Change in Microscopic Traffic Simulation Practice with Respect to the Emerging Automated Driving Technology

Fang, Xuan; Tettamanti, Tamás

doi:10.3311/ppci.17411

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…At this moment, h in Equation ( 4) is adjusted according to the lateral displacement between the current vehicle position and the initial point. Therefore, the lane change abortion path is generated by Equations ( 4)- (6). The HAV drives back to its original lane following the abortion path.…”

Section: Lateral Controlmentioning

confidence: 99%

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Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario

Fang

Tettamanti

et al. 2022

Energies

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There is consensus in industry and academia that Highly Automated Vehicles (HAV) and Connected Automated Vehicles (CAV) will be launched into the market in the near future due to emerging autonomous driving technology. In this paper, a mixed traffic simulation framework that integrates vehicle models with different automated driving systems in the microscopic traffic simulation was proposed. Currently, some of the more mature Automated Driving Systems (ADS) functions (e.g., Adaptive Cruise Control (ACC), Lane Keeping Assistant (LKA), etc.) are already equipped in vehicles, the very next step towards a higher automated driving is represented by Level 3 vehicles and CAV which show great promise in helping to avoid crashes, ease traffic congestion, and improve the environment. Therefore, to better predict and simulate the driving behavior of automated vehicles on the motorway scenario, a virtual test framework is proposed which includes the Highway Chauffeur (HWC) and Vehicle-to-Vehicle (V2V) communication function. These functions are implemented as an external driver model in PTV Vissim. The framework uses a detailed digital twin based on the M86 road network located in southwestern Hungary, which was constructed for autonomous driving tests. With this framework, the effect of the proposed vehicle models is evaluated with the microscopic traffic simulator PTV Vissim. A case study of the different penetration rates of HAV and CAV was performed on the M86 motorway. Preliminary results presented in this paper demonstrated that introducing HAV and CAV to the current network individually will cause negative effects on traffic performance. However, a certain ratio of mixed traffic, 60% CAV and 40% Human Driver Vehicles (HDV), could reduce this negative impact. The simulation results also show that high penetration CAV has fine driving stability and less travel delay.

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Section: Lateral Controlmentioning

confidence: 99%

“…However, the current Traffic Analysis, Modeling, and Simulation (TAMS) tools are not adequate for evaluating CAV or HAV driving behavior. Changes of the driving behavior parameter even had the opposite effect in different microscopic traffic simulation tools [6]. The reasons for this are as follows.…”

Section: Introductionmentioning

confidence: 99%

Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario

Fang

Tettamanti

et al. 2022

Energies

Self Cite

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“…Many infrastructure projects are accompanied by traffic simulation programs, such as Vissim or Visum, belonging to the PTV group and the popular SUMO application [34,35]. These tools may include built-in modules to estimate exhaust emissions or can be used with external modules, such as Enviver for Vissim [36].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Emissions in Vehicles Equipped with Energy-Saving Start-Stop Technology: THC and NOx Modeling Insights

Mądziel

2024

Preprint

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Creating accurate emission models capable of capturing the variability and dynamics of modern propulsion systems is crucial for future mobility planning. This paper presents a methodology for creating THC and NOx emission models for vehicles equipped with start-stop technology. A key aspect of this endeavor is to find techniques that accurately replicate the engine stop stages when there are no emissions. To this end, several machine learning techniques were tested using the Python programming language. Random forest and gradient boosting methods demonstrated the best predictive capabilities for THC and NOx emissions, achieving R2 scores of approximately 0.9 for both cold and hot engine emissions. Additionally, recommendations for effective modeling of such emissions from vehicles are presented in the paper.

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Section: Introductionmentioning

confidence: 99%

Quantifying Emissions in Vehicles Equipped with Energy-Saving Start–Stop Technology: THC and NOx Modeling Insights

Mądziel

2024

Energies

View full text Add to dashboard Cite

Creating accurate emission models capable of capturing the variability and dynamics of modern propulsion systems is crucial for future mobility planning. This paper presents a methodology for creating THC and NOx emission models for vehicles equipped with start–stop technology. A key aspect of this endeavor is to find techniques that accurately replicate the engine’s stop stages when there are no emissions. To this end, several machine learning techniques were tested using the Python programming language. Random forest and gradient boosting methods demonstrated the best predictive capabilities for THC and NOx emissions, achieving R2 scores of approximately 0.9 for engine emissions. Additionally, recommendations for effective modeling of such emissions from vehicles are presented in the paper.

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Change in Microscopic Traffic Simulation Practice with Respect to the Emerging Automated Driving Technology

Cited by 6 publications

References 19 publications

Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario

Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario

Quantifying Emissions in Vehicles Equipped with Energy-Saving Start-Stop Technology: THC and NOx Modeling Insights

Quantifying Emissions in Vehicles Equipped with Energy-Saving Start–Stop Technology: THC and NOx Modeling Insights

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