An online estimation of driving style using data-dependent pointer model

Suzdaleva, Evgenia; Nagy, Iván

doi:10.1016/j.trc.2017.11.001

Cited by 41 publications

(28 citation statements)

References 51 publications

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“…In our future research, we will improve the proposed model from several aspects. Firstly, many researchers have demonstrated that driver's decision to change a lane is also affected by vehicle type and driver's driving skills [36,37]; for instance, a car has different lane-changing factor compared with a bus. However, in this study, we did not consider vehicle type because we used the vehicles of the same type.…”

Section: Discussionmentioning

confidence: 99%

Driver Lane-Changing Behavior Prediction Based on Deep Learning

Wei

Hui

Khattak

2021

Journal of Advanced Transportation

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A correct lane-changing plays a crucial role in traffic safety. Predicting the lane-changing behavior of a driver can improve the driving safety significantly. In this paper, a hybrid neural network prediction model based on recurrent neural network (RNN) and fully connected neural network (FC) is proposed to predict lane-changing behavior accurately and improve the prospective time of prediction. The dynamic time window is proposed to extract the lane-changing features which include driver physiological data, vehicle kinematics data, and driver kinematics data. The effectiveness of the proposed model is validated through the experiments in real traffic scenarios. Besides, the proposed model is compared with five prediction models, and the results show that the proposed prediction model can effectively predict the lane-changing behavior more accurate and earlier than the other models. The proposed model achieves the prediction accuracy of 93.5% and improves the prospective time of prediction by about 2.1 s on average.

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Section: Discussionmentioning

confidence: 99%

Driver Lane-Changing Behavior Prediction Based on Deep Learning

Wei

Hui

Khattak

2021

Journal of Advanced Transportation

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“…• Online learning may be integrated into the proposed framework, since individual driver's driving behaviours may change with more driving experience being accumulated. The design of online learning algorithm can borrow the experiences from (Suzdaleva and Nagy, 2018).…”

Section: Discussionmentioning

confidence: 99%

A machine learning based personalized system for driving state recognition

Liu

et al. 2019

Transportation Research Part C: Emerging Technologies

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Reliable driving state recognition (e.g. normal, drowsy, and aggressive) plays a significant role in improving road safety, driving experience and fuel efficiency. It lays the foundation for a number of advanced functions such as driver safety monitoring systems and adaptive driving assistance systems. In these applications, state recognition accuracy is of paramount importance to guarantee user acceptance. This paper is mainly focused on developing a personalized driving state recognition system by learning from non-intrusive, easily accessible vehicle related measurements and its validation using real-world driving data. Compared to conventional approaches, this paper first highlights the necessities of adopting a personalized system by analysing feature distribution of individual driver's data and all drivers' data via advanced data visualization and statistical analysis. If significant differences are identified, a dedicated personalized model is learnt to predict the driver's driving state. Spearman distance is also drawn to evaluate the differences between individual driver's data and all drivers' data in a quantitative manner. In addition, five categories of classifiers are tested and compared to identify a suitable one for classification, where random forest with Bayesian parameter optimization outperforms others and therefore is adopted in this paper. A recently collected dataset from real-world driving experiments is adopted to evaluate the proposed system. Comparative experimental results indicate that the personalized learning system with road information significantly outperforms conventional approaches without considering personalized characteristics or road information, where the overall accuracy increases from 81.3% to 91.6%. It is believed that the newly developed personalized learning system can find a wide range of applications where diverse behaviours exist.

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“…Although current DSS technologies such as adaptive cruise control and forward collision warning/avoidance systems can help drivers regulate and maintain their speed, modelling these technology features to learn and reflect the risk-averse driving characteristics of older drivers such as maintaining greater headway distance based on the vehicle ahead, or providing alerts of vehicles displaying poor driving behavior can be a useful feature. Current research in automation has increased the focus on developing algorithms that predict driving style, and enable different driving style modes based on the driver’s preference behind the wheel [57]. And lastly, more work needs to be done to address the age differences in driving challenges within the older adult population, as rural drivers tend to be older than urban and suburban older drivers [58].…”

Section: Can Driver Support Systems Help Address Older Driver Chalmentioning

confidence: 99%

Challenges for Older Drivers in Urban, Suburban, and Rural Settings

2018

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Along with age-related factors, geographical settings—urban, suburban, and rural areas—also contribute to the differences in fatal crashes among older drivers. These differences in crash outcomes might be attributed to the various driving challenges faced by older drivers residing in different locations. To understand these challenges from the perspective of the older driver, a focus group study was conducted with drivers 65 and older from urban, suburban, and rural settings. Guided-group interviews were used to assess driving challenges, mobility options, opportunities for driver support systems (DSS), and alternate transportation needs. Content analysis of the interview responses resulted in four categories representing common challenges faced by older drivers across the settings: behavior of other drivers on the road, placement of road signs, reduced visibility of road signs due to age-related decline, and difficulties using in-vehicle technologies. Six categories involved location-specific challenges such as heavy traffic situations for urban and suburban drivers, and multi-destination trips for rural drivers. Countermeasures implemented by older drivers to address these challenges primarily involved route selection and avoidance. Technological advances of DSS systems provide a unique opportunity to support the information needs for route selection and avoidance preferences of drivers. Using the content analysis results, a framework was built to determine additional and modified DSS features to meet the specific challenges of older drivers in urban, suburban, and rural settings. These findings suggest that there is heterogeneity in the driving challenges and preferences of older drivers based on their location. Consequently, DSS technologies and vehicle automation need to be tailored to not only meet the driving safety and mobility needs of older drivers as a population, but also to their driving environment.

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An online estimation of driving style using data-dependent pointer model

Cited by 41 publications

References 51 publications

Driver Lane-Changing Behavior Prediction Based on Deep Learning

Driver Lane-Changing Behavior Prediction Based on Deep Learning

A machine learning based personalized system for driving state recognition

Challenges for Older Drivers in Urban, Suburban, and Rural Settings

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