scholarly journals Gaze-Head Input: Examining Potential Interaction with Immediate Experience Sampling in an Autonomous Vehicle

2020 ◽  
Vol 10 (24) ◽  
pp. 9011
Author(s):  
Aya Ataya ◽  
Won Kim ◽  
Ahmed Elsharkawy ◽  
SeungJun Kim
Keyword(s):  
Autonomous Vehicles ◽  
Sampling Method ◽  
Autonomous Vehicle ◽  
Experience Sampling ◽  
Potential Interaction ◽  
Promising Candidate ◽  
Secondary Tasks ◽  
Perceived Workload ◽  
Immediate Experience ◽  
Reliable Interaction

Autonomous vehicles (AV) increasingly allow drivers to engage in secondary tasks such as eating or working on a laptop and thus require easy and reliable interaction inputs to facilitate communication between the driver and the vehicle. However, drivers report feeling less in control when driving is no longer the primary task, which suggests that novel approaches for assessing satisfaction regarding AV decision-making are needed. Therefore, we propose an immediate experience sampling method (IESM) that learns driver preferences for AV actions. We also suggest gaze-head input (G-HI) as a novel input in an AV. G-HI provides a hands-free, remote, and intuitive input modality that allows drivers to interact with the AV while continuing to engage in non-driving related tasks. We compare G-HI with voice and touch inputs via IESM for two simulated driving scenarios. Our results report the differences among the three inputs in terms of system usability, reaction time, and perceived workload. It also reveals that G-HI is a promising candidate for AV input interaction, which could replace voice or touch inputs where those inputs could not be utilized. Variation in driver satisfaction and expectations for AV actions confirms the effectiveness of using IESM to increase drivers’ sense of control.

Emotional Variability Predicts Tiredness in Daily Life

2016 ◽  
Vol 37 (3) ◽  
pp. 181-193 ◽  
Author(s):  
Aire Mill ◽  
Anu Realo ◽  
Jüri Allik
Keyword(s):  
Sampling Method ◽  
Daily Life ◽  
Emotional Experience ◽  
Experience Sampling ◽  
Experience Sampling Method ◽  
Building Blocks ◽  
Intraindividual Variability ◽  
Unique Role ◽  
Over Time

Abstract. Intraindividual variability, along with the more frequently studied between-person variability, has been argued to be one of the basic building blocks of emotional experience. The aim of the current study is to examine whether intraindividual variability in affect predicts tiredness in daily life. Intraindividual variability in affect was studied with the experience sampling method in a group of 110 participants (aged between 19 and 84 years) during 14 consecutive days on seven randomly determined occasions per day. The results suggest that affect variability is a stable construct over time and situations. Our findings also demonstrate that intraindividual variability in affect has a unique role in predicting increased levels of tiredness at the momentary level as well at the level of individuals.

Monitoring Daily Life Using Mobile Phones: The Experience Sampling Method

2006 ◽  
Author(s):  
Alessandra Preziosa ◽  
Marta Bassi ◽  
Daniela Villani ◽  
Andrea Gaggioli ◽  
Giuseppe Riva
Keyword(s):  
Mobile Phones ◽  
Sampling Method ◽  
Daily Life ◽  
Experience Sampling ◽  
Experience Sampling Method

Experience Sampling Method sa Konteksto ng Sikolohiyang Pilipino (Experience Sampling Method in the Context of Filipino Psychology)

2019 ◽  
Author(s):  
Grazianne-Geneve V. Mendoza ◽  
Christie Sio
Keyword(s):  
Research Methods ◽  
Sampling Method ◽  
Experience Sampling ◽  
Experience Sampling Method ◽  
Unique Contribution ◽  
Smartphone Applications ◽  
Modern Age ◽  
Traditional Research ◽  
The Everyday ◽  
Do So

Filipino:Sa loob ng mahabang panahon, ang mga metodong pampananaliksik na ginagamit sa Sikolohiyang Pilipino (SP) ay hango sa pang-araw-araw na pakikipag-ugnayan ng mga Pilipino. Ngunit makalipas ang higit 40 taon simula nang unang itatag ang SP, malaki na ang pinagbago ng pakikipag-ugnayan at pakikitungo ng mga Pilipino sa isa’t isa dahil sa modernisasyon at pag-unlad ng teknolohiya. Gayundin, dumarami na rin ang mga iskolar ng SP na kumikilala sa kahalagahan ng pagsasakatutubo-mula-sa-labas upang higit pang mapayaman ang disiplina. Kabilang dito ang pag-aangkop ng mga lapit at metodong pampananaliksik. Bilang tugon sa mga pagbabagong ito, tinatampok sa kasalukuyang pag-aaral ang experience sampling method (ESM), isang metodong malaki ang potensiyal ngunit hindi pa nagagamit sa kontekstong Pilipino. Kumpara sa mga tradisyunal na metodo, may kakayahan ang ESM na suriin at pag-aralan ang karanasan ng tao, kabilang na ang kaniyang damdamin, saloobin, at kilos, habang nangyayari ito mismo sa kasalukuyan. Upang higit na mailapit ito sa araw-araw na buhay at gawi ng mga kalahok, marami nang mga smartphone applications o apps na magagamit sa pagsasagawa ng ESM. Sa papel na ito, tinasa ang kaangkupan ng ESM bilang metodong pampananaliksik sa SP sa pamamagitan ng paggamit nito sa pag-aaral ng mga emosyonal na karanasan ng mga Pilipinong kalahok. Batay sa mga obserbasyong nakalap mula sa pag-aaral, masasabing mayroong natatanging kontribusyon ang ESM sa pag-unlad ng SP dahil tugma ito sa layunin ng disiplina at malapit ito sa araw-araw na pamumuhay ng mga Pilipino sa makabagong panahon. Iminumungkahi ang paggamit ng ESM katuwang ng iba pang metodong kasalukuyang tinatanggap sa SP upang higit na mapalalim ang pag-unawa sa karanasang Pilipino.English:For the longest time, the research methods used in Sikolohiyang Pilipino (SP) are those derived from the day-to-day manner of communication among Filipinos. However, more than 40 years since SP was first established, modernity and rapid advancements in technology have greatly changed the way Filipinos interact and communicate with each other. At the same time, scholars have increasingly recognized the importance of indigenization-from-without to further enrich the study of SP, including the adoption of non-indigenous approaches to research. In response to these changes, the current study features the Experience Sampling Method (ESM), which, though currently underutilized in the Filipino context, has great potential in the study of it. Compared to traditional research methods, ESM allows researchers to study people’s experiences, including their emotions, thoughts, and behaviors, as they occur in the present. Furthermore, ESM smartphone applications or apps have been created to facilitate the use of ESM in obtaining a more representative sample of the everyday lives of participants. This paper aims to evaluate the appropriateness of ESM as a research method to be used in the study of SP. To do so, ESM was used to study the emotional experiences of Filipino participants. The observations derived from the study lend support to the unique contribution of ESM to the advancement of SP because it coincides with the goals of the discipline and simulates the day-to-day lives of Filipinos in the modern age. As such, ESM is recommended to be used with other methods currently used in SP to further deepen our understanding of the lives of Filipinos.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

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