scholarly journals Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

2020 ◽  
Vol 16 (3) ◽  
pp. 776-806
Author(s):  
Silvia F. Varotto ◽  
Haneen Farah ◽  
Klaus Bogenberger ◽  
Bart van Arem ◽  
Serge P. Hoogendoorn
Ergonomics ◽  
2005 ◽  
Vol 48 (10) ◽  
pp. 1294-1313 ◽  
Author(s):  
Neville A. Stanton ◽  
Mark S. Young

2014 ◽  
Vol 125 ◽  
pp. 57-67 ◽  
Author(s):  
Dongbin Zhao ◽  
Zhaohui Hu ◽  
Zhongpu Xia ◽  
Cesare Alippi ◽  
Yuanheng Zhu ◽  
...  

2018 ◽  
Vol 117 ◽  
pp. 318-341 ◽  
Author(s):  
Silvia F. Varotto ◽  
Haneen Farah ◽  
Tomer Toledo ◽  
Bart van Arem ◽  
Serge P. Hoogendoorn

Author(s):  
Silvia F. Varotto ◽  
Haneen Farah ◽  
Tomer Toledo ◽  
Bart van Arem ◽  
Serge P. Hoogendoorn

Automated vehicles and driving assistance systems such as adaptive cruise control (ACC) are expected to reduce traffic congestion, accidents, and levels of emissions. Field operational tests have found that drivers may prefer to deactivate ACC in dense traffic flow conditions and before changing lanes. Despite the potential effects of these control transitions on traffic flow efficiency and safety, most mathematical models evaluating the impact of ACC do not adequately represent that process. This research aimed to identify the main factors influencing drivers’ choice to resume manual control. A mixed logit model that predicted the choice to deactivate the system or overrule it by pressing the gas pedal was estimated. The data set was collected in an on-road experiment in which 23 participants drove a research vehicle equipped with full-range ACC on a 35.5-km freeway in Munich, Germany, during peak hours. The results reveal that drivers were 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 were more likely to overrule the ACC system by pressing the gas pedal a few seconds after the system had been activated and when the vehicle decelerated. Everything else being equal, some drivers had higher probabilities to resume manual control. This study concludes that a novel 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 manual driving.


Vehicles ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 749-763
Author(s):  
Srikanth Kolachalama ◽  
Hafiz Malik

This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by optimizing the engine operating conditions (EOC) considering vehicle level vectors (VLV) (body parameter, environment, driver behaviour) as the affecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the VLV. We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived at each second. Thus, three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. A performance comparison between predicted and constant ACCSSP’s indicates that the predictive model outperforms constant ACCSSP.


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