scholarly journals Effectiveness of Flashing Brake and Hazard Systems in Avoiding Rear-End Crashes

2014 ◽  
Vol 6 ◽  
pp. 792670 ◽  
Author(s):  
Guofa Li ◽  
Wenjun Wang ◽  
Shengbo Eben Li ◽  
Bo Cheng ◽  
Paul Green
Keyword(s):  
Driving Simulator ◽  
Response Times ◽  
Brake System ◽  
Warning Systems ◽  
Time Gap ◽  
Vehicle Deceleration ◽  
Lead Vehicle

Three experiments were conducted to examine the effectiveness of two forward crash warning systems, a flashing brake system and a flashing hazard system, using an advanced driving simulator. In Experiment 1, 20 subjects followed a lead vehicle with a desired time gap and braked when necessary. Results showed that time gap, velocity, and deceleration of the lead vehicle all significantly affected drivers’ brake response times. In Experiment 2, six brake response times to a sudden lead vehicle deceleration (0.6 g at 80 km/h) were measured for six time gaps. Results showed that flashing brake system and flashing hazard system reduced drivers' brake response times by 0.14~0.62 s and 0.03~0.95 s, respectively, in the various situations tested. The effects of flashing color and illuminated size on drivers' brake response times were examined in Experiment 3. Results showed that flashing amber lamps reduced drivers' brake response times significantly by 0.11 s (10%) on average compared with red lamps. These findings demonstrate the effectiveness of both flashing systems in reducing drivers' brake response times in urgent situations and may warrant further consideration by manufacturers.

scholarly journals The Role of Situation Criticality in Affecting the Effect of Cognitive Load on Drivers’ Brake responses: A Driving Simulator Based Study

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Qingwan Xue ◽  
Xuedong Yan ◽  
Yi Zhao ◽  
Yuting Zhang
Keyword(s):  
Cognitive Load ◽  
Driving Simulator ◽  
Response Times ◽  
Explicit Simulation ◽  
Study Participants ◽  
The Relationship ◽  
Accumulator Models ◽  
Different Levels ◽  
Lead Vehicle

A dramatic increase in talking on the phone whilst driving has been seen over the past decades, which posed a significant safety threat on the whole society consequently. Studies on the topic regarding the effect of phone conversations on drivers’ driving performances have never come to a cease, especially on the studies of drivers’ brake response times. However, few studies focus on the relationship between situation criticality and the effect of cognitive load on drivers’ brake responses. To better understand it, a driving simulator experiment with two braking scenarios corresponding to two levels of situation criticality was conducted in this study. Participants were asked to follow a lead vehicle as they normally did and answer arithmetic problems (simple and complex) in three phone modes (baseline, hands-free, and handheld) in the meantime. Drivers’ brake response times to the lead vehicle under five conditions were collected and fitted in accumulator models, in which visual looming and brake lights onset were included as the sensory cues. Results demonstrated that the previously proposed mechanistically explicit simulation model was able to predict drivers’ brake response times on different levels of cognitive load and the increased effect of cognitive load on drivers’ brake response times in less critical situations was demonstrated in this paper as well.

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

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.

The Effects of Cognitive and Visual Workload on Peripheral Detection in the Detection Response Task

2018 ◽  
Vol 60 (6) ◽  
pp. 855-869 ◽  
Author(s):  
Wim van Winsum
Keyword(s):  
Cognitive Load ◽  
Driving Simulator ◽  
Response Times ◽  
Cognitive Task ◽  
Theory Development ◽  
Reaction Times ◽  
Task Load ◽  
Detection Response ◽  
Response Task ◽  
Stimulus Eccentricity

Objective: The independent effects of cognitive and visual load on visual Detection Response Task (vDRT) reaction times were studied in a driving simulator by performing a backwards counting task and a simple driving task that required continuous focused visual attention to the forward view of the road. The study aimed to unravel the attentional processes underlying the Detection Response Task effects. Background: The claim of previous studies that performance degradation on the vDRT is due to a general interference instead of visual tunneling was challenged in this experiment. Method: vDRT stimulus eccentricity and stimulus conspicuity were applied as within-subject factors. Results: Increased cognitive load and visual load both resulted in increased response times (RTs) on the vDRT. Cognitive load increased RT but revealed no task by stimulus eccentricity interaction. However, effects of visual load on RT showed a strong task by stimulus eccentricity interaction under conditions of low stimulus conspicuity. Also, more experienced drivers performed better on the vDRT while driving. Conclusion: This was seen as evidence for a differential effect of cognitive and visual workload. The results supported the tunnel vision model for visual workload, where the sensitivity of the peripheral visual field reduced as a function of visual load. However, the results supported the general interference model for cognitive workload. Application: This has implications for the diagnosticity of the vDRT: The pattern of results differentiated between visual task load and cognitive task load. It also has implications for theory development and workload measurement for different types of tasks.

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

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.

scholarly journals Estimating and Comparing Response Times in Traditional and Connected Environments

2019 ◽  
Vol 2673 (4) ◽  
pp. 674-684 ◽  
Author(s):  
Anshuman Sharma ◽  
Zuduo Zheng ◽  
Jiwon Kim ◽  
Ashish Bhaskar ◽  
Md. Mazharul Haque
Keyword(s):  
Decision Making ◽  
Traffic Safety ◽  
Driving Simulator ◽  
Response Times ◽  
Flow Characteristics ◽  
Trajectory Data ◽  
Stimulus Response ◽  
Wide Range ◽  
The Mean ◽  
The Impact

Response time (RT) is a critical human factor that influences traffic flow characteristics and traffic safety, and is governed by drivers’ decision-making behavior. Unlike the traditional environment (TE), the connected environment (CE) provides information assistance to drivers. This in-vehicle informed environment can influence drivers’ decision-making and thereby their RTs. Therefore, to ascertain the impact of CE on RT, this study develops RT estimation methodologies for TE (RTEM-TE) and CE (RTEM-CE), using vehicle trajectory data. Because of the intra-lingual inconsistency among traffic engineers, modelers, and psychologists in the usage of the term RT, this study also provides a ubiquitous definition of RT that can be used in a wide range of applications. Both RTEM-TE and RTEM-CE are built on the fundamental stimulus–response relationship, and they utilize the wavelet-based energy distribution of time series of speeds to detect the stimulus–response points. These methodologies are rigorously examined for their efficiency and accuracy using noise-free and noisy synthetic data, and driving simulator data. Analysis results demonstrate the excellent performance of both the methodologies. Moreover, the analysis shows that the mean RT in CE is longer than the mean RT in TE.

Braking Response Times for 100 Drivers in the Avoidance of an Unexpected Obstacle as Measured in a Driving Simulator

1996 ◽  
Vol 40 (18) ◽  
pp. 900-904 ◽  
Author(s):  
Nancy L. Broen ◽  
Dean P. Chiang
Keyword(s):  
Standard Deviation ◽  
Response Time ◽  
Driving Simulator ◽  
Response Times ◽  
Video Camera ◽  
Video Data ◽  
Location Information ◽  
Accelerator Pedal ◽  
Shoe Size ◽  
Dynamic Research

This study examined the effect of brake and accelerator pedal configuration on braking response time to an unexpected obstacle. One hundred subjects drove in the Dynamic Research, Inc, (DRI) Interactive Driving Simulator through a simulated neighborhood 21 times, each time with a different pedal configuration. Each subject was presented with an unexpected obstacle only one time, for one of three previously selected pedal configurations, to which he or she was instructed to brake as quickly as possible. Foot movements were recorded with a video camera mounted above the pedals. Data were analyzed manually, using time and course location information superimposed on the video data. Response times were analyzed using ANOVA to determine effects of pedal configuration and various driver factors. Response times ranged from 0.81 sec to 2.44 sec with a mean of 1.33 sec and a standard deviation of 0.27 sec. There was no significant effect of pedal configuration on response time. Driver age was significant, with increased age corresponding to increased response time. Car normally driven, gender, driver height, and shoe size had no significant effect.

scholarly journals Predicting Driver Behavior Using Field Experiment Data and Driving Simulator Experiment Data: Assessing Impact of Elimination of Stop Regulation at Railway Crossings

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Toshihisa Sato ◽  
Motoyuki Akamatsu ◽  
Toru Shibata ◽  
Shingo Matsumoto ◽  
Naoki Hatakeyama ◽  
...  
Keyword(s):  
Field Experiments ◽  
Driving Simulator ◽  
Driver Behavior ◽  
Assessment Method ◽  
Driving Behavior ◽  
Experiment Data ◽  
Driving Speed ◽  
Railway Crossing ◽  
The Impact ◽  
Lead Vehicle

We investigated the impact of deregulating the presence of stop signs at railway crossings on car driver behavior. We estimated the probability that a driver would stop inside the crossing, thereby obstructing the tracks, when a lead vehicle suddenly stopped after the crossing and a stop regulation was eliminated. We proposed a new assessment method of the driving behavior as follows: first, collecting driving behavior data in a driving simulator and in a real road environment; then, predicting the probability based on the collected data. In the simulator experiment, we measured the distances between a lead vehicle and the driver’s vehicle and the driver’s response time to the deceleration of the leading vehicle when entering the railway crossing. We investigated the influence of the presence of two leading vehicles on the driver’s vehicle movements. The deceleration data were recorded in the field experiments. Slower driving speed led to a higher probability of stopping inside the railway crossing. The probability was higher when the vehicle in front of the leading vehicle did not slow down than when both the lead vehicle and the vehicle in front of it slowed down. Finally, advantages of our new assessment method were discussed.

Observations of Driver Time Gap Acceptance at Intersections in Left-Turn Across-Path–Opposite-Direction Scenarios

2005 ◽  
Vol 1910 (1) ◽  
pp. 10-19 ◽  
Author(s):  
Ching-Yao Chan ◽  
David R. Ragland ◽  
Steven E. Shladover ◽  
James A. Misener ◽  
David Marco
Keyword(s):  
Opposite Direction ◽  
Data Sets ◽  
Warning Systems ◽  
Gap Acceptance ◽  
Collision Warning ◽  
Left Turn ◽  
Time Gap ◽  
Warning Criteria ◽  
Collision Warning Systems ◽  
Selection Of

Intersection collision warning systems can potentially reduce the number of collisions and associated losses. A critical design aspect of these systems is the selection of warning criteria, which represent a set of conditions and parameters under which the decision and the timing to issue warnings are determined. Proper warning criteria allow the generation of timely signals for drivers while minimizing false and nuisance alarms. The paper describes the development of a methodology to observe and analyze the selection of time gaps exhibited by driver behaviors in a real-world setting. The data collection procedures and analysis techniques are explained for left-turn across-path–opposite-direction scenarios, which constitute more than a quarter of crossing path crashes at intersections. Exemplar data sets from an urban, signalized intersection are used to illustrate methods of deriving time gap acceptance behaviors. The extracted information can serve as the basis for selecting gap acceptance thresholds in warning criteria, and the demonstrated methodology can be applied in the development of intersection collision warning systems.

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