ICT in road vehicles &amp;#x2014; Reliable vehicle sensor information from OBD versus CAN

Szalay, Zsolt; Kánya, Zoltán; Lengyel, László; Ekler, Péter; Ujj, Tamas; Balogh, Tamás; Charaf, Hassan

doi:10.1109/mtits.2015.7223296

Cited by 14 publications

(9 citation statements)

References 2 publications

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“…The frequency of the OBD-II port is 1 Hz. while the frequency of the CAN bus could be higher however it was limited to 5 Hz in our measurement setup (as the limited throughput for uploading samples) [13]. Another difference is the amount of parameters.…”

Section: Discussionmentioning

confidence: 99%

“…In Figure 2b a CAN bus gateway is presented. This device based on FMS standards [13] (Fleet Management System) was made by the Inventure Inc. [2] mainly for fleet management for trucks so a lot of special parameters are available like pedal positions, tachograph data, driver identification, etc. However these gateways are car-specific, more expensive than the OBD-II adapters and also their installation is more difficult in a vehicle.…”

Section: Vehicle As a Set Of Sensorsmentioning

confidence: 99%

“…A previous research [13] introduced a measurement tool and its environment from collecting parameters from vehicles. By using one smartphone with this tool, it was possible to collect the data only from one interface in the same time.…”

Section: Introductionmentioning

confidence: 99%

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Gamification and driving decision support using the sensors of vehicles and smartphones

Sik¹,

Ekler²,

Lengyel³

2017

IDT

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Vehicles are not just a means of transportation anymore, but they are also moving entities that collect and transmit a great amount of data, captured by sensors from the internals of the vehicle and from its environment as well. These data extended with smartphone sensors can be a stable base for smart car-based infocommunication systems. There are two main interfaces to extract the data from a vehicle: OBD-II (On-board Diagnostics) and CAN bus (Controller Area Network bus). This paper introduces an advanced measurement tool, called ObdCanCompare. The purpose of this tool is to provide an environment to compare these data streams in real time and conclude how and which interface can be used for derivation of driving decisions. It also enables creating business intelligence reports using the SensorHUB-based backend, which is our data collection and service development for sensor networks, smart devices and cloud-based backend environments. Results show that the OBD-II interface is quite accurate compared to CAN bus, in order to demonstrate this we have created the Social Driving application as a socio-cognitive ICT pilot application that displays vehicle data to users from OBD-II and enables to compare usage statistics of different drivers in the network as a socio-cognitive gamification.

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Section: Discussionmentioning

confidence: 99%

Section: Vehicle As a Set Of Sensorsmentioning

confidence: 99%

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Gamification and driving decision support using the sensors of vehicles and smartphones

Sik¹,

Ekler²,

Lengyel³

2017

IDT

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“…CAN is an overloaded term Szalay et al (2015). Originally, CAN refers to the ISO standard 11898-1 specifying the physical and data link layers of the CAN protocol stack.…”

Section: Can: Controller Area Networkmentioning

confidence: 99%

Extracting Vehicle Sensor Signals from CAN Logs for Driver Re-identification

Lestyán

Ács

Biczók

et al. 2019

Proceedings of the 5th International Conference on Information Systems Security and Privacy

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Data is the new oil for the car industry. Cars generate data about how they are used and who's behind the wheel which gives rise to a novel way of profiling individuals. Several prior works have successfully demonstrated the feasibility of driver re-identification using the in-vehicle network data captured on the vehicle's CAN (Controller Area Network) bus. However, all of them used signals (e.g., velocity, brake pedal or accelerator position) that have already been extracted from the CAN log which is itself not a straightforward process. Indeed, car manufacturers intentionally do not reveal the exact signal location within CAN logs. Nevertheless, we show that signals can be efficiently extracted from CAN logs using machine learning techniques. We exploit that signals have several distinguishing statistical features which can be learnt and effectively used to identify them across different vehicles, that is, to quasi "reverse-engineer" the CAN protocol. We also demonstrate that the extracted signals can be successfully used to re-identify individuals in a dataset of 33 drivers. Therefore, not revealing signal locations in CAN logs per se does not prevent them to be regarded as personal data of drivers.

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“…Sensors attached to their respective ECUs, whose number ranges from 5 to a hundred per vehicle, continuously generate a vast amount of realtime data. In order to implement complex control tasks for traffic safety or passenger infotainment, these data are then transferred among ECUs and other nodes over the in-vehicle network, most commonly following the Controller Area Network (CAN bus) standard [1]. In addition to being used real-time for automotive control, data are also transferred to car manufacturers through Internet-connected devices or pre-installed modems every half to five minutes.…”

Section: Introductionmentioning

confidence: 99%

Automatic Driver Identification from In-Vehicle Network Logs

Remeli¹,

Lestyán²,

Ács³

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Data generated by cars is growing at an unprecedented scale. As cars gradually become part of the Internet of Things (IoT) ecosystem, several stakeholders discover the value of in-vehicle network logs containing the measurements of the multitude of sensors deployed within the car. This wealth of data is also expected to be exploitable by third parties for the purpose of profiling drivers in order to provide personalized, valueadded services. Although several prior works have successfully demonstrated the feasibility of driver re-identification using the in-vehicle network data captured on the vehicle's CAN (Controller Area Network) bus, they inferred the identity of the driver only from known sensor signals (such as the vehicle's speed, brake pedal position, steering wheel angle, etc.) extracted from the CAN messages. However, car manufacturers intentionally do not reveal exact signal location and semantics within CAN logs. We show that the inference of driver identity is possible even with off-the-shelf machine learning techniques without reverse-engineering the CAN protocol. We demonstrate our approach on a dataset of 33 drivers and show that a driver can be re-identified and distinguished from other drivers with an accuracy of 75-85%. 2 bit.ly/2V14YkK 3 www.wsj.com/articles/what-your-car-knows-about-you-1534564861

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ICT in road vehicles — Reliable vehicle sensor information from OBD versus CAN

Cited by 14 publications

References 2 publications

Gamification and driving decision support using the sensors of vehicles and smartphones

Gamification and driving decision support using the sensors of vehicles and smartphones

Extracting Vehicle Sensor Signals from CAN Logs for Driver Re-identification

Automatic Driver Identification from In-Vehicle Network Logs

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