Recognition of the Driving Style in Vehicle Drivers

Cordero, Jorge; Aguilar, José; Aguilar, Kristell; Chávez, Danilo; Puerto, Eduard

doi:10.3390/s20092597

Cited by 31 publications

(18 citation statements)

References 36 publications

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“…conditions, lights, etc. ), in-vehicle sensors for drivers [5] and in-vehicle monitors of driving statistics [17,25]; and ➋ for Processing-Outside-Vehicles, other applications may need assistance from centralized servers within IoV , due to either the application requirements or computational powers. There are many examples of applications, which require data aggregations in centralized servers.…”

Section: Classifying Application Of Iov : Processing Inside or Outsid...mentioning

confidence: 99%

Characterizing Differentially-Private Techniques in the Era of Internet-of-Vehicles

Duan¹,

Liu²,

Jin³

et al. 2022

Preprint

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Recent developments of advanced Human-Vehicle Interactions rely on the concept Internet-of-Vehicles (IoV ), to achieve large-scale communications and synchronizations of data in practice. The concept of IoV is highly similar to a distributed system, where each vehicle is considered as a node and all nodes are grouped with a centralized server. In this manner, the concerns of data privacy are significant since all vehicles collect, process and share personal statistics (e.g. multi-modal, driving statuses and etc.). Therefore, it's important to understand how modern privacy-preserving techniques suit for IoV .We present the most comprehensive study to characterize modern privacy-preserving techniques for IoV to date. We focus on Differential Privacy (DP), a representative set of mathematicallyguaranteed mechanisms for both privacy-preserving processing and sharing on sensitive data. The purpose of our study is to demystify the tradeoffs of deploying DP techniques, in terms of service quality. We first characterize representative privacy-preserving processing mechanisms, enabled by advanced DP approaches. Then we perform a detailed study of an emerging in-vehicle, Deep-Neural-Network-driven application, and study the upsides and downsides of DP for diverse types of data streams. Our study obtains 11 key findings and we highlight FIVE most significant observations from our detailed characterizations. We conclude that there are a large volume of challenges and opportunities for future studies, by enabling privacy-preserving IoV with low overheads for service quality.

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Section: Classifying Application Of Iov : Processing Inside or Outsid...mentioning

confidence: 99%

Characterizing Differentially-Private Techniques in the Era of Internet-of-Vehicles

Duan¹,

Liu²,

Jin³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…PCA is employed for driving style analysis; however, the questionnaire depends on subject judgement of how was driving experience under different circumstances. Cordero et al proposed a hierarchical model for driving style recognition [24]. e driving style is composed of three levels of features including emotional state, driver state, and driving style.…”

Section: Introductionmentioning

confidence: 99%

Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model

Chen

Zhang

et al. 2021

Journal of Advanced Transportation

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In recent years, the automated driving system has been known to be one of the most popular research topics of artificial intelligence (AI) and intelligent transportation system (ITS). The journey experience on automated vehicles and the intelligent automated driving system could be improved by individualization driving understanding. Although previous studies have proposed methods for driving styles understanding, the individualization driving classification has not been addressed thoroughly. Therefore, in this study, a supervised method is proposed to understand driving behavioral structure and the latent driving styles by incorporating the prior knowledge. Firstly, a novel method is established for driving behavioral encoding and raw driving data mining. Then, the Labeled Latent Dirichlet Allocation (LLDA) is proposed to understand the latent driving styles from individual driving with driving behaviors. Finally, the Safety Pilot Model Deployment (SPMD) data are used to validate the performance of the proposed model. Experimental results show that the proposed model uncovers latent driving styles effectively and shows good agreement to real situations, which provides theoretical guidance on driving behavior recognition for better individual experience on automated driving vehicles.

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“…The relationship between drivers and ADAS is an important aspect to consider, and more specifically, how ADAS can adapt to the characteristics of each driver. A model for incorporating human factors into an ADAS is proposed in [ 12 ]. Other safety-related applications where driving style awareness is of interest include driving fatigue detection [ 13 ] and distraction detection [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Holistic Vehicle Instrumentation for Assessing Driver Driving Styles

Garrosa

Olmeda

Toro

et al. 2021

Sensors

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Nowadays, autonomous vehicles are increasing, and the driving scenario that includes both autonomous and human-driven vehicles is a fact. Knowing the driving styles of drivers in the process of automating vehicles is interest in order to make driving as natural as possible. To this end, this article presents a first approach to the design of a controller for the braking system capable of imitating the different manoeuvres that any driver performs while driving. With this aim, different experimental tests have been carried out with a vehicle instrumented with sensors capable of providing real-time information related to the braking system. The experimental tests consist of reproducing a series of braking manoeuvres at different speeds on a flat floor track following a straight path. The tests distinguish between three types of braking manoeuvre: maintained, progressive and emergency braking, which cover all the driving circumstances in which the braking system may intervene. This article presents an innovative approach to characterise braking types thanks to the methodology of analysing the data obtained by sensors during experimental tests. The characterisation of braking types makes it possible to dynamically classify three driving styles: cautious, normal and aggressive. The proposed classifications allow it possible to identify the driving styles on the basis of the pressure in the hydraulic brake circuit, the force exerted by the driver on the brake pedal, the longitudinal deceleration and the braking power, knowing in all cases the speed of the vehicle. The experiments are limited by the fact that there are no other vehicles, obstacles, etc. in the vehicle’s environment, but in this article the focus is exclusively on characterising a driver with methods that use the vehicle’s dynamic responses measured by on-board sensors. The results of this study can be used to define the driving style of an autonomous vehicle.

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Recognition of the Driving Style in Vehicle Drivers

Cited by 31 publications

References 36 publications

Characterizing Differentially-Private Techniques in the Era of Internet-of-Vehicles

Characterizing Differentially-Private Techniques in the Era of Internet-of-Vehicles

Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model

Holistic Vehicle Instrumentation for Assessing Driver Driving Styles

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