An Approach for Handling Uncertainties Related to Behaviour and Vehicle Mixes in Traffic Simulation Experiments with Automated Vehicles

Olstam, Johan; Johansson, Fredrik; Alessandrini, Adriano; Sukennik, Peter; Lohmiller, Jochen; Friedrich, Markus

doi:10.1155/2020/8850591

Cited by 16 publications

(10 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The downside is that results obtained from simulations are only as good as the mathematical models involved and as the assumptions made behind them. Strategies to deal with the attached uncertainties when modeling automated driving in traffic simulations have been proposed in Mintsis [26] and in Olstam et al [27]. The proposed strategies are based on the limited available empirical data and/or on recommendations from OEMs.…”

Section: How Has Previous Work Considered the Aspectsmentioning

confidence: 99%

Modeling Automated Driving in Microscopic Traffic Simulations for Traffic Performance Evaluations: Aspects to Consider and State of the Practice

Farah

Postigo²,

Reddy

et al. 2023

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle's sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations.

show abstract

Section: How Has Previous Work Considered the Aspectsmentioning

confidence: 99%

Modeling Automated Driving in Microscopic Traffic Simulations for Traffic Performance Evaluations: Aspects to Consider and State of the Practice

Farah

Postigo²,

Reddy

et al. 2023

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The four levels of traffic operations are based on Elefteriadou [41] variants were originally developed to represent human driving behaviour. Vissim has been recently updated to better cater for automated vehicles by allowing to remove stochastic distributions in some input parameters [53], however not all studies used this new feature. Two studies conducted simulations with several models.…”

Section: Figurementioning

confidence: 99%

“…Most simulation studies consider rather homogeneous behaviour of the ACC or automated vehicles, with one type of controller implemented for each vehicle type. Considering the difference in liability between driver assistance systems (SAE L1-2) where drivers are responsible for safe completion of the dynamic driving task and automated driving (SAE L3-5) where the manufacturer is responsible, it is indeed not likely that drivers can choose the desired time gaps in the same way as they can in current ACC systems [53]. Different car manufacturers may however implement different settings, as shown in field studies with commercial ACC systems of several vehicle manufacturers [75,76].…”

Section: Model Limitationsmentioning

confidence: 99%

Evidence on impacts of automated vehicles on traffic flow efficiency and emissions: Systematic review

Aittoniemi

2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

Despite high expectations of driving automation improving road traffic, its practical implications on traffic flow and emissions are not yet definite. This study systematically reviewed literature on practical impacts of non‐connected automation of passenger cars on motorway traffic efficiency. A conceptual framework showed the importance of understanding interactions between vehicles, both human‐driven and automated, but they are not yet sufficiently known and reproduced by traffic models. Field studies have focused on equipped vehicles. Simulation studies have used different models and assumptions, narrow fleet compositions and road layouts, and covered the theoretical potential in ideal conditions rather than likely impacts in practice. Simulations with automated vehicle time gaps below 1.2 s have found throughput increases, but recent field experiments and simulations using commercial ACC vehicles indicate decreased traffic flow efficiency with increasing traffic volumes and penetration rates. Concluding implications for real traffic from available data is challenging. While benefits are possible for equipped vehicles in low traffic, results suggest negative implications for throughput and emissions at higher traffic volumes. Importantly, more differentiated discussion on the impacts of automated vehicles on traffic flow is needed, considering also the practical implications, such as tradeoffs with safety goals, if benefits are to be achieved.

show abstract

“…The development of CAV is thriving in both academia and industry, which is expected to improve traffic. However, the deployment must experience a gradual mixed transition from introductory, established, to prevalent [22] with CAV penetration rate varying. Existing work presents that CAV mixing in traffic still brings uncertainty.…”

Section: Efficiency and Safety For Mixed-autonomy Trafficmentioning

confidence: 99%

CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning

Guo¹,

Cheng²,

Wang³

2022

Preprint

View full text Add to dashboard Cite

The target of reducing travel time only is insufficient to support the development of future smart transportation systems. To align with the United Nations Sustainable Development Goals (UN-SDG), a further reduction of fuel and emissions, improvements of traffic safety, and the ease of infrastructure deployment and maintenance should also be considered. Different from existing work focusing on the optimization of the control in either traffic light signal (to improve the intersection throughput), or vehicle speed (to stabilize the traffic), this paper presents a multi-agent deep reinforcement learning (DRL) system called CoTV, which Cooperatively controls both Traffic light signals and connected autonomous Vehicles (CAV). Therefore, our CoTV can well balance the achievement of the reduction of travel time, fuel, and emissions. In the meantime, CoTV can also be easy to deploy by cooperating with only one CAV that is the nearest to the traffic light controller on each incoming road. This enables more efficient coordination between traffic light controllers and CAV, thus leading to the convergence of training CoTV under the large-scale multi-agent scenario that is traditionally difficult to converge. We give the detailed system design of CoTV and demonstrate its effectiveness in a simulation study using SUMO under various grid maps and realistic urban scenarios with mixed-autonomy traffic.

show abstract

An Approach for Handling Uncertainties Related to Behaviour and Vehicle Mixes in Traffic Simulation Experiments with Automated Vehicles

Cited by 16 publications

References 33 publications

Modeling Automated Driving in Microscopic Traffic Simulations for Traffic Performance Evaluations: Aspects to Consider and State of the Practice

Modeling Automated Driving in Microscopic Traffic Simulations for Traffic Performance Evaluations: Aspects to Consider and State of the Practice

Evidence on impacts of automated vehicles on traffic flow efficiency and emissions: Systematic review

CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning

Contact Info

Product

Resources

About