Comparing the Effect of Age, Gender, and Desired Speed on Car-Following Behavior by Using Driving Simulator

In this study, the effect of age, gender, and desired speed (DS) factors on General Motors car-following (CF) behavior was investigated. DS was defined as the speed selected by the driver in free driving situation. A low-level driving simulator was used to collect data. The CF model for each driver was calibrated by genetic algorithm. Gender and DS were effective in CF behavior, while the age factor was not. The drivers’ sensitivity to the variables of speed and distance in the CF model increased with increasing the DS. The gender factor affected only the magnitude of deceleration which was higher in women. For further investigation, the effect of the desired speed on the time headway in the steady-state CF was also examined. DS factor was effective in steady-state CF behavior. As the DS increased, the time headway decreased. Examining CF threshold demonstrated that women maintained larger distance than men. Finally, it can be said that DS and gender would be more important than age to be considered in CF models.

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Do drivers change their manual car-following behaviour after automated car-following?

10.31234/osf.io/zn3td ◽

2020 ◽

Author(s):

Tyron Louw ◽

Rafael Goncalves ◽

Guilhermina Torrao ◽

Vishnu Radhakrishnan ◽

Wei Lyu ◽

...

Keyword(s):

Driving Simulator ◽

Cruise Control ◽

Automated Driving ◽

Behavioural Adaptation ◽

Car Following ◽

Time Headway ◽

Driving Behaviour ◽

Driving Assistance ◽

Change In Mean ◽

Lead Vehicle

There is evidence that drivers’ behaviour adapts after using different advanced driving assistance systems. For instance, drivers’ headway during car-following reduces after using adaptive cruise control. However, little is known about whether, and how, drivers’ behaviour will change if they experience automated car-following, and how this is affected by engagement in non-driving related tasks (NDRT). The aim of this driving simulator study, conducted as part of the H2020 L3Pilot project, was to address this topic. We also investigated the effect of the presence of a lead vehicle during the resumption of control, on subsequent manual driving behaviour. Thirty-two participants were divided into two experimental groups. During automated car-following, one group was engaged in an NDRT (SAE Level 3), while the other group was free to look around the road environment (SAE Level 2). Both groups were exposed to Long (1.5 s) and Short (.5 s) Time Headway (THW) conditions during automated car-following, and resumed control both with and without a lead vehicle. All post-automation manual drives were compared to a Baseline Manual Drive, which was recorded at the start of the experiment. Drivers in both groups significantly reduced their time headway in all post-automation drives, compared to a Baseline Manual Drive. There was a greater reduction in THW after drivers resumed control in the presence of a lead vehicle, and also after they had experienced a shorter THW during automated car following. However, whether drivers were in L2 or L3 did not appear to influence the change in mean THW. Subjective feedback suggests that drivers appeared not to be aware of the changes to their driving behaviour, but preferred longer THWs in automation. Our results suggest that automated driving systems should adopt longer THWs in car-following situations, since drivers’ behavioural adaptation may lead to adoption of unsafe headways after resumption of control.

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Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

10.31234/osf.io/bwp6f ◽

2021 ◽

Author(s):

Vishnu Radhakrishnan ◽

Natasha Merat ◽

Tyron Louw ◽

Rafael Goncalves ◽

Wei Lyu ◽

...

Keyword(s):

Driving Simulator ◽

Electrodermal Activity ◽

Automated Driving ◽

Physiological Indicators ◽

Car Following ◽

Time Headway ◽

Driver Workload ◽

Driving Task ◽

Vehicle Automation ◽

Lead Vehicle

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle.

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Time Headway in Car following and Operational Performance during Unexpected Braking

Perceptual and Motor Skills ◽

10.2466/pms.1997.84.3c.1247 ◽

1997 ◽

Vol 84 (3_suppl) ◽

pp. 1247-1257 ◽

Cited By ~ 40

Author(s):

Wim Van Winsum ◽

Wiebo Brouwer

Keyword(s):

Driving Simulator ◽

Theoretical Perspective ◽

Open Loop ◽

Braking Performance ◽

Individual Level ◽

Car Following ◽

Time Headway ◽

Motor Component ◽

Visual Motor ◽

The Individual

The relation between car-following behaviour and braking performance was studied in a driving simulator. The theoretical perspective was that individual differences in tactical car-driving behaviour may be related to skills on the operational level of the driving task via a process of adaptation. In a sample of 16 young and middle-aged experienced drivers independent assessments were made of preferred time headway during car following and of braking skill. Starting from modern theories of visual-motor learning, braking performance was analyzed in terms of a reaction time component, an open-loop visual-motor component, and a closed-loop visual-motor component involving the precise adjustment of braking (timing and force) to the situation. The efficiency of the visual-motor component of braking was a strong and significant predictor of choice of time headway to the lead vehicle in such a way that less efficient braking indicated a preference for a longer time headway. This result supports the theory of adaptation on the individual level.

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Mobile Phone Use in a Car-Following Situation: Impact on Time Headway and Effectiveness of Driver’s Rear-End Risk Compensation Behavior via a Driving Simulator Study

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17041328 ◽

2020 ◽

Vol 17 (4) ◽

pp. 1328 ◽

Cited By ~ 2

Author(s):

Yunxing Chen ◽

Rui Fu ◽

Qingjin Xu ◽

Wei Yuan

Keyword(s):

Regression Model ◽

Mobile Phone ◽

Driving Simulator ◽

Accident Risk ◽

Mobile Phone Use ◽

Car Following ◽

Time Headway ◽

Accident Probability ◽

Compensation Behavior ◽

The Relationship

Mobile phone use while driving has become one of the leading causes of traffic accidents and poses a significant threat to public health. This study investigated the impact of speech-based texting and handheld texting (two difficulty levels in each task) on car-following performance in terms of time headway and collision avoidance capability; and further examined the relationship between time headway increase strategy and the corresponding accident frequency. Fifty-three participants completed the car-following experiment in a driving simulator. A Generalized Estimating Equation method was applied to develop the linear regression model for time headway and the binary logistic regression model for accident probability. The results of the model for time headway indicated that drivers adopted compensation behavior to offset the increased workload by increasing their time headway by 0.41 and 0.59 s while conducting speech-based texting and handheld texting, respectively. The model results for the rear-end accident probability showed that the accident probability increased by 2.34 and 3.56 times, respectively, during the use of speech-based texting and handheld texting tasks. Additionally, the greater the deceleration of the lead vehicle, the higher the probability of a rear-end accident. Further, the relationship between time headway increase patterns and the corresponding accident frequencies showed that all drivers’ compensation behaviors were different, and only a few drivers increased their time headway by 60% or more, which could completely offset the increased accident risk associated with mobile phone distraction. The findings provide a theoretical reference for the formulation of traffic regulations related to mobile phone use, driver safety education programs, and road safety public awareness campaigns. Moreover, the developed accident risk models may contribute to the development of a driving safety warning system.

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Desired Time Gap and Time Headway in Steady-State Car-Following on Two-Lane Roads

Journal of Transportation Engineering ◽

10.1061/(asce)0733-947x(2009)135:10(687) ◽

2009 ◽

Vol 135 (10) ◽

pp. 687-693 ◽

Cited By ~ 20

Author(s):

Partha Pratim Dey ◽

Satish Chandra

Keyword(s):

Steady State ◽

Car Following ◽

Time Headway ◽

Time Gap

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Do drivers change their manual car-following behaviour after automated car-following?

Cognition Technology & Work ◽

10.1007/s10111-020-00658-5 ◽

2020 ◽

Author(s):

Tyron Louw ◽

Rafael Goncalves ◽

Guilhermina Torrao ◽

Vishnu Radhakrishnan ◽

Wei Lyu ◽

...

Keyword(s):

Driving Simulator ◽

Cruise Control ◽

Automated Driving ◽

Behavioural Adaptation ◽

Car Following ◽

Time Headway ◽

Driving Behaviour ◽

Driving Assistance ◽

Change In Mean ◽

Lead Vehicle

AbstractThere is evidence that drivers’ behaviour adapts after using different advanced driving assistance systems. For instance, drivers’ headway during car-following reduces after using adaptive cruise control. However, little is known about whether, and how, drivers’ behaviour will change if they experience automated car-following, and how this is affected by engagement in non-driving-related tasks (NDRT). The aim of this driving simulator study, conducted as part of the H2020 L3Pilot project, was to address this topic. We also investigated the effect of the presence of a lead vehicle during the resumption of control, on subsequent manual driving behaviour. Thirty-two participants were divided into two experimental groups. During automated car-following, one group was engaged in an NDRT (SAE Level 3), while the other group was free to look around the road environment (SAE Level 2). Both groups were exposed to Long (1.5 s) and Short (.5 s) Time Headway (THW) conditions during automated car-following, and resumed control both with and without a lead vehicle. All post-automation manual drives were compared to a Baseline Manual Drive, which was recorded at the start of the experiment. Drivers in both groups significantly reduced their time headway in all post-automation drives, compared to a Baseline Manual Drive. There was a greater reduction in THW after drivers resumed control in the presence of a lead vehicle, and also after they had experienced a shorter THW during automated car-following. However, whether drivers were in L2 or L3 did not appear to influence the change in mean THW. Subjective feedback suggests that drivers appeared not to be aware of the changes to their driving behaviour, but preferred longer THWs in automation. Our results suggest that automated driving systems should adopt longer THWs in car-following situations, since drivers’ behavioural adaptation may lead to adoption of unsafe headways after resumption of control.

Download Full-text