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

Abstract

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.

Digital Twins in the Automotive Industry: The Road toward Physical-Digital Convergence

“…The industry retains some of its traditional infrastructure while being constantly enriched with emerging digital services based on sensors and devices utilizing artificial intelligence, which operate mainly after the product enters the market. The involvement and importance of Digital Twins in the improvement of the vehicle aid in its life cycle-added value and also in the optimized design of future vehicles [49,50]. Figure 3 In order for a vehicle to enter the market, it should undergo a number of obligatory design and control stages.…”

“…Innov. 2022, 5, x FOR PEER REVIEW 6 of 12the improvement of the vehicle aid in its life cycle-added value and also in the optimized design of future vehicles[49][50]. Figure3depicts a typical Digital Twin arrangement applied in the automotive industry.…”

Macroscopic Experiments on Coexistence of Autonomous Vehicle Behavior on Various Heterogeneous Traffic Conditions

“…AVs are likely to bring paradigm shift in the future of transportation and in the transformation of urban space. It is expected that the proportion of AVs would gradually increase in the near future, which initially coexist with conventional vehicles (CVs) under mixed traffic condition [1][2][3][4]. Although there are still many unanswered questions regarding the precise effects of AVs on energy and the environment, it is generally acknowledged that at increasing AV penetration, a substantial net decrease in greenhouse gas emissions can be achieved [5][6][7][8].…”

“…AVs with integrated communication systems and network technologies to accomplish intelligent information transfer, exchange, and sharing between vehicles and the environment are referred to as connected and automated vehicles (CAVs). HAVs have the potential to change future mobility patterns since they can complete the entire dynamic driving task (DDT) and include crash mitigation and avoidance capabilities as part of their ADS feature [2,9,10].…”

Advances in Automated Driving Systems

“…Article [1] deals with a methodology to quantify the performance of sensor models in virtual validation and verification (V & V) of automated driving functions, an important step towards reduction of on-road testing. The effect of automation on traffic flow during the transition phase in mixed traffic was investigated by [2]. Article [3] deals with the quality of ground truth annotation data to improve the transfer of on-road testing results into simulation.…”