Augmented naive Bayesian network for driver behavior modeling

Bouslimi, Wajih; Kassaagi, Mohamed; Lourdeaux, Domitile; Fuchs, Philippe

doi:10.1109/ivs.2005.1505108

Cited by 12 publications

(10 citation statements)

References 16 publications

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“…ese studies have devoted most of their emphasis on the typical driving conditions. ere are two modeling routines to follow: the performance model and the cognitive model [26].…”

Section: Literature Review On Bayesian Network (Bn)mentioning

confidence: 99%

Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors

Naderi

Nassiri

Zahedieh

2021

Journal of Advanced Transportation

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Traffic collisions are one of the most important challenges threatening the general health of the world. Iran’s crash statistics demonstrate that approximately 16,500 people lose their lives every year due to road collisions. According to the traffic police of Iran, heavy vehicles (including trailers, trucks, and panel trucks) contributed to 20.5% of the fatal road traffic collisions in the year 2013. This highlights the need for devoting special attention to heavy vehicle drivers to further explore their driving characteristics. In this research, the effect of heavy vehicle drivers’ behavior on at-fault collisions over three years has been investigated with an innovative approach of structural equation modeling (SEM) and Bayesian Network (BN). The database utilized in this research was collected using a questionnaire. For this purpose, 474 heavy vehicle drivers have been questioned in the Parviz Khan Border Market, located on the border of Iran and Iraq. The response rate of the survey was 80%. The participants answered the questions on Driver Behavior Questionnaire (DBQ) and a sleep assessing questionnaire named Global Dissatisfaction with Sleep (GSD). In this research, human factors affecting at-fault collisions of heavy vehicles were identified and their relationships with other variables were determined using the SEM approach. Then the descriptive model constructed by the SEM method was used as the basis of the BN, and the conditional probabilities of each node in the BN were calculated by the database collected by the field survey. SEM indicates that other attributes including GSD, mobile usage, daily fatigue, exposure, and education level have an indirect relation with heavy vehicle drivers’ at-fault collisions. According to the BN, if there is no information about the characteristics of a heavy vehicle driver, the driver will likely have at least one collision during the next three years with the probability of 0.17. Also, it was indicated that the minimum probability of the at-fault collision occurrence for a heavy vehicle is 0.08.

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“…ese studies have devoted most of their emphasis on the typical driving conditions. ere are two modeling routines to follow: the performance model and the cognitive model [26].…”

Section: Literature Review On Bayesian Network (Bn)mentioning

confidence: 99%

Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors

Naderi

Nassiri

Zahedieh

2021

Journal of Advanced Transportation

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“…Several research works propose complex models for predicting the human driver intentions based on a variety of indirect intention measurements, e.g. steering input, head position, subject position in the lane and presence of objects [BKLF05,MDT11]. However, complicated indirect measurements only give a guess on human intentions.…”

Section: Longitudinal Controlmentioning

confidence: 99%

Highly Automated Driving on Highways Based on Legal Safety

Vanholme¹,

Gruyer²,

Lusetti³

et al. 2013

IEEE Trans. Intell. Transport. Syst.

116

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Vehicle automation is proposed as one of the solutions to make transport safer, more comfortable and more environmentally friendly. It is gradually being introduced through Advanced Driver Assistance Systems (ADAS). ADAS started as Anti-lock Braking Systems (ABS) and have now extended to Adaptive Cruise Control (ACC) and Lane Keeping Assist Systems (LKAS). This work aims to contribute to this evolution, by discussing how driving systems can share the road with human drivers. It presents the legal safety concept for the design of a highly automated driving system for highways. The legal safety concept proposes to base driving system design on traffic rules. This allows fully automated driving in traffic with human drivers, without necessarily changing equipment on other vehicles or infrastructure. The driving system uses traffic rules to predict legal or nonlegal trajectories of objects in its perception zone and worst-case objects outside its perception zone. If all objects respect traffic rules, accidents will be avoided. If not, driving defensively will avoid most accidents. Today, international law allows highly automated driving, but not yet fully automated driving. The driving system interacts with the human driver, via human rules. The HAVEit project (European Seventh Framework Programme) and ABV project (French National Research Agency) propose human rules based on the horse-rider metaphor (Hmetaphor). If needed, the driving system takes over control in order to avoid accidents. If the human driver cannot continue driving, the driving system brings the vehicle to a safe standstill on the emergency lane. The consequences of these human rules on driving system design are explored. System rules form the third set of rules of the legal safety concept. With system rules, system components respect the limitations of other system components. The requirements on perception, control and Human-Machine Interface (HMI) components of the legal safety system are discussed. The decision component, which is the central component of the legal safety system, is completely worked out from requirements to design. The legal safety system has been implemented on PC and automotive Electronic Control Units (ECUs). The integration and validation of legal safety components on LIVIC, HAVEit and ABV demonstrators are presented. The work concludes that, for highway environments, legal safety decision, control and HMI can be achieved with state-of-the-art technology, and legal safety perception could be available in medium term.

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“…Several studies monitor and detail the operational actions of drivers in these critical situations [16], [17], [18], some with an eye toward prediction [19], [20], [21], [22] and taking corrective actions in case of emergencies [23], [24]. Due to the proliferation of good surveys and literature on operational maneuvers [10], [11], [12], we instead focus more on tactical maneuvers below.…”

Section: Time-scalementioning

confidence: 99%

Tactical driver behavior prediction and intent inference: A review

Doshi

Trivedi

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

104

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Abstract-Drawing upon fundamental research in human behavior prediction, recently there has been a research focus on how to predict driver behaviors. In this paper we review the field of driver behavior and intent prediction, with a specific focus on tactical maneuvers, as opposed to operational or strategic maneuvers. The aim of a driver behavior prediction system is to forecast the trajectory of the vehicle prior in real-time, which could allow a Driver Assistance System to compensate for dangerous or uncomfortable circumstances. This review provides insights into the scope of the problem, as well as the inputs, algorithms, performance metrics, and shortcomings in the state-of-the-art systems.

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Augmented naive Bayesian network for driver behavior modeling

Cited by 12 publications

References 16 publications

Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors

Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors

Highly Automated Driving on Highways Based on Legal Safety

Tactical driver behavior prediction and intent inference: A review

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