Longitudinal driving behavior under adverse weather conditions: adaptation effects, model performance and freeway capacity in case of fog

Hoogendoorn, Raymond; Tamminga, Guus; Hoogendoorn, Serge P.; Daamen, Winnie

doi:10.1109/itsc.2010.5625046

Cited by 33 publications

(28 citation statements)

References 11 publications

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“…The weather impacts on the speed parameters comply with the results presented in [17]. The added weather conditions can represent the weather in Egypt, specifically the weather of Alexandria city that include; light rain, normal rain, heavy rain, light fog, heavy fog, and sandstorm with 5%, 10%, 12%, 25%, 30%, 36% speed reduction than dry weather, respectively.…”

Section: Impact Of Adverse Weather On Traffic Conditionssupporting

confidence: 62%

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Multi-objective traffic light control system based on Bayesian probability interpretation

Khamis

Gomaa

El‐Shishiny

2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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Traffic light control is a challenging problem in modern societies. This is due to the huge number of vehicles and the high dynamics of the traffic system. Poor traffic management causes a high rate of accidents, time losses, and negative impact on the economy as well as the environment. In this paper, we develop a multiagent traffic light control system based on a multi-objective sequential decision making framework. In order to respond effectively to the changing environment and the non-stationarity of the road network, the proposed traffic light controller is based on the Bayesian interpretation of probability. We use the open source Green Light District (GLD) vehicle traffic simulator as a testbed framework. The change in road conditions is modeled by varying the vehicles generation probability distributions and adapting the Intelligent Driver Model (IDM) parameters to the adverse weather conditions. We have added a set of new performance indices in GLD based on collaborative learning to better evaluate the performance of the proposed multi-objective traffic light controller. The results show that the proposed multiobjective controller outperforms the single-objective controller.

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Section: Impact Of Adverse Weather On Traffic Conditionssupporting

confidence: 62%

“…The results presented in [17] show that adverse weather conditions (specifically fog) led to a decrease in the desired speed, v 0 . A substantial increase is observed in both of the distance to the front vehicle, s 0 , and the minimum headway, T .…”

Section: Impact Of Adverse Weather On Traffic Conditionsmentioning

confidence: 83%

Multi-objective traffic light control system based on Bayesian probability interpretation

Khamis

Gomaa

El‐Shishiny

2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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“…Billot et al also assessed the rain impact in time headways: A drop of 12.1% of the time headway less than 2 seconds is observed under light rainy conditions, while in heavy rainy conditions, a sharper decrease of more than 18% is observed (a rise of the time headway between 2 and 10 s). Hoogendoorn et al observed an increase of the time headway from an average of around 1.5 seconds in normal conditions to a very high value of around 3.7 seconds in the adverse weather condition. This is an indication of strong adaptation effects in longitudinal driving behaviour, and the author estimated the values for the IDM model .…”

Section: Literature Review and Related Workmentioning

confidence: 99%

“…Soria et al estimated that, for the Gipps model, the different rain conditions resulted in the same maximum acceleration: 3.3 m/s 2 . However, Hoogendoorn et al observed that the maximum acceleration decreases substantially with rain conditions: from 1.6 to 0.5 m/s 2 , for the IDM model. Zhao et al also observed that the maximum observed acceleration decreases from 4.36 to 2.50 m/s 2 under inclement weather.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

“…However, the literature has just started to investigate the applicability of various car‐following models under varying environmental contexts such as adverse weather conditions. () Driving scenarios (eg, highways, intersections, and roundabouts for dry roads) are usually studied separately, and as a result, emerging control systems do not consider contextual changes of scenarios, while real‐world driving involves mixing different scenarios on the same route.…”

Section: Introductionmentioning

confidence: 99%

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Context‐dependent reconfiguration of autonomous vehicles in mixed traffic

Horcas

Monteil

Bouroche

et al. 2017

J Software Evolu Process

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Human drivers naturally adapt their behaviour depending on the traffic conditions, such as the current weather and road type. Autonomous vehicles need to do the same, in a way that is both safe and efficient in traffic composed of both conventional and autonomous vehicles. In this paper, we demonstrate the applicability of a reconfigurable vehicle controller agent for autonomous vehicles that adapts the parameters of a used car‐following model at runtime, so as to maintain a high degree of traffic quality (efficiency and safety) under different weather conditions. We follow a dynamic software product line approach to model the variability of the car‐following model parameters, context changes and traffic quality, and generate specific configurations for each particular context. Under realistic conditions, autonomous vehicles have only a very local knowledge of other vehicles' variables. We investigate a distributed model predictive controller agent for autonomous vehicles to estimate their behavioural parameters at runtime, based on their available knowledge of the system. We show that autonomous vehicles with the proposed reconfigurable controller agent lead to behaviour similar to that achieved by human drivers, depending on the context.

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Addressing hazardous weather conditions on Middle East highways with smart infrastructure and connected vehicles using agent-based simulation

Outay,

Galland,

Abbas-Turki

et al. 2023

Pers Ubiquit Comput

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The lack of visibility due to foggy conditions is known to cause of a lot of accidents every year in the United Arab Emirates, eventually leading to fatal injuries. Yet, today's technology can help to overcome these visibility issues by providing dynamic information to the driver about the current weather and an appropriate speed limit. This paper explores four strategies, ranging from static road signs to advanced inter-vehicular communication, to better warn the drivers and make them adapt their speed depending on the weather. To evaluate the impact of each policy, agent-based simulations are designed and performed. The results show that a dynamic communication about the weather conditions, supported by either an infrastructure-to-vehicle or a vehicle-to-vehicle protocol, can reduce the probability of occurrence of accidents.

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Longitudinal driving behavior under adverse weather conditions: adaptation effects, model performance and freeway capacity in case of fog

Cited by 33 publications

References 11 publications

Multi-objective traffic light control system based on Bayesian probability interpretation

Multi-objective traffic light control system based on Bayesian probability interpretation

Context‐dependent reconfiguration of autonomous vehicles in mixed traffic

Addressing hazardous weather conditions on Middle East highways with smart infrastructure and connected vehicles using agent-based simulation

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