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 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 Tell Them How They Did: Feedback on Operator Performance Helps Calibrate Perceived Ease of Use in Automated Driving

2019 ◽  
Vol 3 (2) ◽  
pp. 29 ◽  
Author(s):  
Yannick Forster ◽  
Sebastian Hergeth ◽  
Frederik Naujoks ◽  
Josef Krems ◽  
Andreas Keinath
Keyword(s):  
Driving Simulator ◽  
Ease Of Use ◽  
Use Case ◽  
Perceived Ease Of Use ◽  
Automated Driving ◽  
Human Machine Interfaces ◽  
Level 3 ◽  
Level 2 ◽  
Over Time

The development of automated driving will profit from an agreed-upon methodology to evaluate human–machine interfaces. The present study examines the role of feedback on interaction performance provided directly to participants when interacting with driving automation (i.e., perceived ease of use). In addition, the development of ratings itself over time and use case specificity were examined. In a driving simulator study, N = 55 participants completed several transitions between Society of Automotive Engineers (SAE) level 0, level 2, and level 3 automated driving. One half of the participants received feedback on their interaction performance immediately after each use case, while the other half did not. As expected, the results revealed that participants judged the interactions to become easier over time. However, a use case specificity was present, as transitions to L0 did not show effects over time. The role of feedback also depended on the respective use case. We observed more conservative evaluations when feedback was provided than when it was not. The present study supports the application of perceived ease of use as a diagnostic measure in interaction with automated driving. Evaluations of interfaces can benefit from supporting feedback to obtain more conservative results.

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.

scholarly journals Does Adaptive Mode Transition Contribute to Better Driver Intervention in Highly Automated Driving?

2019 ◽  
Vol 63 (1) ◽  
pp. 287-291
Author(s):  
Huiping Zhou ◽  
Makoto Itoh ◽  
Satoshi Kitazaki
Keyword(s):  
Driving Simulator ◽  
Mode Transition ◽  
Automated Driving ◽  
Automated Control ◽  
Human Driver ◽  
Level 3 ◽  
Level Transition ◽  
Verbal Messages ◽  
Level 2 ◽  
Highly Automated Driving

This paper presents an adaptive mode (level) transition in highly combined driving automation in which the mode of a system could adaptively shift to any level including SAE level 3 (conditional automation, CA) to level 2 (partial automation) based on the driving environment. We show the effects of the adaptive transition on the take over of car control by a human driver and driving behavior after intervention when the system issues a response to intervene. A driving simulator experiment is conducted to collect data during the transition from automated control to manual driving in three scenes: obstacle on a driving lane, blurred lane mark, and stopped car ahead. Results indicate that the interventions of drivers who experience the adaptive transition are delayed in comparison to those who experience only the fixed transition. The adaptive transition is conducive for drivers to stop the car for preventing a potential collision with a stopped car ahead. Owing to the adaptive transition, drivers perceive a critical hazard after taking over car control and provide a rapid response. In addition, during the adaptive transition, drivers prefer verbal messages to the simple “beeping” message.

Historical text archives and prosopography: the COEL database system

1998 ◽  
Vol 10 (1-3) ◽  
pp. 57-72 ◽  
Author(s):  
K. S. B. Keats-Rohan
Keyword(s):  
Research Tool ◽  
Direct Access ◽  
Specific Situation ◽  
Core Data ◽  
Secondary Sources ◽  
The Core ◽  
Level 3 ◽  
Full Discussion ◽  
Available Information ◽  
Level 2

The COEL database and database software, a combined reference and research tool created by historians for historians, is presented here through Screenshots illustrating the underlying theoretical model and the specific situation to which that has been applied. The key emphases are upon data integrity, and the historian's role in interpreting and manipulating what is often contentious data. From a corpus of sources (Level 1) certain core data are extracted for separate treatment at an interpretive level (Level 3), based upon a master list of the core data (Level 2). The core data are interdependent: each record in Level 2 is of interest in itself; and it either could or should be associated with an(other) record(s) as a specific entity. Sometimes the sources are ambiguous and the association is contentious, necessitating a probabilty-coding approach. The entities created by the association process can then be treated at a commentary level, introducing material external to the database, whether primary or secondary sources. A full discussion of the difficulties is provided within a synthesis of available information on the core data. Direct access to the source texts is only ever a mouse click away. Fully query able, COEL is formidable look-up and research tool for users of all levels, who remain free to exercise an alternative judgement on the associations of the core data. In principle, there is no limit on the type of text or core data that could be handled in such a system.

EVALUASI PELATIHAN DI PT. PUPUK KUJANG CIKAMPEK

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Lania Muharsih ◽  
Ratih Saraswati
Keyword(s):  
Training Evaluation ◽  
Work Productivity ◽  
Learning Behavior ◽  
Level 3 ◽  
Level 1 ◽  
Four Levels ◽  
A Company ◽  
Level 2 ◽  
Evaluation Sheet ◽  
Assessment Factor

This study aims to determine the training evaluation at PT. Kujang Fertilizer. PT. Pupuk Kujang is a company engaged in the field of petrochemicals. Evaluation sheet of PT. Fertilizer Kujang is made based on Kirkpatrick's theory which consists of four levels of evaluation, namely reaction, learning, behavior, and results. At level 1, namely reaction, in the evaluation sheet is in accordance with the theory of Kirkpatrick, at level 2 that is learning should be held pretest and posttest but only made scale. At level 3, behavior, according to theory, but on assessment factor number 3, quantity and work productivity should not need to be included because they are included in level 4. At level 4, that is the result, here is still lacking to get a picture of the results of the training that has been carried out because only based on answers from superiors without evidence of any documents.   Keywords: Training Evaluation, Kirkpatrick Theory.    Penelitian ini bertujuan mengetahui evaluasi training di PT. Pupuk Kujang. PT. Pupuk Kujang merupakan perusahaan yang bergerak di bidang petrokimia. Lembar evaluasi PT. Pupuk Kujang dibuat berdasarkan teori Kirkpatrick yang terdiri dari empat level evaluasi, yaitu reaksi, learning, behavior, dan hasil. Pada level 1 yaitu reaksi, di lembar evaluasi tersebut sudah sesuai dengan teori dari Kirkpatrick, pada level 2 yaitu learning seharusnya diadakan pretest dan posttest namun hanya dibuatkan skala. Pada level 3 yaitu behavior, sudah sesuai teori namun pada faktor penilaian nomor 3 kuantitas dan produktivitas kerja semestinya tidak perlu dimasukkan karena sudah termasuk ke dalam level 4. Pada level 4 yaitu hasil, disini masih sangat kurang untuk mendapatkan gambaran hasil dari pelatihan yang sudah dilaksanakan karena hanya berdasarkan dari jawaban atasan tanpa bukti dokumen apapun.   Kata kunci: Evaluasi Pelatihan, Teori Kirkpatrick.

Variation in antibiotic use across intensive care units (ICU): A population-based cohort study in Ontario, Canada

2020 ◽  
Vol 41 (9) ◽  
pp. 1035-1041
Author(s):  
Erika Y. Lee ◽  
Michael E. Detsky ◽  
Jin Ma ◽  
Chaim M. Bell ◽  
Andrew M. Morris
Keyword(s):  
Cohort Study ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Ventilatory Support ◽  
Severity Of Illness ◽  
Antibiotic Use ◽  
Patient Factors ◽  
Days Of Therapy ◽  
Level 3 ◽  
Level 2

AbstractObjectives:Antibiotics are commonly used in intensive care units (ICUs), yet differences in antibiotic use across ICUs are unknown. Herein, we studied antibiotic use across ICUs and examined factors that contributed to variation.Methods:We conducted a retrospective cohort study using data from Ontario’s Critical Care Information System (CCIS), which included 201 adult ICUs and 2,013,397 patient days from January 2012 to June 2016. Antibiotic use was measured in days of therapy (DOT) per 1,000 patient days. ICU factors included ability to provide ventilator support (level 3) or not (level 2), ICU type (medical-surgical or other), and academic status. Patient factors included severity of illness using multiple-organ dysfunction score (MODS), ventilatory support, and central venous catheter (CVC) use. We analyzed the effect of these factors on variation in antibiotic use.Results:Overall, 269,351 patients (56%) received antibiotics during their ICU stay. The mean antibiotic use was 624 (range 3–1460) DOT per 1,000 patient days. Antibiotic use was significantly higher in medical-surgical ICUs compared to other ICUs (697 vs 410 DOT per 1,000 patient days; P < .0001) and in level 3 ICUs compared to level 2 ICUs (751 vs 513 DOT per 1,000 patient days; P < .0001). Higher antibiotic use was associated with higher severity of illness and intensity of treatment. ICU and patient factors explained 47% of the variation in antibiotic use across ICUs.Conclusions:Antibiotic use varies widely across ICUs, which is partially associated with ICUs and patient characteristics. These differences highlight the importance of antimicrobial stewardship to ensure appropriate use of antibiotics in ICU patients.

The Impact of Mental States on Semi-autonomous Driving Takeover Performance: A Systematic Review

2020 ◽  
Vol 64 (1) ◽  
pp. 1372-1376
Author(s):  
Gaojian Huang ◽  
Christine Petersen ◽  
Brandon J. Pitts
Keyword(s):  
Systematic Review ◽  
Autonomous Vehicles ◽  
Response Times ◽  
Autonomous Driving ◽  
Mental States ◽  
Manual Control ◽  
Monitoring Systems ◽  
Wide Range ◽  
Different Types ◽  
The Impact

Semi-autonomous vehicles still require drivers to occasionally resume manual control. However, drivers of these vehicles may have different mental states. For example, drivers may be engaged in non-driving related tasks or may exhibit mind wandering behavior. Also, monitoring monotonous driving environments can result in passive fatigue. Given the potential for different types of mental states to negatively affect takeover performance, it will be critical to highlight how mental states affect semi-autonomous takeover. A systematic review was conducted to synthesize the literature on mental states (such as distraction, fatigue, emotion) and takeover performance. This review focuses specifically on five fatigue studies. Overall, studies were too few to observe consistent findings, but some suggest that response times to takeover alerts and post-takeover performance may be affected by fatigue. Ultimately, this review may help researchers improve and develop real-time mental states monitoring systems for a wide range of application domains.

scholarly journals An Atmospheric Visibility Grading Method Based on Ensemble Learning and Stochastic Weight Average

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 869
Author(s):  
Xiuguo Zou ◽  
Jiahong Wu ◽  
Zhibin Cao ◽  
Yan Qian ◽  
Shixiu Zhang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Traffic Accidents ◽  
Feature Sets ◽  
Atmospheric Visibility ◽  
Average Accuracy ◽  
Level 3 ◽  
Visual Characteristics ◽  
Level 1 ◽  
Grading Model ◽  
Level 2

In order to adequately characterize the visual characteristics of atmospheric visibility and overcome the disadvantages of the traditional atmospheric visibility measurement method with significant dependence on preset reference objects, high cost, and complicated steps, this paper proposed an ensemble learning method for atmospheric visibility grading based on deep neural network and stochastic weight averaging. An experiment was conducted using the scene of an expressway, and three visibility levels were set, i.e., Level 1, Level 2, and Level 3. Firstly, the EfficientNet was transferred to extract the abstract features of the images. Then, training and grading were performed on the feature sets through the SoftMax regression model. Subsequently, the feature sets were ensembled using the method of stochastic weight averaging to obtain the atmospheric visibility grading model. The obtained datasets were input into the grading model and tested. The grading model classified the results into three categories, with the grading accuracy being 95.00%, 89.45%, and 90.91%, respectively, and the average accuracy of 91.79%. The results obtained by the proposed method were compared with those obtained by the existing methods, and the proposed method showed better performance than those of other methods. This method can be used to classify the atmospheric visibility of traffic and reduce the incidence of traffic accidents caused by atmospheric visibility.

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