scholarly journals Sight distance analyses for autonomous vehicles in Civil 3D

2021 ◽  
Author(s):  
Khalil Khaska ◽  
Dániel Miletics
Keyword(s):  
Reaction Time ◽  
Human Factors ◽  
Surface Properties ◽  
Autonomous Vehicles ◽  
Road Surface ◽  
Eye Position ◽  
Road Design ◽  
Sight Distance ◽  
Automation Level ◽  
Self Driving Cars

AbstractNowadays, self-driving cars have a wide reputation among people that is constantly increasing, many manufacturers are developing their own autonomous vehicles. These vehicles are equipped with various sensors that are placed at several points in the car. These sensors provide information to control the vehicle (partially or completely, depending on the automation level). Sight distances on roads are defined according to various traffic situations (stopping, overtaking, crossing, etc.). Safety reasons require these sight distances, which are calculated from human factors (e.g., reaction time), vehicle characteristics (e.g., eye position, brakes), road surface properties, and other factors. Autodesk Civil 3D is a widely used tool in the field of road design, the software however was developed based on the characteristics of the human drivers and conventional vehicles.

Calibrating Design Guidelines using Mental Workload and Reliability Analysis

2020 ◽  
Vol 2674 (8) ◽  
pp. 360-369
Author(s):  
Karim Habib ◽  
Maged Gouda ◽  
Karim El-Basyouny
Keyword(s):  
Human Factors ◽  
Reliability Analysis ◽  
Mental Workload ◽  
Design Guidelines ◽  
Horizontal Curves ◽  
Road Design ◽  
Sight Distance ◽  
Stopping Sight Distance ◽  
The Many ◽  
Time Required

The generic nature of road design is indiscriminate to age, race, or gender, as it is implicitly assumed that there are few behavioral differences between drivers while traversing various alignment elements (e.g., horizontal curves, tangential segments, etc.). For instance, the perception reaction time required, which is based on an 85th percentile value, on a tangent section is the same as that on a horizontal curve. This suggests that current guidelines do not consider the complexity that some geometric features might induce on drivers, and consequently, there is a need to address the many considerations of diversity. In this respect, human factors should be explicitly included in design guidelines. One aspect of human factors that has received little attention in the literature is related to the mental workload. In this study, a procedure is presented to estimate the mental workload for stopping sight distance. Then, reliability analysis is conducted to compare the change in the probability of non-compliance owing to the available sight distance and based on the mental workload. By analyzing data from 12 horizontal curves in Alberta, Canada, the probability of non-compliance dropped from 9.1% to 0.7%, and a moderate correlation with collisions was found. The results of the analysis showed that incorporating mental workload into the geometric design process can improve safety performance.

scholarly journals Cognitive Load Variability from Road Characteristics Should Influence a Safety Requirement for Vehicle Stopping Sight Distance

2019 ◽  
Vol 63 (1) ◽  
pp. 2129-2133
Author(s):  
Cody A. Pennetti ◽  
Kelsey Hollenback ◽  
Inki Kim ◽  
James H. Lambert
Keyword(s):  
Reaction Time ◽  
Weather Conditions ◽  
Design Standards ◽  
Road Design ◽  
Adverse Weather Conditions ◽  
Sight Distance ◽  
Adverse Weather ◽  
Road Geometry ◽  
Stopping Sight Distance ◽  
Road Characteristics

Current U.S. geometric road design standards are based on a prescribed value for a driver’s perception-reaction time (a constant value of 2.5 seconds), which represents the time necessary for a driver to safely stop the vehicle to avoid a crash (referred to as a stopping sight distance); however, these standards fail to consider how road complexity, driver risk perception, and visual stimuli can influence perception-reaction time. With over a million vehicle fatalities a year (WHO, n.d.), it is necessary to investigate methods of improving driver safety. The influence of road characteristics is considered with some road design policies, but not currently applied to stopping sight distance. This paper introduces theoretical considerations for increasing perception-reaction time (and thereby adjusting speed limits or road geometry) based on roadway complexity (volume of vehicles, road geometry, pedestrian crossings, frequency of adverse weather conditions, or other conditions).

scholarly journals An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1788
Author(s):  
Gomatheeshwari Balasekaran ◽  
Selvakumar Jayakumar ◽  
Rocío Pérez de Prado
Keyword(s):  
Execution Time ◽  
Autonomous Vehicles ◽  
Rapid Development ◽  
Single Layer ◽  
Battery Life ◽  
Learning Approaches ◽  
Task Management ◽  
Inference System ◽  
Safe Driving ◽  
Self Driving Cars

With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms).Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.

scholarly journals CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1402
Author(s):  
Taehee Lee ◽  
Yeohwan Yoon ◽  
Chanjun Chun ◽  
Seungki Ryu
Keyword(s):  
Surface Crack ◽  
Autonomous Vehicles ◽  
Crack Detection ◽  
Model Performance ◽  
Semantic Segmentation ◽  
Road Surface ◽  
Image Brightness ◽  
The Road ◽  
Detection Model ◽  
Artificial Intelligence Models

Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.

scholarly journals Petrographic and Geotechnical Characteristics of Carbonate Aggregates from Poland and Their Correlation with the Design of Road Surface Structures

Materials ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2034
Author(s):  
Jerzy Trzciński ◽  
Emilia Wójcik ◽  
Mateusz Marszałek ◽  
Paweł Łukaszewski ◽  
Marek Krajewski ◽  
...  
Keyword(s):  
Road Surface ◽  
Surface Structures ◽  
Engineering Properties ◽  
Geological Engineering ◽  
Functional Relationships ◽  
Road Design ◽  
Geotechnical Characteristics ◽  
Design Calculations ◽  
Building Engineering ◽  
Design And Construction

The paper presents the basic problem related with practical application of carbonate rocks in construction: are carbonate aggregates produced from such rocks favorable for building engineering, particularly for road design and construction? To resolve this problem, (1) the geological-engineering properties of aggregates are presented, (2) the correlation between petrographic and engineering parameters is shown, and (3) a strict correlation between the geological-engineering properties and the freezing-thawing and crushing resistance is recognized. This knowledge has allowed to assess the usefulness of asphalt concrete (AC) made from dolomite and limestone aggregates in the design and construction of road surface structures. The petrography was characterized using optical microscopy and scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscope (EDS). Engineering properties were determined in accordance with European and Polish norms and guidelines. Statistical and design calculations were performed using dedicated software. The petrographic properties, and selected physical and mechanical parameters of the aggregates, were tested to show their influence on the freezing–thawing and crushing resistance. Strong functional relationships between the water adsorption, and the freezing–thawing and crushing resistance have been observed. Aggregate strength decreased after saturation with increasing concentrations of salt solutions. Calculations of AC fatigue durability and deformation allow for reducing the thickness of the road surface structure by about 20% in comparison to normative solutions. This conclusion has impact on the economy of road design and construction, and allows for a rational utilization of rock resources, which contributes to sustainable development of the construction industry.

The ideological divide in public perceptions of self-driving cars

2020 ◽  
Vol 29 (4) ◽  
pp. 436-451
Author(s):  
Yilang Peng
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Scientific Literacy ◽  
Autonomous Vehicles ◽  
The United States ◽  
Public Perceptions ◽  
New Information ◽  
Nationally Representative ◽  
Self Driving Cars ◽  
Social Conservatism

Applications in artificial intelligence such as self-driving cars may profoundly transform our society, yet emerging technologies are frequently faced with suspicion or even hostility. Meanwhile, public opinions about scientific issues are increasingly polarized along the ideological line. By analyzing a nationally representative panel in the United States, we reveal an emerging ideological divide in public reactions to self-driving cars. Compared with liberals and Democrats, conservatives and Republicans express more concern about autonomous vehicles and more support for restrictively regulating autonomous vehicles. This ideological gap is largely driven by social conservatism. Moreover, both familiarity with driverless vehicles and scientific literacy reduce respondents’ concerns over driverless vehicles and support for regulation policies. Still, the effects of familiarity and scientific literacy are weaker among social conservatives, indicating that people may assimilate new information in a biased manner that promotes their worldviews.

A Comparison of Presentation Mediums for the Study of Trust in Autonomous Vehicles

2021 ◽  
Vol 65 (1) ◽  
pp. 878-882
Author(s):  
Katherine Garcia ◽  
Ian Robertson ◽  
Philip Kortum
Keyword(s):  
Autonomous Vehicles ◽  
Automated System ◽  
Spatial Presence ◽  
Viewing Condition ◽  
Stop Sign ◽  
Flat Screen ◽  
The Mean ◽  
Presentation Methods ◽  
Self Driving Cars ◽  
User Trust

The purpose of this study is to compare presentation methods for use in the validation of the Trust in Selfdriving Vehicle Scale (TSDV), a questionnaire designed to assess user trust in self-driving cars. Previous studies have validated trust instruments using traditional videos wherein participants watch a scenario involving an automated system but there are strong concerns about external validity with this approach. We examined four presentation conditions: a flat screen monitor with a traditional video, a flat screen with a 2D 180 video, an Oculus Go VR headset with a 2D 180 video, and an Oculus Go with a 3D VR video. Participants watched eight video scenarios of a self-driving vehicle attempting a right-hand tum at a stop sign and rated their trust in the vehicle shown in the video after each scenario using the TSDV and rated telepresence for the viewing condition. We found a significant interaction between the mean TSDV scores for pedestrian collision and presentation condition. The TSDV mean in the Headset 2D 180 condition was significantly higher than the other three conditions. Additionally, when used to view the scenarios as 3D VR videos, the headset received significantly higher ratings of spatial presence compared to the condition using a flatscreen a 2D video; none of the remaining comparisons were statistically significant. Based on the results it is not recommended that the headset be used for short scenarios because the benefits do not outweigh the costs.

Pedestrians, Autonomous Vehicles, and Cities

2016 ◽  
Vol 38 (1) ◽  
pp. 6-12 ◽  
Author(s):  
Adam Millard-Ball
Keyword(s):  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Neighborhoods ◽  
Urban Traffic ◽  
Risk Averse ◽  
Strategic Interactions ◽  
Road Users ◽  
Self Driving Cars ◽  
Time Autonomous

Autonomous vehicles, popularly known as self-driving cars, have the potential to transform travel behavior. However, existing analyses have ignored strategic interactions with other road users. In this article, I use game theory to analyze the interactions between pedestrians and autonomous vehicles, with a focus on yielding at crosswalks. Because autonomous vehicles will be risk-averse, the model suggests that pedestrians will be able to behave with impunity, and autonomous vehicles may facilitate a shift toward pedestrian-oriented urban neighborhoods. At the same time, autonomous vehicle adoption may be hampered by their strategic disadvantage that slows them down in urban traffic.

scholarly journals What drives you to use a driverless car?

2020 ◽  
Author(s):  
Sándor Huszár ◽  
Zoltán Majó-Petri
Keyword(s):  
Technology Acceptance ◽  
Influencing Factors ◽  
Behavioral Intention ◽  
Autonomous Vehicles ◽  
Structural Equation ◽  
Technological Development ◽  
Perceived Safety ◽  
Psychological Models ◽  
Acceptance Model ◽  
Self Driving Cars

The investigation of driverless car from the economic perspective is one of the most discussed topics nowadays. Although it can be approached from various perspectives there is still a lack of studies focusing on the behavioral intention to use self-driving cars and its influencing factors. Over the last few decades, various psychological models have been developed to investigate the influencing factors of usage of certain technologies, but most of them cannot provide clear answers on consumer attitudes and intentions with regard to autonomous vehicles. Thus, new models have appeared to better describe the psychological factors of this new technological development that will revolutionize the future of mobility. In our research CTAM (Car Technology Acceptance Model) was used to measure intention to using self-driving cars. In 2019, 314 participants responded to our questionnaire and provided answers to the given questions. We used structural equation modelling to investigate the linkages between the behavioral intention and influencing factors revealed during the literature review. According to the results, the most important influencing factors of intention are attitude, perceived safety and social norms, while anxiety (of using the technology), effort expectancy, performance expectancy, and self-efficacy have not been proven important factors. The model used in our investigation explains behavioral intention to a great extent (63%).

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