scholarly journals Effects of temporal and spatiotemporal cues on detection of dynamic road hazards

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Benjamin Wolfe ◽  
Anna Kosovicheva ◽  
Simon Stent ◽  
Ruth Rosenholtz
Keyword(s):  
Cognitive Psychology ◽  
Reaction Time ◽  
Real World ◽  
Standard Laboratory ◽  
Extra Time ◽  
The Road ◽  
Significant Difference ◽  
Invalid Condition ◽  
On The Road ◽  
First Moment

AbstractWhile driving, dangerous situations can occur quickly, and giving drivers extra time to respond may make the road safer for everyone. Extensive research on attentional cueing in cognitive psychology has shown that targets are detected faster when preceded by a spatially valid cue, and slower when preceded by an invalid cue. However, it is unknown how these standard laboratory-based cueing effects may translate to dynamic, real-world situations like driving, where potential targets (i.e., hazardous events) are inherently more complex and variable. Observers in our study were required to correctly localize hazards in dynamic road scenes across three cue conditions (temporal, spatiotemporal valid and spatiotemporal invalid), and a no-cue baseline. All cues were presented at the first moment the hazardous situation began. Both types of valid cues reduced reaction time (by 58 and 60 ms, respectively, with no significant difference between them, a larger effect than in many classic studies). In addition, observers’ ability to accurately localize hazards dropped 11% in the spatiotemporal invalid condition, a result with dangerous implications on the road. This work demonstrates that, in spite of this added complexity, classic cueing effects persist—and may even be enhanced—for the detection of real-world hazards, and that valid cues have the potential to benefit drivers on the road.

scholarly journals Validation of CO dispersion model due to the road position on the dominant wind direction on transport sector

2018 ◽  
Vol 197 ◽  
pp. 13017 ◽  
Author(s):  
Vera Surtia Bachtiar ◽  
Purnawan ◽  
Reri Afrianita ◽  
Randa Anugerah
Keyword(s):  
Wind Direction ◽  
Dispersion Model ◽  
Transport Sector ◽  
The Road ◽  
Validation Test ◽  
Traffic Characteristics ◽  
Significant Difference ◽  
Co Concentration ◽  
On The Road ◽  
Co Dispersion

This study aims to validate CO dispersion model due to the position of the road toward the dominant wind direction on the transport sector. Sampling for modelling was done on the road with the road angle to wind direction is 0 degree (Jend. A. Yani Road), 30 degree (Andalas Road) and 60 degree (Prof. Dr. Hamka Road). CO dispersion model was obtained from the relations between CO concentration with traffic volume, traffic speed, wind speed and dominant wind direction. Sampling for validation was done at three location points, i.e. Jend. Ahmad Yani Road, By Pass Road and Dr. Wahidin Road, each of which has a position of 0, 45 and 90 degrees toward dominant wind direction. Sampling for CO was done using impinger. Measurement of traffic characteristics and meteorological conditions was performed in conjunction with CO sampling. Validation test was done by using Pearson Product Moment formula and Test of Two Variance. Results of the Two-Variance Test showed no significant difference between two concentrations of CO model and CO measurement. It showed the Test Ratio (RUf) smaller than the Critical Point. Validation test using Pearson Product Moment showed that the CO model can be used for predicting CO dispersion.

Drivers' Brake Reaction Times

1971 ◽  
Vol 13 (1) ◽  
pp. 23-27 ◽  
Author(s):  
Gunnar Johansson ◽  
Kåre Rumar
Keyword(s):  
Reaction Time ◽  
Small Group ◽  
Correction Factor ◽  
Reaction Times ◽  
Large Group ◽  
The Road ◽  
On The Road

The object of this investigation was to determine the distribution of brake reaction times which can be expected from drivers who have to brake suddenly and completely unexpectedly in traffic situations. The experiments were carried out as follows: 1. Brake reaction time was measured on a large group of drivers (321), in an anticipated situation on the road (Brake reaction time 1). 2. A small group of drivers (5) was repeatedly tested in the same way (Brake reaction time 2). 3. The same small group was repeatedly tested in a surprise situation (Brake reaction time 3). 4. The ratio of brake reaction time 3 to brake reaction time 2 was used as a correction factor and applied to brake reaction time 1. The corrected median of the resulting distribution was 0.9 sec.; 25% of the group was estimated to have a brake reaction time longer than 1.2 sec.

Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

2019 ◽  
Vol 2673 (12) ◽  
pp. 367-379 ◽  
Author(s):  
Yalda Rahmati ◽  
Mohammadreza Khajeh Hosseini ◽  
Alireza Talebpour ◽  
Benjamin Swain ◽  
Christopher Nelson
Keyword(s):  
Autonomous Vehicles ◽  
Simulation Models ◽  
Driving Behavior ◽  
Data Driven ◽  
Microscopic Simulation ◽  
Car Following ◽  
The Road ◽  
Human Driver ◽  
Significant Difference ◽  
On The Road

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.

More obstacles on the road to unification

2007 ◽  
Vol 30 (1) ◽  
pp. 41-41 ◽  
Author(s):  
Eric Alden Smith
Keyword(s):  
Decision Making ◽  
Human Behavior ◽  
Real World ◽  
Institutional Constraints ◽  
Historical Contingency ◽  
Behavioral Sciences ◽  
The Road ◽  
On The Road

The synthesis proposed by Gintis is valuable but insufficient. Greater consideration must be given to epistemological diversity within the behavioral sciences, to incorporating historical contingency and institutional constraints on decision-making, and to vigorously testing deductive models of human behavior in real-world contexts.

scholarly journals Perbedaan Penyerapan Pb pada Berbagai Jenis Tanaman

2020 ◽  
Vol 15 (1) ◽  
pp. 140-148
Author(s):  
Sucia Elsa Azzahri ◽  
Burhan Muslim ◽  
Muchsin Riviwanto
Keyword(s):  
Motor Vehicles ◽  
P Value ◽  
The Road ◽  
Study Approach ◽  
Significant Difference ◽  
Lead Levels ◽  
Wet Ashing ◽  
Analytical Research ◽  
The Government ◽  
On The Road

Air pollution comes from many factors, one of which comes from vehicles where the smoke produced by motor vehicles contains dangerous heavy metals, Pb. Ujung Gurun Road is one of the densely populated roads which has many pollutant-absorbing plants that line the roadside. This research was conducted with the aim to determine differences in plant types in absorbing lead content (Pb) of air on the road. Analytical research with a comparative study approach. The measurement used is the Wet Ashing Method (wet ashing) for the destruction of the sample, then analyzed using the Atomic Absorption Spectrophotometer (AAS). Data were analyzed using Anova test to see whether there were differences in Glondokan, Mahogany and Angsana plants in absorbing lead air. The results showed lead levels in leaves of glondokan plants were 0.9134 μg / g higher than leaves of mahogany plants as much as 0.764 ug / g and angsana 0.40 ug / g. There is a significant difference in the types of plants in the absorption of air Pb levels in Jalan Ujung Gurun Padang City with p value 0.002 where p <a. For this reason, the monday plant can be used as one of the plants that can be used as one of the government program plants for the absorption of Pb content of air produced by motor vehicles other than mahogany and angsana.

scholarly journals The Effect of Using Chip and Slash in Reducing Sheet Erosion on Forest Road Slopes

2018 ◽  
Author(s):  
Yilmaz Turk
Keyword(s):  
Suspended Sediment ◽  
The Other ◽  
Forest Road ◽  
The Road ◽  
Significant Difference ◽  
Sediment Loss ◽  
On The Road ◽  
Annual Amount ◽  
Runoff Plots ◽  
Sheet Erosion

This study compared the use of chip and slash to minimize the loss of sediment on newly constructed forest road slopes and investigated the annual amount of sediment loss on bare forest road slopes. A runoff block (sample field) was established for each of the four designated test sites (two cutslopes and two fillslopes). Each block had three runoff plots. One of the runoff plots was left empty for the control (CNT), while chip (C) and slash (S), respectively, were deposited in the other two. A total of 108 water samples were taken from the test sites and the amount of their suspended sediment calculated in the laboratory. As a result of this study, it was determined that the amount of soil loss in the control plots was about 1.26 times higher than in the slash plots and 2.21 times higher than in the chip plots. According to the results of variance analysis on the amounts of sediment, a statistically significant difference was found between the suspended sediment quantities transported on the road slopes (P &lt;0.05). However, no statistically significant difference between the suspended sediment quantities transported in the plots and the other variables of aspect, gradient or road slope was revealed by the t-test (P &gt;0.05).

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