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See detailTowards Privacy Preserving Data Centric Super App
Carvalho Ota, Fernando Kaway UL; Meira, Jorge Augusto UL; Frank, Raphaël UL et al

in Carvalho Ota, Fernando Kaway; Meira, Jorge Augusto; Frank, Raphaël (Eds.) et al 2020 Mediterranean Communication and Computer Networking Conference, Arona 17-19 June 2020 (2020, September 10)

The number of smartphone users recently surpassed the numbers of desktop users on Internet, and opened up countless development challenges and business opportunities. Not only the fact that the majority ... [more ▼]

The number of smartphone users recently surpassed the numbers of desktop users on Internet, and opened up countless development challenges and business opportunities. Not only the fact that the majority of users are connected using their smartphones, but the number of Internet users in general has popularized the massive use of data-driven applications. In this context, the concept of super apps seems to be the next game-changer for the mobile apps industry, and the challenges related to security and privacy are key aspects for keeping user data safe. Thus, by combining different components for provisioning, authentication, membership and others, we propose a novel framework that enables the creation of a super app using privacy by design principles. [less ▲]

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See detailLeveraging Privileged Information to Limit Distraction in End-to-End Lane Following
Robinet, François UL; Demeules, Antoine; Frank, Raphaël UL et al

in 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) (2020)

Convolutional Neural Networks have been successfully used to steer vehicles using only road-facing cameras. In this work, we investigate the use of Privileged Information for training an end-to-end lane ... [more ▼]

Convolutional Neural Networks have been successfully used to steer vehicles using only road-facing cameras. In this work, we investigate the use of Privileged Information for training an end-to-end lane following model. Starting from the prior assumption that such a model should spend a sizeable fraction of its focus on lane markings, we take advantage of lane geometry information available at training time to improve its performance. To this end, we constrain the class of learnable functions by imposing a prior stemming from a lane segmentation task. For each input frame, we compute the set of pixels that most contribute to the prediction of the model using the VisualBackProp method. These pixel-relevance heatmaps are then compared with ground truth lane segmentation masks. A Distraction Loss term is added to the objective to regularize the training process. We learn from real-world data collected using our experimental vehicle and compare the results to those obtained using the simple Mean Squared Error objective. We show that the presence of our regularizer benefits both the performance and the stability of the model across a variety of evaluation metrics. We use a pretrained lane segmentation model without fine-tuning to extract lane marking masks and show that valuable learning signals can be extracted even from imperfect privileged knowledge. This method can be implemented easily and very efficiently on top of an existing architecture, without requiring the addition of any trainable parameter. [less ▲]

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See detailIntrusion detection on robot cameras using spatio-temporal autoencoders: A self-driving car application
Amrouche, Faouzi UL; Lagraa, Sofiane UL; Frank, Raphaël UL et al

in 91st IEEE Vehicular Technology Conference, VTC Spring 2020, Antwerp, Belgium, May 25-28, 2020 (2020)

Robot Operating System (ROS) is becoming more and more important and is used widely by developers and researchers in various domains. One of the most important fields where it is being used is the self ... [more ▼]

Robot Operating System (ROS) is becoming more and more important and is used widely by developers and researchers in various domains. One of the most important fields where it is being used is the self-driving cars industry. However, this framework is far from being totally secure, and the existing security breaches do not have robust solutions. In this paper we focus on the camera vulnerabilities, as it is often the most important source for the environment discovery and the decision-making process. We propose an unsupervised anomaly detection tool for detecting suspicious frames incoming from camera flows. Our solution is based on spatio-temporal autoencoders used to truthfully reconstruct the camera frames and detect abnormal ones by measuring the difference with the input. We test our approach on a real-word dataset, i.e. flows coming from embedded cameras of self-driving cars. Our solution outperforms the existing works on different scenarios. [less ▲]

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See detailHow Road and Mobile Networks Correlate: Estimating Urban Traffic Using Handovers
Derrmann, Thierry; Frank, Raphaël UL; Viti, Francesco UL et al

in IEEE Transactions on Intelligent Transportation Systems (2019)

We propose a novel way of linking mobile network signaling data to the state of the underlying urban road network. We show how a predictive model of traffic flows can be created from mobile network ... [more ▼]

We propose a novel way of linking mobile network signaling data to the state of the underlying urban road network. We show how a predictive model of traffic flows can be created from mobile network signaling data. To achieve this, we estimate the vehicular density inside specific areas using a polynomial function of the inner and exiting mobile phone handovers performed by the base stations covering those areas. We can then use the aggregated handovers as flow proxies alongside the density proxy to directly estimate an average velocity within an area. We evaluate the model in a simulation study of Luxembourg city and generalize our findings using a real-world data set extracted from the LTE network of a Luxembourg operator. By predicting the real traffic states as measured through floating car data, we achieve a mean absolute percentage error of 11.12%. Furthermore, in our study case, the approximations of the network macroscopic fundamental diagrams (MFD) of road network partitions can be generated. The analyzed data exhibit low variance with respect to a quadratic concave flow-density function, which is inline with the previous theoretical results on MFDs and are similar when estimated from simulation and real data. These results indicate that mobile signaling data can potentially be used to approximate MFDs of the underlying road network and contribute to better estimate road traffic states in urban congested networks. [less ▲]

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See detailEvaluation of End-To-End Learning for Autonomous Driving: The Good, the Bad and the Ugly
Varisteas, Georgios UL; Frank, Raphaël UL; Sajadi Alamdari, Seyed Amin UL et al

in 2nd International Conference on Intelligent Autonomous Systems, Singapore, Feb. 28 to Mar. 2, 2019 (2019, March 01)

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See detailJUNIOR, A Research Platform for Connected and Automated Driving
Varisteas, Georgios UL; Frank, Raphaël UL

in Proceedings of 2019 IEEE Vehicular Networking Conference (2019)

This poster paper presents a platform to conduct connected and automated driving experiments. We describe a low cost and easy to implement hardware and software platform suitable for universities and ... [more ▼]

This poster paper presents a platform to conduct connected and automated driving experiments. We describe a low cost and easy to implement hardware and software platform suitable for universities and companies. We first motivate the choice of the vehicle and the various hardware components, followed by the description of the open-source software stack. We will conclude by providing an overview of use cases and ongoing projects. [less ▲]

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See detailVisualizing the Learning Progress of Self-Driving Cars
Mund, Sandro; Frank, Raphaël UL; Varisteas, Georgios UL et al

in 21st International Conference on Intelligent Transportation Systems (2018, November 02)

Using Deep Learning to predict lateral and longitudinal vehicle control, i.e. steering, acceleration and braking, is becoming increasingly popular. However, it remains widely unknown why those models ... [more ▼]

Using Deep Learning to predict lateral and longitudinal vehicle control, i.e. steering, acceleration and braking, is becoming increasingly popular. However, it remains widely unknown why those models perform so well. In order for them to become a commercially viable solution, it first needs to be understood why a certain behavior is triggered and how and what those networks learn from human-generated driving data to ensure safety. One research direction is to visualize what the network sees by highlighting regions of an image that influence the outcome of the model. In this vein, we propose a generic visualization method using Attention Heatmaps (AHs) to highlight what a given Convolutional Neural Network (CNN) learns over time. To do so, we rely on a novel occlusion technique to mask different regions of an input image to observe the effect on a predicted steering signal. We then gradually increase the amount of training data and study the effect on the resulting Attention Heatmaps, both in terms of visual focus and temporal behavior. [less ▲]

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See detailUsing mobile phone data for urban network state estimation
Derrmann, Thierry; Frank, Raphaël UL; Engel, Thomas UL et al

Scientific Conference (2018, June)

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See detailCharacterizing Driving Environments Through Bluetooth Discovery
Bronzi, Walter UL; Faye, Sébastien UL; Frank, Raphaël UL et al

Scientific Conference (2017, October)

Within the world of wireless technologies, Bluetooth has recently been at the forefront of innovation. It is becoming increasingly relevant for vehicles to become aware of their surroundings. Therefore ... [more ▼]

Within the world of wireless technologies, Bluetooth has recently been at the forefront of innovation. It is becoming increasingly relevant for vehicles to become aware of their surroundings. Therefore, having knowledge of nearby Bluetooth devices, both inside and outside other vehicles, can provide the listening vehicles with enough data to learn about their environment. In this paper, we collect and analyze a dataset of Bluetooth Classic (BC) and Low Energy (BLE) discoveries. We evaluate their respective characteristics and ability to provide context-aware information from a vehicular perspective. By taking a look at data about the encountered devices, such as GPS location, quantity, quality of signal and device class information, we infer distinctive behaviors between BC and BLE relative to context and application. For this purpose, we propose a set a features to train a classifier for the recognition of different driving environments (i.e. road classes) from Bluetooth discovery data alone. Comparing the performance of our classifier with different sampling parameters, the presented results indicate that, with our feature selection, we are able to predict with reasonable confidence up to three classes (Highway, City, Extra-Urban) by using only discovery data and no geographical information. This outcome gives promising results targeted at low energy and privacy-friendly applications and can open up a wide range of research directions. [less ▲]

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See detailEstimating Urban Road Traffic States Using Mobile Network Signaling Data
Derrmann, Thierry UL; Frank, Raphaël UL; Viti, Francesco UL et al

in Abstract book of the 20th International Conference on Intelligent Transportation Systems (2017, October)

It is intuitive that there is a causal relationship between human mobility and signaling events in mobile phone networks. Among these events, not only the initiation of calls and data sessions can be used ... [more ▼]

It is intuitive that there is a causal relationship between human mobility and signaling events in mobile phone networks. Among these events, not only the initiation of calls and data sessions can be used in analyses, but also handovers between different locations that reflect mobility. In this work, we investigate if handovers can be used as a proxy metric for flows in the underlying road network, especially in urban environments. More precisely, we show that characteristic profiles of handovers within and between clusters of mobile network cells exist. We base these profiles on models from road traffic flow theory, and show that they can be used for traffic state estimation using floating-car data as ground truth. The presented model can be beneficial in areas with good mobile network coverage but low road traffic counting infrastructure, e.g. in developing countries, but also serve as an additional predictor for existing traffic state monitoring systems. [less ▲]

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See detailHow Mobile Phone Handovers reflect Urban Mobility: A Simulation Study
Derrmann, Thierry UL; Frank, Raphaël UL; Engel, Thomas UL et al

in Proceedings of the 5th IEEE Conference on Models and Technologies for Intelligent Transportation Systems. (2017, June 26)

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See detailSmartphone-based Adaptive Driving Maneuver Detection: A large-scale Evaluation Study
Castignani, German UL; Derrmann, Thierry UL; Frank, Raphaël UL et al

in IEEE Transactions on Intelligent Transportation Systems (2017)

The proliferation of connected mobile devices together with advances in their sensing capacity has enabled a new distributed telematics platform. In particular, smartphones can be used as driving sensors ... [more ▼]

The proliferation of connected mobile devices together with advances in their sensing capacity has enabled a new distributed telematics platform. In particular, smartphones can be used as driving sensors to identify individual driver behavior and risky maneuvers. However, in order to estimate driver behavior with smartphones, the system must deal with different vehicle characteristics. This is the main limitation of existing sensing platforms, which are principally based on fixed thresholds for different sensing parameters. In this paper, we propose an adaptive driving maneuver detection mechanism that iteratively builds a statistical model of the driver, vehicle, and smartphone combination using a multivariate normal model. By means of experimentation over a test track and public roads, we first explore the capacity of different sensor input combinations to detect risky driving maneuvers, and we propose a training mechanism that adapts the profiling model to the vehicle, driver, and road topology. A large-scale evaluation study is conducted, showing that the model for maneuver detection and scoring is able to adapt to different drivers, vehicles, and road conditions. [less ▲]

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See detailPoster: LuST-LTE: A Simulation Package for Pervasive Vehicular Connectivity
Derrmann, Thierry UL; Faye, Sébastien UL; Frank, Raphaël UL et al

Poster (2016, December 08)

Recent technological advances in communication technology have provided new ways to understand human mobility. Connected vehicles with their rising market penetration are particularly representative of ... [more ▼]

Recent technological advances in communication technology have provided new ways to understand human mobility. Connected vehicles with their rising market penetration are particularly representative of this trend. They become increasingly interesting, not only as sensors, but also as participants in Intelligent Transportation System (ITS) applications. More specifically, their pervasive connectivity to cellular networks enables them as passive and active sensing units. In this paper, we introduce LuST-LTE, a package of open-source simulation tools that allows the simulation of vehicular traffic along with pervasive LTE connectivity. [less ▲]

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See detailTowards Privacy-Neutral Travel Time Estimation from Mobile Phone Signalling Data
Derrmann, Thierry UL; Frank, Raphaël UL; Faye, Sébastien UL et al

in Proceedings of the 2016 IEEE International Smart Cities Conference (ISC2) (2016, September)

Today’s mobile penetration rates enable cellular signaling data to be useful in diverse fields such as transportation planning, the social sciences and epidemiology. Of particular interest for these ... [more ▼]

Today’s mobile penetration rates enable cellular signaling data to be useful in diverse fields such as transportation planning, the social sciences and epidemiology. Of particular interest for these applications are mobile subscriber dwell times. They express how long users stay in the service range of a base station. In this paper, we want to evaluate whether dwell time distributions can serve as predictors for road travel times. To this end, we transform floating car data into synthetic dwell times that we use as weights in a graph-based model. The model predictions are evaluated using the floating car ground truth data. Additionally, we show a potential link between handover density and travel times. We conclude that dwell times are a promising predictor for travel times, and can serve as a valuable input for intelligent transportation systems. [less ▲]

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See detailLuST-LTE: A Simulation Package for Pervasive Vehicular Connectivity
Derrmann, Thierry UL; Faye, Sébastien UL; Frank, Raphaël UL et al

Presentation (2016, June 30)

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See detailLuxembourg SUMO Traffic (LuST) Scenario: Traffic Demand Evaluation
Codeca, Lara UL; Frank, Raphaël UL; Faye, Sébastien UL et al

in IEEE Intelligent Transportation Systems Magazine (2016)

Both the industrial and the scientific communities are working on problems related to vehicular traffic congestion, intelligent transportation systems, and mobility patterns using information collected ... [more ▼]

Both the industrial and the scientific communities are working on problems related to vehicular traffic congestion, intelligent transportation systems, and mobility patterns using information collected from a variety of sources. Usually, a vehicular traffic simulator, with an appropriate scenario for the problem at hand, is used to reproduce realistic mobility patterns. Many mobility simulators are available, and the choice is made based on the type of simulation required, but a common problem is finding a realistic traffic scenario. The aim of this work is to provide and evaluate a scenario able to meet all the basic requirements in terms of size, realism, and duration, in order to have a common basis for evaluations. In the interest of building a realistic scenario, we used information from a real city with a typical topology common in mid-size European cities, and realistic traffic demand and mobility patterns. In this paper, we show the process used to build the Luxembourg SUMO Traffic (LuST) Scenario, and present a summary of its characteristics together with our evaluation and validation of the traffic demand and mobility patterns. [less ▲]

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See detailLuxembourg SUMO Traffic (LuST) Scenario: 24 Hours of Mobility for Vehicular Networking Research
Codeca, Lara UL; Frank, Raphaël UL; Engel, Thomas UL

in Proceedings of the 7th IEEE Vehicular Networking Conference (2015, December)

Different research communities varying from telecommunication to traffic engineering are working on problems related to vehicular traffic congestion, intelligent transportation systems, and mobility ... [more ▼]

Different research communities varying from telecommunication to traffic engineering are working on problems related to vehicular traffic congestion, intelligent transportation systems, and mobility patterns using information collected from a variety of sensors. To test the solutions, the first step is to use a vehicular traffic simulator with an appropriate scenario in order to reproduce realistic mobility patterns. Many mobility simulators are available, and the choice is usually done based on the size and type of simulation required, but a common problem is to find a realistic traffic scenario. In order to evaluate and compare new communication protocols for vehicular networks, it is necessary to use a wireless network simulator in combination with a vehicular traffic simulator. This additional step introduces further requirements for the scenario. The aim of this work is to provide a scenario able to meet all the common requirements in terms of size, realism and duration, in order to have a common basis for the evaluations. In the interest of building a realistic scenario, we decided to start from a real city with a standard topology common in mid-size European cities, and real information concerning traffic demands and mobility patterns. In this paper we show the process used to build the Luxembourg SUMO Traffic (LuST) Scenario, and present a summary of its characteristics together with an overview of its possible use cases. [less ▲]

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See detailA Car Hacking Experiment: When Connectivity meets Vulnerability
Jafarnejad, Sasan UL; Codeca, Lara UL; Bronzi, Walter UL et al

in Globecom Workshops (GC Wkshps), 2015 IEEE (2015, December)

Interconnected vehicles are a growing commodity providing remote access to on-board systems for monitoring and controlling the state of the vehicle. Such features are built to facilitate and strengthen ... [more ▼]

Interconnected vehicles are a growing commodity providing remote access to on-board systems for monitoring and controlling the state of the vehicle. Such features are built to facilitate and strengthen the owner’s knowledge about its car but at the same time they impact its safety and security. Vehicles are not ready to be fully connected as various attacks are currently possible against their control systems. In this paper, we analyse possible attack scenarios on a recently released all-electric car and investigate their impact on real life driving scenarios. We leverage our findings to change the behaviour of safety critical components of the vehicle in order to achieve autonomous driving using an Open Vehicle Monitoring System. Furthermore, to demonstrate the potential of our setup, we developed a novel mobile application able to control such vehicle systems remotely through the Internet. We challenge the current state-of-the-art technology in today’s vehicles and provide a vulnerability analysis on modern embedded systems. [less ▲]

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See detailValidation study of risky event classification using driving pattern factors
Castignani, German UL; Derrmann, Thierry UL; Frank, Raphaël UL et al

Scientific Conference (2015, November 24)

Detailed reference viewed: 180 (6 UL)