Development of Quantitative Methods for Evaluating Failure Safety of Level 3 Autonomous Vehicles

2019 ◽  
Vol 18 (1) ◽  
pp. 91-102
Author(s):  
Dooyong Kim ◽  
◽  
Sangyeop Lee ◽  
Hyuckkee Lee ◽  
Inseong Choi ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Quantitative Methods ◽  
Level 3

scholarly journals Driver seat comfort for level 3-4 autonomous vehicles

Work ◽  
2021 ◽  
Vol 68 (s1) ◽  
pp. S111-S118
Author(s):  
Neil J. Mansfield ◽  
Kartikeya Walia ◽  
Aditya Singh
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Case Methods ◽  
Driving Mode ◽  
Full Control ◽  
Seat Comfort ◽  
Autonomous Mode ◽  
Different Types ◽  
Level 3 ◽  
Level 2

BACKGROUND: Autonomous vehicles can be classified on a scale of automation from 0 to 5, where level 0 corresponds to vehicles that have no automation to level 5 where the vehicle is fully autonomous and it is not possible for the human occupant to take control. At level 2, the driver needs to retain attention as they are in control of at least some systems. Level 3-4 vehicles are capable of full control but the human occupant might be required to, or desire to, intervene in some circumstances. This means that there could be extended periods of time where the driver is relaxed, but other periods of time when they need to drive. OBJECTIVE: The seat must therefore be designed to be comfortable in at least two different types of use case. METHODS: This driving simulator study compares the comfort experienced in a seat from a production hybrid vehicle whilst being used in a manual driving mode and in autonomous mode for a range of postures. RESULTS: It highlights how discomfort is worse for cases where the posture is non-optimal for the task. It also investigates the design of head and neckrests to mitigate neck discomfort, and shows that a well-designed neckrest is beneficial for drivers in autonomous mode.

scholarly journals Computational Solutions Based on Bayesian Networks to Hierarchize and to Predict Factors Influencing Gender Fairness in the Transport System: Four Use Cases

2021 ◽  
Vol 13 (20) ◽  
pp. 11372
Author(s):  
Gemma Dolores Molero ◽  
Sara Poveda-Reyes ◽  
Ashwani Kumar Malviya ◽  
Elena García-Jiménez ◽  
Maria Chiara Leva ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Transport System ◽  
Autonomous Vehicles ◽  
Personal Space ◽  
Use Cases ◽  
Railway Transport ◽  
Analytic Hierarchy ◽  
Perception Of Safety ◽  
Level 3 ◽  
The Difference

Previous studies have highlighted inequalities and gender differences in the transport system. Some factors or fairness characteristics (FCs) strongly influence gender fairness in the transport system. The difference with previous studies, which focus on general concepts, is the incorporation of level 3 FCs, which are more detailed aspects or measures that can be implemented by companies or infrastructure managers and operators in order to increase fairness and inclusion in each use case. The aim of this paper is to find computational solutions, Bayesian networks, and analytic hierarchy processes capable of hierarchizing level 3 FCs and to predict by simulation their values in the case of applying some improvements. This methodology was applied to data from women in four use cases: railway transport, autonomous vehicles, bicycle sharing stations, and transport employment. The results showed that fairer railway transport requires increased personal space, hospitality rooms, help points, and helpline numbers. For autonomous vehicles, the perception of safety, security, and sustainability should be increased. The priorities for bicycle sharing stations are safer cycling paths avoiding hilly terrains and introducing electric bicycles, child seats, or trailers to carry cargo. In transport employment, the priorities are fair recruitment and promotion processes and the development of family-friendly policies.

scholarly journals Multimodal Cue Combinations: A Possible Approach to Designing In-Vehicle Takeover Requests for Semi-autonomous Driving

2019 ◽  
Vol 63 (1) ◽  
pp. 1739-1743
Author(s):  
Gaojian Huang ◽  
Clayton Steele ◽  
Xinrui Zhang ◽  
Brandon J. Pitts
Keyword(s):  
Autonomous Vehicles ◽  
Response Times ◽  
Autonomous Driving ◽  
Multimodal Signals ◽  
Road Sign ◽  
Sign Detection ◽  
Level 3 ◽  
Perceived Workload ◽  
Tactile Cues ◽  
Vehicle Automation

The rapid growth of autonomous vehicles is expected to improve roadway safety. However, certain levels of vehicle automation will still require drivers to ‘takeover’ during abnormal situations, which may lead to breakdowns in driver-vehicle interactions. To date, there is no agreement on how to best support drivers in accomplishing a takeover task. Therefore, the goal of this study was to investigate the effectiveness of multimodal alerts as a feasible approach. In particular, we examined the effects of uni-, bi-, and trimodal combinations of visual, auditory, and tactile cues on response times to takeover alerts. Sixteen participants were asked to detect 7 multimodal signals (i.e., visual, auditory, tactile, visual-auditory, visual-tactile, auditory-tactile, and visual-auditory-tactile) while driving under two conditions: with SAE Level 3 automation only or with SAE Level 3 automation in addition to performing a road sign detection task. Performance on the signal and road sign detection tasks, pupil size, and perceived workload were measured. Findings indicate that trimodal combinations result in the shortest response time. Also, response times were longer and perceived workload was higher when participants were engaged in a secondary task. Findings may contribute to the development of theory regarding the design of takeover request alert systems within (semi) autonomous vehicles.

scholarly journals Development of a Situational Awareness Estimation Model Considering Traffic Environment for Unscheduled Takeover Situations

2020 ◽  
Author(s):  
Hiroaki Hayashi ◽  
Naoki Oka ◽  
Mitsuhiro Kamezaki ◽  
Shigeki Sugano
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Situational Awareness ◽  
Support Vector ◽  
Steering Control ◽  
Estimation Model ◽  
Level 3 ◽  
Estimation System ◽  
Leave One Out ◽  
The Relationship

Abstract In semi-autonomous vehicles (SAE level 3) that requires drivers to takeover (TO) the control in critical situations, a system needs to judge if the driver have enough situational awareness (SA) for manual driving. We previously developed a SA estimation system that only used driver’s glance data. For deeper understanding of driver’s SA, the system needs to evaluate the relevancy between driver’s glance and surrounding vehicle and obstacles. In this study, we thus developed a new SA estimation model considering driving-relevant objects and investigated the relationship between parameters. We performed TO experiments in a driving simulator to observe driver’s behavior in different position of surrounding vehicles and TO performance such as the smoothness of steering control. We adopted support vector machine to classify obtained dataset into safe and dangerous TO, and the result showed 83% accuracy in leave-one-out cross validation. We found that unscheduled TO led to maneuver error and glance behavior differed from individuals.

Translationese and interlanguage in inverse translation: A case study

2021 ◽  
Vol 22 (1) ◽  
pp. 45-63
Author(s):  
Ming Yue ◽  
Boyang Sun
Keyword(s):  
Quantitative Methods ◽  
Comparative Method ◽  
Translation Studies ◽  
Language Complexity ◽  
Complexity Metrics ◽  
Working Definition ◽  
Level 3 ◽  
Definition Of ◽  
Inverse Translation

AbstractCurrent translation studies do not present a clear distinction between ‘translationese’ and ‘interlanguage’, giving rise to conceptual and terminology confusion. To disentangle these two concepts, we start with a relatively conservative working definition of translationese, then find it necessary to first differentiate between direct and inverse translations, according to whether the translator's L1 equals to TL or not. Taking Zhuangzi (a Daoist classic) as a case, we made both inter- and intra-speaker comparisons among Lin Yu-tang's inverse translation, James Legge's direct translation, and the two translators' creative works in English, with well-established language complexity metrics and quantitative methods. Results show that: (1) Inverse and direct translations are remarkably different in terms of complexity; (2) Inverse translation demonstrates both features of interlanguage and translationese, with the former mostly at lexical level and the latter at syntactic level; (3) Similar patterns are also discovered in Lin's other inverse translated works, suggesting our quantitative comparative method proposed may be reliable to some extent. Such results support our proposal that translationese and interlanguage should and can be differentiated for both theoretical and practical purposes.

Takeover Request Design in Automated Driving: A Systematic Review

2021 ◽  
Vol 65 (1) ◽  
pp. 868-872
Author(s):  
Kevin Joel Salubre ◽  
Dan Nathan-Roberts
Keyword(s):  
Systematic Review ◽  
Current Literature ◽  
Autonomous Vehicles ◽  
Manual Control ◽  
Automated Driving ◽  
Lateral Control ◽  
Vehicle System ◽  
Level 3

Autonomous vehicles (AV) with “level 3” automation and above are expected to take full longitudinal and lateral control, which relinquishes the driver from manual control and allows for engagement with non-driving-related tasks. Despite the advance nature of a level 3 vehicle, system limitations can occur, and the driver is expected to re-engage in manual driving at a moment’s notice. Current literature has been focused on takeover performance during a takeover request (TOR) and the effects of multimodal warnings, but there is little consensus on how modality stimulus is presented. This systematic review summarizes the current designs and implementations of TORs of level 3 AVs and above. Identified themes in the review were categorized into three sections: non-driving-related tasks, driving scenarios, and takeover modality. A summary of how researchers utilized these themes in the current literature are discussed as well as implications and further research.

scholarly journals Acceptance of Self-Driving Cars in United Arab Emirates

2020 ◽  
pp. 19-63
Author(s):  
Arwa A. Al Shamsi
Keyword(s):  
United Arab Emirates ◽  
Technology Implementation ◽  
Autonomous Vehicles ◽  
Quantitative Methods ◽  
Research Paper ◽  
Transportation Industry ◽  
Adoption Of Technology ◽  
Car Accidents ◽  
The Government ◽  
Self Driving Cars

Transportation industry witnessing a revolution of the emerging of self-driving cars which are autonomous vehicles that drive by itself without human involvement. It is expected that self-driving cars would have powerful feature and would provide a lot of benefits such as reducing traveling time, reducing traffic jams, reducing car accidents and many other benefits. The government of United Arab Emirates adopt technology implementation in all life aspects in the country starting by turning into smart government and then smart education and many other implementations of using technology in different aspects of the country. This adoption of technology positively affected UAE people’s intention toward accepting technology. As UAE government always adopt best technology practices, it is expected that United Arab Emirates would adopt the using of autonomous cars. The aim of this research paper is to investigate UAE people’s intention to turn into using self-driving cars. Researcher aim as well to explore the most common factors that may affect people’s intention to turn into using self-driving cars. This research paper methodology based on quantitative methods for gathering data in which questionnaire developed and sent to people live in United Arab Emirates.

scholarly journals Tradeoffs between safe/comfortable headways versus mobility-enhancing headways in an automated driving environment: preliminary insights using a driving simulator experiment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yujie Li ◽  
Tiantian Chen ◽  
Sikai Chen ◽  
Samuel Labi
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Research Question ◽  
Probit Analysis ◽  
Automated Driving ◽  
Content Type ◽  
Driving Mode ◽  
Level 3 ◽  
Autonomous Mobility ◽  
User Friendly

PurposeThe anticipated benefits of connected and autonomous vehicles (CAVs) include safety and mobility enhancement. Small headways between successive vehicles, on one hand, can cause increased capacity and throughput and thereby improve overall mobility. On the other hand, small headways can cause vehicle occupant discomfort and unsafety. Therefore, in a CAV environment, it is important to determine appropriate headways that offer a good balance between mobility and user safety/comfort.Design/methodology/approachIn addressing this research question, this study carried out a pilot experiment using a driving simulator equipped with a Level-3 automated driving system, to measure the threshold headways. The Method of Constant Stimuli (MCS) procedure was modified to enable the estimation of two comfort thresholds. The participants (drivers) were placed in three categories (“Cautious,” “Neutral” and “Confident”) and 250 driving tests were carried out for each category. Probit analysis was then used to estimate the threshold headways that differentiate drivers' discomfort and their intention to re-engage the driving tasks.FindingsThe results indicate that “Cautious” drivers tend to be more sensitive to the decrease in headways, and therefore exhibit greater propensity to deactivate the automated driving mode under a longer headway relative to other driver groups. Also, there seems to exist no driver discomfort when the CAV maintains headway up to 5%–9% shorter than the headways they typically adopt. Further reduction in headways tends to cause discomfort to drivers and trigger take over control maneuver.Research limitations/implicationsIn future studies, the number of observations could be increased further.Practical implicationsThe study findings can help guide specification of user-friendly headways specified in the algorithms used for CAV control, by vehicle manufacturers and technology companies. By measuring and learning from a human driver's perception, AV manufacturers can produce personalized AVs to suit the user's preferences regarding headway. Also, the identified headway thresholds could be applied by practitioners and researchers to update highway lane capacities and passenger-car-equivalents in the autonomous mobility era.Originality/valueThe study represents a pioneering effort and preliminary pilot driving simulator experiment to assess the tradeoffs between comfortable headways versus mobility-enhancing headways in an automated driving environment.

scholarly journals SBML Level 3 package: Qualitative Models, Version 1, Release 1

2015 ◽  
Vol 12 (2) ◽  
pp. 691-730 ◽  
Author(s):  
Claudine Chaouiya ◽  
Sarah M. Keating ◽  
Duncan Berenguier ◽  
Aurélien Naldi ◽  
Denis Thieffry ◽  
...  
Keyword(s):  
Biological Networks ◽  
Quantitative Methods ◽  
Expression Data ◽  
Qualitative Modelling ◽  
The Core ◽  
Qualitative Models ◽  
Level 3 ◽  
Influence Graphs ◽  
Definition Of ◽  
Modelling Methods

Summary Quantitative methods for modelling biological networks require an in-depth knowledge of the biochemical reactions and their stoichiometric and kinetic parameters. In many practical cases, this knowledge is missing. This has led to the development of several qualitative modelling methods using information such as, for example, gene expression data coming from functional genomic experiments. The SBML Level 3 Version 1 Core specification does not provide a mechanism for explicitly encoding qualitative models, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs.The SBML Qualitative Models package for SBML Level 3 adds features so that qualitative models can be directly and explicitly encoded. The approach taken in this package is essentially based on the definition of regulatory or influence graphs. The SBML Qualitative Models package defines the structure and syntax necessary to describe qualitative models that associate discrete levels of activities with entity pools and the transitions between states that describe the processes involved. This is particularly suited to logical models (Boolean or multi-valued) and some classes of Petri net models can be encoded with the approach.

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