"uuid","repository link","title","author","contributor","publication year","abstract","subject topic","language","publication type","publisher","isbn","issn","patent","patent status","bibliographic note","access restriction","embargo date","faculty","department","research group","programme","project","coordinates"
"uuid:96a3f3dd-6ec3-4b89-a232-539ca885e3d3","http://resolver.tudelft.nl/uuid:96a3f3dd-6ec3-4b89-a232-539ca885e3d3","Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study","Varotto, S.F. (TU Delft Transport and Planning); Farah, H. (TU Delft Transport and Planning); Bogenberger, Klaus (University of the Federal Armed Forces Munich); van Arem, B. (TU Delft Transport and Planning); Hoogendoorn, S.P. (TU Delft Transport and Planning)","","2020","Adaptive Cruise Control (ACC) can reduce traffic congestion and accidents. In dense traffic flow conditions and when changing lanes, drivers prefer to deactivate the ACC. These control transitions between automation and manual driving could impact driver behaviour characteristics. However, few studies have analysed the magnitude and duration of these adaptations. This research aims at quantifying the adaptations in speed, acceleration, distance headway and relative speed when drivers resume manual control. We collected driver behaviour data in an on-road experiment with full-range ACC during peak hours in Munich. We analysed these data using linear mixed-effects models to identify statistically significant changes in driver behaviour characteristics after drivers resumed manual control (transition period). The results reveal that the speed decreased significantly after the system was deactivated and it increased significantly after the system was overruled by pressing the gas pedal. These adaptations might have a substantial impact on traffic efficiency and safety.","Adaptive Cruise Control; Control transitions; driver behaviour; linear mixed-effects models; on-road experiment; transition period","en","journal article","","","","","","Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.","","2020-08-11","","Transport and Planning","Transport and Planning","","",""
"uuid:6f18f1f8-b512-4c95-bcdd-3a0988c7d98e","http://resolver.tudelft.nl/uuid:6f18f1f8-b512-4c95-bcdd-3a0988c7d98e","Continuous-discrete choices of control transitions and speed regulations in full-range adaptive cruise control","Varotto, S.F. (TU Delft Transport and Planning); Farah, H. (TU Delft Transport and Planning); Toledo, Tomer (Technion); van Arem, B. (TU Delft Transport and Planning); Hoogendoorn, S.P. (TU Delft Transport and Planning)","","2018","Driving assistance systems such as Adaptive Cruise Control (ACC) and automated vehicles can contribute to mitigate traffic congestion, accidents, and levels of emissions. Automated vehicles may increase roadway capacity, improve traffic flow stability, and speed up the outflow from a queue (1). The functionalities of automated systems have been gradually introduced into the market, such as in the case of Adaptive Cruise Control (ACC). The ACC assists drivers in maintaining a desired speed and time headway, therefore influencing substantially the performance of the driving task. On-road studies have shown potential safety benefits of ACC systems that are inactive at low speeds when they are activated (2-5). In certain traffic situations, drivers may prefer to disengage ACC and resume manual control (6). These transitions between automation and manual driving are called control transitions (7) and may influence considerably traffic flow efficiency (8) and safety (9). Recently, full-range ACC systems that can operate in dense traffic have been introduced into the market. These ACC systems are more likely to be active in dense traffic conditions and have a positive impact on traffic flow efficiency","","en","conference paper","Transportation Research Board (TRB)","","","","","Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.","","2018-07-11","","Transport and Planning","Transport and Planning","","",""
"uuid:9a7c26ce-277f-48c0-bbb7-14dc12e801fb","http://resolver.tudelft.nl/uuid:9a7c26ce-277f-48c0-bbb7-14dc12e801fb","Modelling decisions of control transitions and target speed regulations in full-range Adaptive Cruise Control based on Risk Allostasis Theory","Varotto, S.F. (TU Delft Transport and Planning); Farah, H. (TU Delft Transport and Planning); Toledo, Tomer (Technion); van Arem, B. (TU Delft Transport and Planning); Hoogendoorn, S.P. (TU Delft Transport and Planning)","","2018","Adaptive Cruise Control (ACC) and automated vehicles can contribute to reduce traffic congestion and accidents. Recently, an on-road study has shown that drivers may prefer to deactivate full-range ACC when closing in on a slower leader and to overrule it by pressing the gas pedal a few seconds after the activation of the system. Notwithstanding the influence of these control transitions on driver behaviour, a theoretical framework explaining driver decisions to transfer control and to regulate the target speed in full-range ACC is currently missing.
This research develops a modelling framework describing the underlying decision-making process of drivers with full-range ACC at an operational level, grounded on Risk Allostasis Theory (RAT). Based on this theory, a driver will choose to resume manual control or to regulate the ACC target speed if its perceived level of risk feeling and task difficulty falls outside the range considered acceptable to maintain the system active. The feeling of risk and task difficulty evaluation is formulated as a generalized ordered probit model with random thresholds, which vary between drivers and within drivers over time. The ACC system state choices are formulated as logit models and the ACC target speed regulations as regression models, in which correlations between system state choices and target speed regulations are captured explicitly. This continuous-discrete choice model framework is able to address interdependencies across drivers’ decisions in terms of causality, unobserved driver characteristics, and state dependency, and to capture inconsistencies in drivers’ decision making that might be caused by human factors.
The model was estimated using a dataset collected in an on-road experiment with full-range ACC. The results reveal that driver decisions to resume manual control and to regulate the target speed in full-range ACC can be interpreted based on the RAT. The model can be used to forecast driver response to a driving assistance system that adapts its settings to prevent control transitions while guaranteeing safety and comfort. The model can also be implemented into a microscopic traffic flow simulation to evaluate the impact of ACC on traffic flow efficiency and safety accounting for control transitions and target speed regulations
This research aims to identify the main factors influencing drivers’ choice to resume manual control. A mixed logit model that predicts the choice to deactivate the system or overrule it by pressing the gas pedal was estimated. The dataset was collected in an on-road experiment in which twenty-three participants drove a research vehicle equipped with full-range ACC on a 35.5-km freeway in Munich during peak hours.
The results reveal that drivers are more likely to deactivate the ACC and resume manual control when approaching a slower leader, when expecting vehicles cutting in, when driving above the ACC target speed, and before exiting the freeway. Drivers are more likely to overrule the ACC system by pressing the gas pedal a few seconds after the system has been activated, and when the vehicle decelerates. Everything else being equal, some drivers have higher probabilities to resume manual control. We conclude that a novel 16 conceptual framework linking ACC system settings, driver behavior characteristics, driver characteristics and environmental factors is needed to model driver behavior in control transitions between ACC and 18 manual driving