A Driver Steering Model With Personalized Desired Path Generation

Schnelle, Scott; Wang, Junmin; Su, Hai‐Jun; Jagacinski, Richard J.

doi:10.1109/tsmc.2016.2529582

Cited by 97 publications

(44 citation statements)

References 35 publications

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“…Here, the fuzzy c-mean clustering algorithm is combined with Kalman filter to estimate the distance from following vehicle to the heading vehicle more accurately. Another approach implements personalized lane changing by proposing a compensatory transfer function based on a driver model in combination with a feedforward anticipatory subsystem [27]. Furthermore, [85] learns a driver's steering characteristics by using inverse optimal control.…”

Section: Lane Change Assistancementioning

confidence: 99%

“…guiding drivers to rest stops, alerting drivers) can be provided. It is noticeable that the generic approach trains or designs a model by using the driving data of all drivers indiscriminately, and, as a result, personalized driving characteristics and preferences of individual drivers may be neglected [27]. In practice, different drivers may have distinct driving characteristics and preferences even in a similar driving scenario [3].…”

Section: Introductionmentioning

confidence: 99%

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Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

et al. 2020

IEEE Trans. Intell. Veh.

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In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With considering personal driving preferences and characteristics, these systems become more acceptable and trustworthy. This paper presents a survey of recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, gains of personalization, application prospects, and future focal points. Several existing driving datasets are summarized and open issues of personalized driving assistance are also suggested to facilitate future research. By creating an organized categorization of the field, this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the use of these techniques by researchers within the driving automation community.

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Section: Lane Change Assistancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

et al. 2020

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

show abstract

“…The concept of personalized system is now drawing increasing attention in a wide range of applications. For example, to make ADAS cooperate better with human drivers, personalized driver steering model is developed in [1] by considering each driver's desired path. Personalized driver modeling is used in [2] to describe driving behavior so that driving styles are evaluated in a better way.…”

Section: Personalized Driver Workload Inferencementioning

confidence: 99%

“…R ECENT interest in intelligent vehicles is more concentrated on how to enhance safety and convenience to drivers [1]- [4]. Different advanced functions are being developed for such a purpose.…”

Section: Introductionmentioning

confidence: 99%

Personalized Driver Workload Inference by Learning From Vehicle Related Measurements

Liu

2019

IEEE Trans. Syst. Man Cybern, Syst.

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Adapting in-vehicle systems (e.g., advanced driver assistance systems and in-vehicle information systems) to individual drivers' workload can enhance both safety and convenience. To make this possible, it is a prerequisite to infer driver workload so that adaptive aiding can be provided to the driver at the right time and in an appropriate manner. Rather than developing an average model for all drivers, a personalized driver workload inference (PDWI) system considering individual drivers driving characteristics is developed using machine learning techniques via easily accessed vehicle related measurements (VRMs). The proposed PDWI system comprises two stages. In offline training, individual drivers workload is first automatically splitted into different categories according to its inherent data characteristics using fuzzy C-means (FCM) clustering. Then an implicit mapping between VRMs and different levels of workload is constructed via classification algorithms. In online implementation, VRMs samples are classified into different clusters, consequently driver workload type can be successfully inferred. A recently collected dataset from real-world naturalistic driving experiments is drawn to validate the proposed PDWI system. Comparative experimental results indicate that the proposed framework integrating FCM clustering and support vector machine classifier provides a promising workload recognition performance in terms of accuracy, precision, recall, F 1 -score, and prediction time. The interindividual differences in term of workload are also identified and can be accommodated by the proposed framework due to its adaptiveness.Index Terms-Fuzzy C-means (FCM) clustering, personalized aiding, support vector machine (SVM), workload recognition.

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“…So a personalized system is not applicable. One has to rely on an average system, which may result in a limited performance since the driving characteristics have not been accommodated [14]. Therefore, this paper aims to tackle the problem of workload prediction for new drivers so that user confidence in DWPSs is not compromised due to the new user problem.…”

Section: Introductionmentioning

confidence: 99%

New Driver Workload Prediction Using Clustering-Aided Approaches

Liu

2019

IEEE Trans. Syst. Man Cybern, Syst.

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Awareness of driver workload plays a paramount role in enhancing driving safety and convenience for intelligent vehicles. The Driver Workload Prediction Systems (DWPSs) proposed so far learn either from individual driver's data (termed personalized system) or existing drivers' data indiscriminately (termed average system). As a result, they either do not work or lead to a limited performance for new drivers without labelled data. To this end, we develop clustering-aided approaches exploiting group characteristics of the existing drivers' data. Two clustering aided predictors are proposed. The first is Clustering-Aided Regression (CAR) model, where the regression model for the cluster with the highest likelihood is adopted. The second is Clustering-Aided Multiple Model Regression (CAMMR) model, where the concept of multiple models is further augmented to CAR. A recent dataset from real-world driving experiments is adopted to validate the algorithms. Comparative results against the conventional average system demonstrate that by incorporating clustering information, both the proposed approaches significantly improve workload prediction performance.

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A Driver Steering Model With Personalized Desired Path Generation

Cited by 97 publications

References 35 publications

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

Personalized Driver Workload Inference by Learning From Vehicle Related Measurements

New Driver Workload Prediction Using Clustering-Aided Approaches

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