Behavior Measurement, Analysis, and Regime Classification in Car Following

Ma, Xiaoliang; Andréasson, Ingmar

doi:10.1109/tits.2006.883111

Cited by 85 publications

(50 citation statements)

References 18 publications

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“…The emphasis of this paper is more towards identifying some of the actions taken by a driver, such as turning or braking. Ma [3] used a fuzzy clustering algorithm to analyze human driving behavior with respect to car following and lane change maneuvering based on longitudinal and lateral acceleration, applied brake pressure, engine speed and some GPS data. Van [4] explored the possibility of using the vehicle's inertial sensors from the CAN bus to build a classify driving.…”

Section: Introductionmentioning

confidence: 99%

Driving Style Recognition Based on Driver Behavior Questionnaire

Li¹,

Shi²,

Liu³

2017

OJAppS

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The ability to classify driver behavior lays the foundation for more advanced driver assistance systems. The present study aims to research driver pattern and classification feature. Driver behavior self-reported investigation was conducted with standardized driver behavior questionnaire (DBQ) by 225 nonprofessional drivers on the internet in Beijing. Questionnaire's reliability was verified by statistics analysis. Confirmatory factor analysis (CFA) was used to analyze the underlying factor structure. Speed advantage, space occupation, the contend right of way and the contend space advantage were extracted from the questionnaire results to quantify driver characteristics. Based on fuzzy C-means (FCM) algorithm and taking the four factors as pattern features, the number of driver classification distribution was discussed. Then the number of driver classification was determined by statistical indices. The comparison of classification results with the survey finding on whether the driver occurred in traffic accidents within five years shows that the classification result is the same as the actual driving conditions. Finally, correlation between the demographic and types of driving behavior has been analyzed. Female were more likely than male to careful driving, and the older the driver and the less driving experience, the more careful and moderate driving behavior is.

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Section: Introductionmentioning

confidence: 99%

Driving Style Recognition Based on Driver Behavior Questionnaire

Li¹,

Shi²,

Liu³

2017

OJAppS

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“…Moreover, the risk of overfitting (or at least unnecessary over-complexity) is high 24 in the case of models with several parameters (Punzo et al, 2015). 25 The first interpretative paradigm is based on a state-space model applied to car following. Indeed, car following can 26 be viewed as a time-continuous dynamic process; for instance, it can be represented (see Wilson, 2008) with equation 27 1), where for the sake of simplicity the so-called additive acceleration (representing a random noise) has been omitted: 28…”

Section: Collision 26mentioning

confidence: 99%

“…Among these we employed for our analyses: 21  the speed of the follower; 22  the relative speed between the leader and the follower; 23  the relative spacing between the leader and the follower. 24 25 As stated in section 2, the approach followed here is twofold. First the equilibrium conditions of observed car-following 26 are directly analysed.…”

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confidence: 99%

Longitudinal control behaviour: Analysis and modelling based on experimental surveys in Italy and the UK

Pariota

Bifulco

Galante

et al. 2016

Accident Analysis & Prevention

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5This paper analyses driving behaviour in car-following conditions, based on extensive individual vehicle data collected 6 during experimental field surveys carried out in Italy and the UK. The aim is to contribute to identify simple evidence to 7 be exploited in the ongoing process of driving assistance and automation which, in turn, would reduce rear-end crashes. 8In particular, identification of differences and similarities in observed car-following behaviours for different samples of 9 drivers could justify common tuning, at a European or worldwide level, of a technological solution aimed at active 10 safety, or, in the event of differences, could suggest the most critical aspects to be taken into account for localisation or 11 customisation of driving assistance solutions. Without intending to be exhaustive, this paper moves one step in this 12 direction. Indeed, driving behaviour and human errors are considered to be among the main crash contributory factors, 13 and a promising approach for safety improvement is the progressive introduction of increasing levels of driving 14 automation in next-generation vehicles, according to the active/preventive safety approach. However, the more 15 advanced the system, the more complex will be the integration in the vehicle, and the interaction with the driver may 16 sometimes become unproductive, or risky, should the driver be removed from the driving control loop. Thus, 17 implementation of these systems will require the interaction of human driving logics with automation logics and then an 18 enhanced ability in modelling drivers' behaviour. This will allow both higher active-safety levels and higher user 19 acceptance to be achieved, thus ensuring that the driver is always in the control loop, even if his/her role is limited to 20 supervising the automatic logic. Currently, the driving mode most targeted by driving assistance systems is longitudinal 21 driving. This is required in various driving conditions, among which car-following assumes key importance because of 22 the huge number of rear-end crashes. 23The increased availability of lower-cost information and communication technologies (ICTs) has enhanced the 24 possibility of collecting copious and reliable car-following individual vehicle data. In this work, data collected from 25 three different experiments, two carried out in Italy and one in the UK, are analysed and compared. The experiments 26 involved 146 drivers (105 Italian drivers and 41 UK drivers). Data were collected by two instrumented vehicles. 27Our analysis focused on inter-vehicular spacing in equilibrium car-following conditions. We observed that (i) the 28 adopted equilibrium spacing can be fitted using lognormal distributions, (ii) the adopted equilibrium spacing increases 29 with speed, and (iii) the dispersion between drivers increases with speed. In addition, according to different headway 30 thresholds (up to 1 second) a significant number of potentially dangerous behaviours is observed. 31Three different car-following paradig...

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“…2) Feature selection: The commonly used criteria for feature selection [13] are as follows: (i) the feature has strong correlation to the variable of interest (i.e., acceleration in our case); (ii) the features are easy to observe/acquire. Following these criterion in conjunction with the observations in…”

Section: A Off-line Trainingmentioning

confidence: 99%

“…Unsupervised machine learning technique (i.e., Fuzzy C-Mean Clustering (FCM)) is used to achieve this task, since unlike supervised methods, little prior information is required and no labelling is needed for large amount of data. The idea of using unsupervised clustering techniques in the field of intelligent vehicle have been previously investigated in [13], [14], which mainly focused on driving style identification.…”

Section: Introductionmentioning

confidence: 99%

Data-driven situation awareness algorithm for vehicle lane change

Liu

2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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Data-Driven Situation Awareness Algorithm for Vehicle Lane ChangeDewei Yi, Jinya Su * , Cunjia Liu and Wen-Hua Chen Abstract-A good level of situation awareness is critical for vehicle lane change decision making. In this paper, a DataDriven Situation Awareness (DDSA) algorithm is proposed for vehicle environment perception and projection using machine learning algorithms in conjunction with the concept of multiple models. Firstly, unsupervised learning (i.e., Fuzzy C-Mean Clustering (FCM)) is drawn to categorize the drivers' states into different clusters using three key features (i.e., velocity, relative velocity and distance) extracted from Intelligent Driver Model (IDM). Statistical analysis is conducted on each cluster to derive the acceleration distribution, resulting in different driving models. Secondly, supervised learning classification technique (i.e., Fuzzy k-NN) is applied to obtain the model/cluster of a given driving scenario. Using the derived model with the associated acceleration distribution, Kalman filter/prediction is applied to obtain vehicle states and their projection. The publicly available NGSIM dataset is used to validate the proposed DDSA algorithm. Experimental results show that the proposed DDSA algorithm obtains better filtering and projection accuracy in comparison with the Kalman filter without clustering.

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Behavior Measurement, Analysis, and Regime Classification in Car Following

Cited by 85 publications

References 18 publications

Driving Style Recognition Based on Driver Behavior Questionnaire

Driving Style Recognition Based on Driver Behavior Questionnaire

Longitudinal control behaviour: Analysis and modelling based on experimental surveys in Italy and the UK

Data-driven situation awareness algorithm for vehicle lane change

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