimizing the inter-vehicle spacing within the allowed range of safety distances, tracking a desired speed profile, and attenuating acceleration shockwaves.
  •  
38.
  • Perez-Cerrolaza, Jon, et al. (författare)
  • Artificial Intelligence for Safety-Critical Systems in Industrial and Transportation Domains: A Survey
  • 2024
  • Ingår i: ACM Computing Surveys. - New York : Association for Computing Machinery. - 0360-0300 .- 1557-7341. ; 56:7
  • Tidskriftsartikel (refereegranskat)abstract
    • Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-critical systems in which Machine Learning (ML) algorithms learn optimized and safe solutions. AI can also support and assist human safety engineers in developing safety-critical systems. However, reconciling both cutting-edge and state-of-the-art AI technology with safety engineering processes and safety standards is an open challenge that must be addressed before AI can be fully embraced in safety-critical systems. Many works already address this challenge, resulting in a vast and fragmented literature. Focusing on the industrial and transportation domains, this survey structures and analyzes challenges, techniques, and methods for developing AI-based safety-critical systems, from traditional functional safety systems to autonomous systems. AI trustworthiness spans several dimensions, such as engineering, ethics and legal, and this survey focuses on the safety engineering dimension.
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39.
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40.
  • Rosberg, Felix, 1995- (författare)
  • Anonymizing Faces without Destroying Information
  • 2024
  • Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Anonymization is a broad term. Meaning that personal data, or rather data that identifies a person, is redacted or obscured. In the context of video and image data, the most palpable information is the face. Faces barely change compared to other aspect of a person, such as cloths, and we as people already have a strong sense of recognizing faces. Computers are also adroit at recognizing faces, with facial recognition models being exceptionally powerful at identifying and comparing faces. Therefore it is generally considered important to obscure the faces in video and image when aiming for keeping it anonymized. Traditionally this is simply done through blurring or masking. But this de- stroys useful information such as eye gaze, pose, expression and the fact that it is a face. This is an especial issue, as today our society is data-driven in many aspects. One obvious such aspect is autonomous driving and driver monitoring, where necessary algorithms such as object-detectors rely on deep learning to function. Due to the data hunger of deep learning in conjunction with society’s call for privacy and integrity through regulations such as the General Data Protection Regularization (GDPR), anonymization that preserve useful information becomes important.This Thesis investigates the potential and possible limitation of anonymizing faces without destroying the aforementioned useful information. The base approach to achieve this is through face swapping and face manipulation, where the current research focus on changing the face (or identity) while keeping the original attribute information. All while being incorporated and consistent in an image and/or video. Specifically, will this Thesis demonstrate how target-oriented and subject-agnostic face swapping methodologies can be utilized for realistic anonymization that preserves attributes. Thru this, this Thesis points out several approaches that is: 1) controllable, meaning the proposed models do not naively changes the identity. Meaning that what kind of change of identity and magnitude is adjustable, thus also tunable to guarantee anonymization. 2) subject-agnostic, meaning that the models can handle any identity. 3) fast, meaning that the models is able to run efficiently. Thus having the potential of running in real-time. The end product consist of an anonymizer that achieved state-of-the-art performance on identity transfer, pose retention and expression retention while providing a realism.Apart of identity manipulation, the Thesis demonstrate potential security issues. Specifically reconstruction attacks, where a bad-actor model learns convolutional traces/patterns in the anonymized images in such a way that it is able to completely reconstruct the original identity. The bad-actor networks is able to do this with simple black-box access of the anonymization model by constructing a pair-wise dataset of unanonymized and anonymized faces. To alleviate this issue, different defense measures that disrupts the traces in the anonymized image was investigated. The main take away from this, is that naively using what qualitatively looks convincing of hiding an identity is not necessary the case at all. Making robust quantitative evaluations important.
  •  
41.
  • Rosberg, Felix, et al. (författare)
  • Comparing Facial Expressions for Face Swapping Evaluation with Supervised Contrastive Representation Learning
  • 2021
  • Ingår i: 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021). - Piscataway : IEEE. - 9781665431767
  • Konferensbidrag (refereegranskat)abstract
    • Measuring and comparing facial expression have several practical applications. One such application is to measure the facial expression embedding, and to compare distances between those expressions embeddings in order to determine the identity- and face swapping algorithms' capabilities in preserving the facial expression information. One useful aspect is to present how well the expressions are preserved while anonymizing facial data during privacy aware data collection. We show that a weighted supervised contrastive learning is a strong approach for learning facial expression representation embeddings and dealing with the class imbalance bias. By feeding a classifier-head with the learned embeddings we reach competitive state-of-the-art results. Furthermore, we demonstrate the use case of measuring the distance between the expressions of a target face, a source face and the anonymized target face in the facial anonymization context. © 2021 IEEE.
  •  
42.
  • Rosberg, Felix, et al. (författare)
  • FaceDancer : Pose- and Occlusion-Aware High Fidelity Face Swapping
  • 2023
  • Ingår i: Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023. - Piscataway : IEEE. - 9781665493468 ; , s. 3443-3452
  • Konferensbidrag (refereegranskat)abstract
    • In this work, we present a new single-stage method for subject agnostic face swapping and identity transfer, named FaceDancer. We have two major contributions: Adaptive Feature Fusion Attention (AFFA) and Interpreted Feature Similarity Regularization (IFSR). The AFFA module is embedded in the decoder and adaptively learns to fuse attribute features and features conditioned on identity information without requiring any additional facial segmentation process. In IFSR, we leverage the intermediate features in an identity encoder to preserve important attributes such as head pose, facial expression, lighting, and occlusion in the target face, while still transferring the identity of the source face with high fidelity. We conduct extensive quantitative and qualitative experiments on various datasets and show that the proposed FaceDancer outperforms other state-of-the-art networks in terms of identityn transfer, while having significantly better pose preservation than most of the previous methods. © 2023 IEEE.
  •  
43.
  • Rosberg, Felix, et al. (författare)
  • FIVA : Facial Image and Video Anonymization and Anonymization Defense
  • 2023
  • Ingår i: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). - Los Alamitos, CA : IEEE. - 9798350307443 ; , s. 362-371
  • Konferensbidrag (refereegranskat)abstract
    • In this paper, we present a new approach for facial anonymization in images and videos, abbreviated as FIVA. Our proposed method is able to maintain the same face anonymization consistently over frames with our suggested identity-tracking and guarantees a strong difference from the original face. FIVA allows for 0 true positives for a false acceptance rate of 0.001. Our work considers the important security issue of reconstruction attacks and investigates adversarial noise, uniform noise, and parameter noise to disrupt reconstruction attacks. In this regard, we apply different defense and protection methods against these privacy threats to demonstrate the scalability of FIVA. On top of this, we also show that reconstruction attack models can be used for detection of deep fakes. Last but not least, we provide experimental results showing how FIVA can even enable face swapping, which is purely trained on a single target image. © 2023 IEEE.
  •  
44.
  • Rosberg, Felix, 1995-, et al. (författare)
  • Towards Privacy Aware Data collection in Traffic : A Proposed Method for Measuring Facial Anonymity
  • 2021
  • Ingår i: Fast-Zero 2021 Proceedings. - Chiyoda : JSAE.
  • Konferensbidrag (refereegranskat)abstract
    • Developing a machine learning-based vehicular safety system that is effective and generalizes well, capable of coping with all the different scenarios in real traffic is a challenge that requires large amounts of data. Especially visual data for when you want an autonomous vehicle to make decisions based on peoples’ possible intent revealed by the facial expression and eye gaze of nearby pedestrians. The problem with collecting this kind of data is the privacy issues and conflict with current laws like General Data Protection Regulation (GDPR). To deal with this problem we can anonymise faces with current identity and face swapping techniques. To evaluate the performance and interpretation of the anonymization process, there is a need for a metric to measure how well these faces are anonymized that takes identity leakage into consideration. To our knowledge, there is currently no such investigation for this problem. However, our method is based on current facial recognition methods and how recent face swapping work determines identity transfer performance. Our suggestion is to utilize state-of-the-art identity encoders like FaceNet and ArcFace to make use of the embedding vectors to measure anonymity. We provide qualitative results that show the applicability of publicly available identity encoders for measuring anonymity. We further strengthen the applicability of how these encoders behave on the VGGFace2 dataset compared to samples that have had their identity changed by Faceshifter, along with a survey regarding the anonymization procedure to pinpoint how strong facial anonymization is compared the vector distance measurements.
  •  
45.
  • Rosell, Joakim, et al. (författare)
  • A Frequency-based Data Mining Approach to Enhance in-vehicle Network Intrusion Detection
  • 2021
  • Ingår i: FAST-zero '21. - Japan : Society of Automotive Engineers.
  • Konferensbidrag (refereegranskat)abstract
    • Modern vehicles have numerous electronic control units (ECUs) that constantly communicate over embedded in-vehicle networks (IVNs) comprised of controlled area network (CAN) segments. The simplicity and size-constrained 8-byte payload of the CAN bus technology makes it infeasible to integrate authenticity and integrity-based protection mechanisms. Thus, a malicious component will be able to inject malicious data into the network with minimal risk for detection. Such vulnerabilities have been demonstrated with various security attacks such as the flooding, fuzzing, and malfunction attacks. A practical approach to improve security in modern vehicles is to monitor the CAN bus traffic to detect anomalies. However, to administer such an intrusion detection system (IDS) with a general approach faces some challenges. First, the proprietary encodings of the CAN data fields need to be omitted as they are intellectual property of the original equipment manufacturers (OEMs) and differ across vehicle manufacturers and their models. Secondly, such general and practical IDS approach must also be computationally efficient in terms of speed and accuracy. Traditional IDSs for computer networks generally utilize a rule or signature-based approach. More recently, the approach of using machine learning (ML) with efficient feature representation has shown significant success because of faster detection and lower development and maintenance costs. Therefore, an efficient data aggregation technique with enhanced frequency-based feature representation to improve the performance of MLbased IDS for the IVNs is proposed. The performance gain was verified with the Survival Analysis Dataset for automobile IDS.
  •  
46.
  •  
47.
  • Sprei, Frances, 1977, et al. (författare)
  • Comparing Elecytric Vehicles and Fossil Driven Vehicles in Free-floating Car Sharing Services
  • 2017
  • Ingår i: Proceedings of EEVC 2017.
  • Konferensbidrag (refereegranskat)abstract
    • In recent years, free-floating car sharing (FFCS) services have been offered as a more flexible option compared to traditional car sharing. FFCS allows users to pick up and return cars anywhere within a specified area of a city. These can be either electric or fossil driven vehicles. We analyze the difference in usage of these two types of vehicles. The analysis is based on a dataset consisting of vehicle availability data sampled between 2014 and 2016 for 9 cities with EVs in the FFCS fleet. We find that there is no statistical difference in how EVs and fossil driven FFCS vehicles are used. When it comes to charging of EVs two main strategies are identified: widespread “slow charging” versus tailored fast-charging.
  •  
48.
  • Sprei, Frances, 1977, et al. (författare)
  • Free-floating car-sharing electrification and mode displacement : Travel time and usage patterns from 12 cities in Europe and the United States
  • 2019
  • Ingår i: Transportation Research Part D. - Oxford : Elsevier BV. - 1361-9209 .- 1879-2340. ; 71, s. 127-140
  • Tidskriftsartikel (refereegranskat)abstract
    • Free-floating car-sharing (FFCS) allows users to book a vehicle through their phone, use it and return it anywhere within a designated area in the city. FFCS has the potential to contribute to a transition to low-carbon mobility if the vehicles are electric, and if the usage does not displace active travel or public transport use. The aim of this paper is to study what travel time and usage patterns of the vehicles among the early adopters of the service reveal about these two issues. We base our analysis on a dataset containing rentals from 2014 to 2017, for 12 cities in Europe and the United States. For seven of these cities, we have collected travel times for equivalent trips with walking, biking, public transport and private car. FFCS services are mainly used for shorter trips with a median rental time of 27 min and actual driving time closer to 15 min. When comparing FFCS with other transport modes, we find that rental times are generally shorter than the equivalent walking time but longer than cycling. For public transport, the picture is mixed: for some trips there is no major time gain from taking FFCS, for others it could be up to 30 min. For electric FFCS vehicles rental time is shorter and the number of rentals per car and day are slightly fewer compared to conventional vehicles. Still, evidence from cities with an only electric fleet show that these services can be electrified and reach high levels of utilization.
  •  
49.
  • Svanström, Fredrik, et al. (författare)
  • A dataset for multi-sensor drone detection
  • 2021
  • Ingår i: Data in Brief. - Amsterdam : Elsevier Inc.. - 2352-3409. ; 39
  • Tidskriftsartikel (refereegranskat)abstract
    • The use of small and remotely controlled unmanned aerial vehicles (UAVs), referred to as drones, has increased dramatically in recent years, both for professional and recreative purposes. This goes in parallel with (intentional or unintentional) misuse episodes, with an evident threat to the safety of people or facilities [1]. As a result, the detection of UAV has also emerged as a research topic [2]. Most of the existing studies on drone detection fail to specify the type of acquisition device, the drone type, the detection range, or the employed dataset. The lack of proper UAV detection studies employing thermal infrared cameras is also acknowledged as an issue, despite its success in detecting other types of targets [2]. Beside, we have not found any previous study that addresses the detection task as a function of distance to the target. Sensor fusion is indicated as an open research issue as well to achieve better detection results in comparison to a single sensor, although research in this direction is scarce too [3–6]. To help in counteracting the mentioned issues and allow fundamental studies with a common public benchmark, we contribute with an annotated multi-sensor database for drone detection that includes infrared and visible videos and audio files. The database includes three different drones, a small-sized model (Hubsan H107D+), a medium-sized drone (DJI Flame Wheel in quadcopter configuration), and a performance-grade model (DJI Phantom 4 Pro). It also includes other flying objects that can be mistakenly detected as drones, such as birds, airplanes or helicopters. In addition to using several different sensors, the number of classes is higher than in previous studies [4]. The video part contains 650 infrared and visible videos (365 IR and 285 visible) of drones, birds, airplanes and helicopters. Each clip is of ten seconds, resulting in a total of 203,328 annotated frames. The database is complemented with 90 audio files of the classes drones, helicopters and background noise. To allow studies as a function of the sensor-to-target distance, the dataset is divided into three categories (Close, Medium, Distant) according to the industry-standard Detect, Recognize and Identify (DRI) requirements [7], built on the Johnson criteria [8]. Given that the drones must be flown within visual range due to regulations, the largest sensor-to-target distance for a drone in the dataset is 200 m, and acquisitions are made in daylight. The data has been obtained at three airports in Sweden: Halmstad Airport (IATA code: HAD/ICAO code: ESMT), Gothenburg City Airport (GSE/ESGP) and Malmö Airport (MMX/ESMS). The acquisition sensors are mounted on a pan-tilt platform that steers the cameras to the objects of interest. All sensors and the platform are controlled with a standard laptop vis a USB hub.
  •  
50.
  • Svanström, Fredrik, et al. (författare)
  • Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities
  • 2022
  • Ingår i: Drones. - Basel : MDPI. - 2504-446X. ; 6:11
  • Tidskriftsartikel (refereegranskat)abstract
    • Automatic detection of flying drones is a key issue where its presence, especially if unauthorized, can create risky situations or compromise security. Here, we design and evaluate a multi-sensor drone detection system. In conjunction with standard video cameras and microphone sensors, we explore the use of thermal infrared cameras, pointed out as a feasible and promising solution that is scarcely addressed in the related literature. Our solution integrates a fish-eye camera as well to monitor a wider part of the sky and steer the other cameras towards objects of interest. The sensing solutions are complemented with an ADS-B receiver, a GPS receiver, and a radar module. However, our final deployment has not included the latter due to its limited detection range. The thermal camera is shown to be a feasible solution as good as the video camera, even if the camera employed here has a lower resolution. Two other novelties of our work are the creation of a new public dataset of multi-sensor annotated data that expands the number of classes compared to existing ones, as well as the study of the detector performance as a function of the sensor-to-target distance. Sensor fusion is also explored, showing that the system can be made more robust in this way, mitigating false detections of the individual sensors. © 2022 by the authors.
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