References of "Jafarnejad, Sasan 50002031"
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See detailNon-intrusive Distracted Driving Detection Based on Driving Sensing Data
Jafarnejad, Sasan UL; Castignani, German UL; Engel, Thomas UL

Scientific Conference (2018, March)

Nowadays Internet-enabled phones have become ubiquitous, and we all witness the flood of information that often arrives with a notification. Most of us immediately divert our attention to our phones even ... [more ▼]

Nowadays Internet-enabled phones have become ubiquitous, and we all witness the flood of information that often arrives with a notification. Most of us immediately divert our attention to our phones even when we are behind the wheel. Statistics show that drivers use their phone on 88% of their trips and on 2015 in the UnitedKingdom 25% of the fatal accidents were caused by distraction or impairment. Therefore there is need to tackle this issue. However, most of the distraction detection methods either use expensive dedicated hardware and/or they make use of intrusive or uncomfortable sensors. We propose distracted driving detection mechanism using non-intrusive vehicle sensor data. In the proposed method 9 driving signals are used. The data is collected, then two sets of statistical and cepstral features are extracted using a sliding window process, further a classifier makes a prediction for each window frame, lastly, a decision function takes the last l predictions and makes the final prediction. We evaluate the subject independent performance of the proposed mechanism using a driving dataset consisting of 13 drivers. We show that performance increases as the decision window become larger.We achieve the best results using a Gradient Boosting classifier with a decision window of total duration 285seconds which yield ROC AUC of 98.7%. [less ▲]

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See detailPoster: Characterizing Driving Behaviors Through a Car Simulation Platform
Faye, Sébastien UL; Jafarnejad, Sasan UL; Costamagna, Juan UL et al

Poster (2017, November 27)

Human mobility has opened up to many themes in recent years. Human behavior and how a driver might react to certain situations, whether dangerous (e.g. an accident) or simply part of the evolution of new ... [more ▼]

Human mobility has opened up to many themes in recent years. Human behavior and how a driver might react to certain situations, whether dangerous (e.g. an accident) or simply part of the evolution of new technologies (e.g. autonomous driving), leaves many avenues to be explored. Although experiments have been deployed in real situations, it remains difficult to encounter the conditions that certain studies may require. For this reason, we have set up a driving simulator (comprising several modules) that is able to reproduce a realistic driving environment. Although, as the literature has already demonstrated, the conditions are often far from reality, simulation platforms are nonetheless capable of reproducing an incredibly large number of scenarios on the fly. In this poster, we explain how we conceived the simulator, as well as the system we developed for collecting metrics on both the driver and the simulation environment. In addition, we take advantage of this conference to publicly share a dataset consisting of 25 drivers performing the same road circuit on the "Project Cars" game. [less ▲]

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See detailAn Open Dataset for Human Activity Analysis using Smart Devices
Faye, Sébastien UL; Louveton, Nicolas UL; Jafarnejad, Sasan UL et al

Report (2017)

The study of human mobility and activities has opened up to an incredible number of studies in the past, most of which included the use of sensors distributed on the body of the subject. More recently ... [more ▼]

The study of human mobility and activities has opened up to an incredible number of studies in the past, most of which included the use of sensors distributed on the body of the subject. More recently, the use of smart devices has been particularly relevant because they are already everywhere and they come with accurate miniaturized sensors. Whether it is smartphones, smartwatches or smartglasses, each device can be used to describe complementary information such as emotions, precise movements, or environmental conditions. In this short paper, we release the applications we have developed and an example of a collected dataset. We propose that opening multi-sensors data from daily activities may enable new approaches to studying human behavior. [less ▲]

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See detailTowards a Real-Time Driver Identification Mechanism Based on Driving Sensing Data
Jafarnejad, Sasan UL; Castignani, German UL; Engel, Thomas UL

in 20th International Conference on Intelligent Transportation Systems (ITSC) (2017)

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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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