Multitask Learning Assisted Driver Identity Authentication and Driving Behavior Evaluation

Xun, Yijie; Li, Jiajia; Shi, Zhenjiang

doi:10.1109/tii.2020.3034276

Cited by 18 publications

(6 citation statements)

References 23 publications

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“…Therefore, the CP system using random forest was able to predict CS and NCS with high accuracy based on θ ( y ) avg and ω ( x ) avg of an older driver while preparing to start the car. Related works predicted driver risk [ 33 ], typical dangerous driving behavior such as chasing a preceding vehicle [ 34 ] or driving while operating a mobile phone [ 35 ], and illegal drivers [ 36 ]. However, there is still not a prediction about the coping skills of older drivers in the face of unexpected situations; although, it is not possible to make a direct comparison because the prediction targets are different, the related study predicts dangerous driving behavior with an accuracy of 92% [ 34 ], and the accuracy of this study is about the same even when compared with related work.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Coping Skills of Older Drivers in the Face of Unexpected Situation

Kajiwara

Kimura

2021

Sensors

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In recent years, when an older driver who cannot immediately recognize, judge, and operate properly faces an unexpected situation, they often panic, which may cause a traffic accident. However, there has not yet been enough discussion about the coping skills of older drivers in the face of this unexpected situation. Therefore, this study discusses the coping skills of older drivers in the face of unexpected situations. Moreover, we propose a coping skills prediction system (CP system). The CP system predicts coping skills from the tilt angle and angular velocity of the left foot when an older driver is driving or preparing to start a car. The experiment carried out two phases, a phase of driving a car and a phase of preparing to start the car. In the driving phase, the young and older driver drive the car in a driving simulator. The average age of the young driver group was ± standard deviation = 20.6 ± 0.7 years, and the age of the older driver group was 78.5 ± 5.1 years. The driving route included 15 cases in which collision accidents are likely to occur. We analyzed the experimental results of the driving phase and clarified the predictors of coping skills. Moreover, we analyzed the correlation between the left foot movement in driving and the left foot movement during preparing to start the car. As a result of the experiment, there was a 0.84 correlation between the tilt angle of the left foot of the older driver in driving and the tilt angle of the left foot of the older driver in preparing to start the car. The result shows that the coping skills can be predicted from the tilt angle of the left foot of the older driver during preparing to start the car. We showed that the coping skill can be predicted with an accuracy of 92% or 94% on average from the tilt angle and the angular velocity of the left foot while driving or preparing to start the car. Moreover, we clarified that the tilt angle of the left foot of a driver without coping skills is perpendicular to the ground compared to a driver with coping skills. This study is expected to contribute to the prevention of traffic accidents that occur in the face of an unexpected situation.

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Section: Discussionmentioning

confidence: 99%

Predicting the Coping Skills of Older Drivers in the Face of Unexpected Situation

Kajiwara

Kimura

2021

Sensors

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“…Emerging AI-driven techniques are deemed essential towards efficient and scalable vehicle authentication without the need to rely on cryptographic attributes [353], [354], [360]. This stems from their ability to enforce adaptive access-control policies, by exploiting the time-varying non-cryptographic features which are intrinsically associated with vehicles' behaviors and the environment.…”

Section: ) Authentication and Access Controlmentioning

confidence: 99%

A Comprehensive Survey of V2X Cybersecurity Mechanisms and Future Research Paths

Sedar

Kalalas

Vázquez-Gallego

et al. 2023

IEEE Open J. Commun. Soc.

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Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.

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“…One of the central promises of multi-task learning is that the shared representation has more generalization power since it needs to learn a more general representation useful for multiple tasks. The use of multi-task learning has been on the rise in the vehicular domains in the past few years, and it has been used in different contexts, including predictive maintenance, autonomous driving, drivers' behavior reasoning, to name just a few (Chowdhuri, Pankaj, & Zipser, 2019;Xie, Hu, Li, & Guo, 2021;Xun, Liu, & Shi, 2020). For example, Chowdhuri et al (2019) proposed a unified multi-scale, multi-task learning for drivers' behavior recognition.…”

Section: Multi-task Learningmentioning

confidence: 99%

“…An interesting work has been done by Xun et al (2020) by adopting multi-task learning for three purposes: illegal driver detection, legal driver identification, and driving behavior evaluation. They collected driving data from on-board diagnostic systems installed on different vehicles.…”

Section: Multi-task Learningmentioning

confidence: 99%

Predicting Vehicle Behavior Using Multi-task Ensemble Learning

Khoshkangini¹,

Mashhadi²,

Tegnered³

et al. 2023

Expert Systems with Applications

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Multitask Learning Assisted Driver Identity Authentication and Driving Behavior Evaluation

Cited by 18 publications

References 23 publications

Predicting the Coping Skills of Older Drivers in the Face of Unexpected Situation

Predicting the Coping Skills of Older Drivers in the Face of Unexpected Situation

A Comprehensive Survey of V2X Cybersecurity Mechanisms and Future Research Paths

Predicting Vehicle Behavior Using Multi-task Ensemble Learning

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