https://www.tandfonline.com/doi/pdf/10.1080/15472450.2017.1291351

Policy and society related implications of automated driving: A review of literature and directions for future research

Mental workload (MWL) is one of the most widely used concepts in ergonomics and human factors and represents a topic of increasing importance. Since modern technology in many working environments imposes ever more cognitive demands upon operators while physical demands diminish, understanding how MWL impinges on performance is increasingly critical. Yet, MWL is also one of the most nebulous concepts, with numerous definitions and dimensions associated with it. Moreover, MWL research has had a tendency to focus on complex, often safety-critical systems (e.g. transport, process control). Here we provide a general overview of the current state of affairs regarding the understanding, measurement and application of MWL in the design of complex systems over the last three decades. We conclude by discussing contemporary challenges for applied research, such as the interaction between cognitive workload and physical workload, and the quantification of workload ‘redlines’ which specify when operators are approachi...

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State of science: mental workload in ergonomics

This introductory paper reviews the current state-of-the-art scientific methods used for modelling, analysing and controlling the dynamics of vehicular traffic. Possible mechanisms underlying traffic jam formation and propagation are presented from a dynamical viewpoint. Stable and unstable motions are described that may give the skeleton of traffic dynamics, and the effects of driver behaviour are emphasized in determining the emergent state in a vehicular system. At appropriate points, references are provided to the papers published in the corresponding Theme Issue.

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Traffic jams: dynamics and control

Over the past decades there has been a considerable development in the modeling of car-following (CF) behavior as a result of research undertaken by both traffic engineers and traffic psychologists While traffic engineers seek to understand the behavior of a traffic stream, traffic psychologists seek to describe the human abilities and errors involved in the driving process This paper provides a comprehensive review of these two research streams It is necessary to consider human-factors in CF modeling for a more realistic representation of CF behavior in complex driving situations (for example, in traffic breakdowns, crash-prone situations, and adverse weather conditions) to improve traffic safety and to better understand widely-reported puzzling traffic flow phenomena, such as capacity drop, stop-and-go oscillations, and traffic hysteresis While there are some excellent reviews of CF models available in the literature, none of these specifically focuses on the human factors in these models This paper addresses this gap by reviewing the available literature with a specific focus on the latest advances in car-following models from both the engineering and human behavior points of view In so doing, it analyses the benefits and limitations of various models and highlights future research needs in the area

Incorporating human-factors in car-following models: A review of recent developments and research needs

The effects of adaptive task allocation on monitoring for automation failure during multitask flight simulation were examined. Participants monitored an automated engine status task while simultaneously performing tracking and fuel management tasks over three 30-min sessions. Two methods of adaptive task allocation, both involving temporary return of the automated engine status task to the human operator ("human control"), were examined as a possible countermeasure to monitoring inefficiency. For the model-based adaptive group, the engine status task was allocated to all participants in the middle of the second session for 10 min, following which it was again returned to automation control. The same occurred for the performance-based adaptive group, but only if an individual participant's monitoring performance up to that point did not meet a specified criterion. For the nonadaptive control groups, the engine status task remained automated throughout the experiment. All groups had low probabilities of detection of automation failures for the first 40 min spent with automation. However, following the 10-min intervening period of human control, both adaptive groups detected significantly more automation failures during the subsequent blocks under automation control. The results show that adaptive task allocation can enhance monitoring of automated systems. Both model-based and performance-based allocation improved monitoring of automation. Implications for the design of automated systems are discussed.

Effects of adaptive task allocation on monitoring of automated systems

Automation may be assumed to have a beneficial impact on traffic flow efficiency. However, the relationship between automation and traffic flow efficiency is complex because behavior of road users influences this efficiency as well. This paper reviews what is known about the influence of automation on traffic flow efficiency and behavior of road users, formulates a theoretical framework, and identifies future research needs. It is concluded that automation can be assumed to have an influence on traffic flow efficiency and on the behavior of road users. The research has shortcomings, and in this context directions are formulated for future scientific research on automation in relation to traffic flow efficiency and human behavior.

Automated Driving, Traffic Flow Efficiency, and Human Factors

Parameter identification of microscopic driving models is a difficult task. This is caused by the fact that parameters--such as reaction time, sensitivity to stimuli, etc.--are generally not directly observable from common traffic data, but also due to the lack of reliable statistical estimation techniques. This contribution puts forward a new approach to identifying parameters of car-following models. One of the main contributions of this article is that the proposed approach allows for joint estimation of parameters using different data sources, including prior information on parameter values (or the valid range of values). This is achieved by generalizing the maximum-likelihood estimation approach proposed by the authors in previous work. The approach allows for statistical analysis of the parameter estimates, including the standard error of the parameter estimates and the correlation of the estimates. Using the likelihood-ratio test, models of different complexity (defined by the number of model parameters) can be cross-compared. A nice property of this test is that it takes into account the number of parameters of a model as well as the performance. To illustrate the workings, the approach is applied to two car-following models using vehicle trajectories of a Dutch freeway collected from a helicopter, in combination with data collected with a driving simulator.

Calibration of microscopic traffic-flow models using multiple data sources

Microscopic simulation models have become widely applied tools in traffic engineering. Nevertheless, parameter identification of these models remains a difficult task. This is for one caused by the fact that parameters are generally not directly observable from common traffic data, but also due to the lack of reliable statistical estimation techniques. This contribution puts forward a new general and structured approach to identifying parameters of car-following models. One of the main contributions of this contribution is that the proposed approach allows for joint estimation of parameters for multiple vehicles. Furthermore, it allows inclusion of prior information on the parameter values (or the valid range of values) to be estimated. It deals with the serial correlation in the trajectory data. In doing so, the newly developed approach generalizes the Maximum Likelihood estimation approach proposed by the author. The approach allows for statistical analysis of the model estimates, including the standard error of the parameter estimates and the correlation of the estimates. Using the likelihood-ratio test, models of different complexity (defined by the number of model parameters) can be cross-compared. A nice property of this test is that it takes into account the number of parameters of a model as well as the performance. To illustrate the workings, the approach is applied to a car-following using vehicle trajectories for a Dutch freeway collected from a helicopter.

A generic calibration framework for joint estimation of car following models using microscopic data

Microscopic simulation models have become widely applied tools in traffic engineering. Nevertheless, parameter identification of these models remains a difficult task. This is partially because parameters are generally not directly observable from common traffic data; also there is a lack of reliable statistical estimation techniques. This study puts forward a new general and structured approach to identifying parameters of car-following models. One of the main contributions of this study is joint estimation of parameters for multiple vehicles. Furthermore, prior information on the parameter values (or the valid range of values) can be estimated. The study also deals with serial correlation in the trajectory data. In doing so, the newly developed approach generalizes the maximum likelihood estimation approach proposed by the authors. The approach allows for statistical analysis of the model estimates, including the standard error of the parameter estimates and the correlation of the estimates. With the likelihood ratio test, models of different complexity (defined by the number of model parameters) can be cross-compared. A useful property of this test is that it takes into account the number of parameters of a model as well as the performance. The approach is applied to car-following behavior by using Dutch freeway vehicle trajectories collected from a helicopter.

Generic Calibration Framework for Joint Estimation of Car-Following Models by Using Microscopic Data

Adverse weather conditions have been shown to have a substantial impact on traffic flow operations. It is however unclear which adaptation effects in actual longitudinal driving behavior underlie this impact, how these adaptation effects relate to freeway capacity as well as to what extent current mathematical models of car-following behavior are adequate in incorporating these adaptation effects. In this regard a driving simulator experiment with a repeated measures design was performed in order to examine the influence of fog on adaptation effects, freeway capacity and parameter value changes and model performance of the Helly model and Intelligent Driver Model. From the results followed that fog led to a decrease in speed as well as in acceleration. Furthermore a substantial increase in distance to the lead vehicle was observed. These effects were implemented and simulated in a traffic simulation model. A substantial reduction in freeway capacity was found. This stresses the need to possess models of driving behavior, which are adequate in describing and predicting these adaptation effects. From the estimation results of the Helly model and IDM using a calibration approach for joint estimation followed that sensitivity factors, maximum acceleration and deceleration decreased substantially after the start of the adverse weather condition. Parameters representing headway increased significantly. Furthermore it followed from the results that the estimated models decreased in performance after the start of the adverse weather conditions

R.G. Hoogendoorn

Papers

Automated Driving, Traffic Flow Efficiency, and Human Factors

Calibration of microscopic traffic-flow models using multiple data sources

A generic calibration framework for joint estimation of car following models using microscopic data

Generic Calibration Framework for Joint Estimation of Car-Following Models by Using Microscopic Data

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