An unsupervised approach for automotive driver identification

Mainardi, Nicholas; Zanella, Michele; Reghenzani, Federico; Raspa, Niccoló; Brandolese, Carlo

doi:10.1145/3285017.3285023

Cited by 3 publications

(3 citation statements)

References 7 publications

(8 reference statements)

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“…Another approach to take into account for driver identification follows Mainardi et al [ 57 ], who computed the average number of drivers per vehicle for the UK region since this value strongly depends on the geosocial conditions. From the publicly available government data, the average number of adults who drive a car is 1.095 per vehicle.…”

Section: Discussionmentioning

confidence: 99%

Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms

Espino-Salinas

Luna-García

Celaya-Padilla

et al. 2023

Sensors

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Driver identification refers to the process whose primary purpose is identifying the person behind the steering wheel using collected information about the driver him/herself. The constant monitoring of drivers through sensors generates great benefits in advanced driver assistance systems (ADAS), to learn more about the behavior of road users. Currently, there are many research works that address the subject in search of creating intelligent models that help to identify vehicle users in an efficient and objective way. However, the different methodologies proposed to create these models are based on data generated from sensors that include different vehicle brands on routes established in real environments, which, although they provide very important information for different purposes, in the case of driver identification, there may be a certain degree of bias due to the different situations in which the route environment may change. The proposed method seeks to intelligently and objectively select the most outstanding statistical features from motor activity generated in the main elements of the vehicle with genetic algorithms for driver identification, this process being newer than those established by the state-of-the-art. The results obtained from the proposal were an accuracy of 90.74% to identify two drivers and 62% for four, using a Random Forest Classifier (RFC). With this, it can be concluded that a comprehensive selection of features can greatly optimize the identification of drivers.

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Section: Discussionmentioning

confidence: 99%

Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms

Espino-Salinas

Luna-García

Celaya-Padilla

et al. 2023

Sensors

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“…In this section, we present this workflow, which will be accelerated on the M2DC infrastructure. Further details of this workflow can be found in [19].…”

Section: The Driver Identification Scenariomentioning

confidence: 99%

Accelerating Automotive Analytics: The M2DC Appliance Approach

Agosta

Brandolese

Fornaciari

et al. 2019

Lecture Notes in Computer Science

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The Modular Microserver DataCenter (M2DC) project provides lowenergy, configurable, heterogeneous servers for applications that focus on the elaboration of large data sets, but can take advantage of performance enhancement provided by transparent acceleration techniques. In this paper, we exemplify the M2DC approach through one of the project's use cases, namely automotive Internet of Things analytics. We present the main goals of the use case and we show how an appropriate M2DC microserver can be used to accelerate the application without significant modifications to its code.

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“…Finally, we developed a real-world Fog video surveillance application to evaluate the frameworks on a self-built real cluster (SmokyGrill ) equipped with different embedded boards. It is worth to be mentioned that the developed use-case and framework allow us to present the methodology that can be further generalized and also used for other type of Fog scenarios (e.g., automotive [11], health care. .…”

Section: Introductionmentioning

confidence: 99%

BarMan: A run-time management framework in the resource continuum

Zanella

Sciamanna

Fornaciari

2022

Sustainable Computing: Informatics and Systems

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Over the last years, the number of IoT devices has grown exponentially, highlighting the current Cloud infrastructure limitations. In this regard, Fog and Edge computing began to move part of the computation closer to data sources by exploiting interconnected devices as part of a single heterogeneous and distributed system in a computing continuum viewpoint. Since these devices are typically heterogeneous in terms of performance, features, and capabilities, this perspective should encompass programming models and run-time management layers. This work presents and evaluates the BarMan open-source framework by implementing a Fog video surveillance use-case. BarMan leverages a taskbased programming model combined with a run-time resource manager and the novel BeeR framework to deploy the application's tasks transparently. This enables the possibility of considering aspects related to the energy and power dissipated by the devices and the single application. Moreover, we developed a task allocation policy to maximize application performance, considering run-time aspects, such as load and connectivity, of the time-varying available devices. Through an experimental evaluation performed on a real cluster equipped with heterogeneous embedded boards, we evaluated different execution scenarios to show the framework's functionality and the benefit of a distributed approach, leading up to an improvement of 66% on the frame processing latency w.r.t. a monolithic solution.

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An unsupervised approach for automotive driver identification

Cited by 3 publications

References 7 publications

Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms

Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms

Accelerating Automotive Analytics: The M2DC Appliance Approach

BarMan: A run-time management framework in the resource continuum

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