scholarly journals AmbientBreath

2021 ◽  
Vol 5 (2) ◽  
pp. 1-30
Author(s):  
Jinmo Lee ◽  
Neska Elhaouij ◽  
Rosalind Picard
Keyword(s):  
Background Noise ◽  
Sensory Feedback ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
User Acceptance ◽  
Driving Safety ◽  
Just In Time ◽  
Breathing Rate ◽  
Safety Measures ◽  
Driving Task

To promote calm breathing inside a car, we designed a just-in-time breathing intervention stimulated by multi-sensory feedback and evaluated its efficacy in a driving simulator. Efficacy was measured via reduction in breathing rate as well as by user acceptance and driving safety measures. Drivers were first exposed to demonstrations of three kinds of ambient feedback designed to stimulate a goal breathing rate: (1) auditory (rhythmic background noise), (2) synchronized modulation of wind (dashboard fans modulating air pointed toward the driver) together with auditory, or (3) synchronized visual (ambient lights) together with auditory. After choosing one preference from these three, each driver engaged in a challenging driving task in a car simulator, where the ambient stimulation was triggered when their breathing exceeded a goal rate adapted to their personal baseline. Two user studies were conducted in a car simulator involving respectively 23 and 31 participants. The studies include both manual and autonomous driving scenarios to evaluate drivers' engagement in the intervention under different cognitive loads. The most frequently selected stimulation was the combined auditory and wind modalities. Measures of changes in breathing rate show that the participants were able to successfully engage in the breathing intervention; however, several factors from the driving context appear to have an impact on when the intervention is or is not effective.

scholarly journals The Effect of Takeover Lead Time on Driver Workload

2019 ◽  
Vol 63 (1) ◽  
pp. 1872-1873
Author(s):  
Yongdeok Yun ◽  
Hyungseok Oh ◽  
Rohae Myung
Keyword(s):  
False Alarm ◽  
Lead Time ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Physiological Signals ◽  
Sufficient Time ◽  
Subjective Workload ◽  
Driver Workload ◽  
Level 3 ◽  
Driving Task

Autonomous driving has been one of the most interesting technologies in recent years with expectation of solving accidents, pollution, and traffic jams (Jo, Lee, & Kim, 2013; Schrank, Eisele, & Lomax, 2012; Singh, 2018). However, current autonomous vehicles cannot handle all driving situations. Therefore, drivers must intervene in certain situations. SAE international defined these levels of autonomous driving as partial (level 2) and conditional (level 3) autonomous driving (SAE international, 2016). In level 3 autonomous driving, drivers are not required to monitor the driving situations and they may perform non-driving related tasks (NDRTs). However, drivers must pay attention to driving situations and make an appropriate reaction when takeover request (TOR) occurs. Takeover request (TOR) is one of the major issues in autonomous driving. A human driver must be ready to transfer the control of the vehicle when TOR is given. Therefore, how and when to request the driver to transfer the control is important. In this regard, takeover lead time (TORlt) has received great attention and there are many existing scholarly works on the effect of TORlt on takeover performance (Gold et al., 2013; Gold et al., 2017; Mok, Johns, Lee, Ive et al., 2015; Mok Johns Lee, Miller et al., 2015; Payre et al., 2016; Van den Beukel & Van der Voort, 2013; Wan & Wu, 2018; Zhang et al., 2018). Besides its impact on takeover performance, TORlt also has an effect on driver workload (Eriksson & Stanton, 2017; Wan & Wu, 2018). Inappropriate TORlt makes driver overload or underload and the abnormal workload deteriorates driver performance in takeover situation (De Winter et al., 2014; Eriksson & Stanton, 2017; Hajek et al., 2013; Wan & Wu, 2018). However, these studies either did not investigate workload induced by TOR or measure driver workload in a subjective method. This study focused on workload induced by TOR. Wan & Wu (2018) stated that takeover request without sufficient time budget may increase driver workload and generate erratic driver's response. However, many researches have focused on workload while performing NDRT alone. Additionally, a few research that assessed workload induced by TOR used subjective methods. The objective of this study is to investigate the effects of TORlt on driver workload in takeover situation. This study hypothesizes that workload would increase when TORlt is too short or too long. To investigate the hypothesis, an experiment was conducted with driving simulator and workload was measured by subjective and objective methods. The experiment with driving simulator was conducted with 28 participants to investigate the effect of TORlt on the driver workload. TORlt was controlled in 7 levels (3s, 7s, 10s, 15s, 30s, 45s, 60s). Each session of the experiment was dealt with one TORlt level and was conducted in random sequence. At the beginning of the session, participants had to perform the NDRT during autonomous driving. Then, they are required to identify an obstacle in ego-lane and make a lane change to avoid the collision when TOR occurs. The dependent variables in this experiment include workload measured by subjective and physiological methods. Driving Activity Load Index (DALI; Pauzie, 2008) was conducted to measure subjective workload and physiological measures including respiration rate (RSP), heart rate (HR), and galvanic skin reponse (GSR) were conducted to evaluate objective workload. The results of this study showed that TORlt had a significant effect on subjective workload. Subjective workload was increased in short TORlt as expected. Drivers, who were given the TOR with short lead time, did not have sufficient time to perceive and comprehend the driving situation nor make an appropriate decision. As a result, drivers could not cope with the takeover situation and their workload increased. However, driver workload was not increased in excessively long TORlt. Long TORlt was expected to increase driver workload because driver could assume long TORlt to be a false alarm or feel distraction (Wan & Wu, 2018). This might be because participants did not consider that 60s of TORlt was long or there was no false alarm in the experiment. There was no significant effect of TORlt on mean RSP and mean HR. This is because each participant behaved differently or regarded driving situation after the takeover as a simple driving task. In contrast to RSP and HR, TORlt had a significant effect on mean GSR. According to Kramer (1991), Physiological signals are sensitive to different resource demands (Kramer, 1991; Ryu & Myung, 2005). In this study, excessive temporal demand because of short TORlt and distraction caused by long TORlt were demands imposed on the participants. Hence, GSR which is sensitive to emotion and frustration (Kramer, 1991) was influenced by TORlt. In conclusion, the results of the study were different from the hypothesis which expected excessive workload with too short or long TORlt. Even though subjective workload and GSR partially support the hypothesis, more complicated and controlled experiment is needed to confirm the hypothesis. In the future research, experiment including complex driving task and false alarm should be conducted and it is necessary to measure physiological signals while controlling various resource demand to investigate the effect of TORlt on physiological signals.

Human-vehicle dynamic model with driver’s neuromuscular characteristic for shared control of autonomous vehicle

2020 ◽  
pp. 095440702097710
Author(s):  
Wei Hanbing ◽  
Wu Yanhong ◽  
Chen Xing ◽  
Xu Jin ◽  
Rahul Sharma
Keyword(s):  
Driving Simulator ◽  
Unscented Kalman Filter ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Least Square ◽  
Shared Control ◽  
Driving Safety ◽  
Dynamic State ◽  
Commercial Application ◽  
Steering Torque

Over a long period of time, the fully autonomous vehicle is far from commercial application. The concept of ‘human-vehicle shared control (HVSC)’ provides a promising solution to enhance autonomous driving safety. In order to characterize the evolution of the driver’s feature in the process of HVSC, a dynamics model of HVSC with the driver’s neuromuscular characteristic is proposed in this paper. It takes into account the driver’s neuromuscular characteristics, such as stretch reflection, feedback stiffness, etc. By designing a model predictive control (MPC) controller, the feedback of the vehicle’s state and steering torque is constructed. For validation of the model, driving simulation has been conducted in our table-based driving simulator. The vehicle state and the surface electromyography of the driver’s arm working muscle group are collected simultaneously. Subsequently, the hierarchical least square (HLS) parameter identification and unscented Kalman filter (UKF) observer is used to identify and estimate the important characteristic parameters respectively based on the experimental results. The comparisons show that the HVSC can characterize the vehicle’s dynamic state and the driver’s personalized characteristic can be identified by HLS. This paper will serve as a theoretical basis of control strategy allocation between the human and vehicle during shared control for L3 class autonomous vehicle.

The Effects of Continuous Driving-Related Feedback on Drivers’ Response to Automation Failures

2017 ◽  
Vol 61 (1) ◽  
pp. 1980-1984 ◽  
Author(s):  
Guy Cohen-Lazry ◽  
Avinoam Borowsky ◽  
Tal Oron-Gilad
Keyword(s):  
Driving Simulator ◽  
Reaction Times ◽  
Autonomous Driving ◽  
The Other ◽  
Control Group ◽  
Driving Situation ◽  
Failure Event ◽  
Level 3 ◽  
Driving Task ◽  
Continuous Feedback

During prolonged periods of autonomous driving, drivers tend to shift their attention away from the driving task. As a result, they require more time to regain awareness of the driving situation and to react to it. This study examined the use of informative automation that during Level-3 autonomous driving provided drivers with continuous feedback regarding the vehicle’s actions and surroundings. It was hypothesized that the operation of informative automation will trigger drivers to allocate more attention to the driving task and will improve their reaction times when resuming control of the vehicle. Sixteen participants drove manual and autonomous driving segments in a driving simulator equipped with Level-3 automation. For half of the participants, the informative automation issued alerts and messages while for the other half no messages were issued (control). The number of on-road glances served as a proxy for drivers’ attention. Drivers’ performance on handling an unexpected automation failure event was measured using their time-to-brake and time-to-steer. Results showed that drivers using the informative automation made more frequent on-road glances than drivers in the control group. Yet, there were no significant differences in reaction times to the automation failure event between the groups. Explanations and implications of these results are discussed.

scholarly journals Eye-head coordination and dynamic visual scanning as indicators of visuo-cognitive demands in driving simulator

2020 ◽  
Author(s):  
Laura Mikula ◽  
Sergio Mejía-Romero ◽  
Romain Chaumillon ◽  
Amigale Patoine ◽  
Eduardo Lugo ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Contact Lenses ◽  
Relevant Information ◽  
Head Movements ◽  
Driving Safety ◽  
Visual Scanning ◽  
Cognitive Demands ◽  
Driving Task ◽  
Scanning Behavior ◽  
Visual Perturbation

AbstractDriving is an everyday task involving a complex interaction between visual and cognitive processes. As such, an increase in the cognitive and/or visual demands can lead to a mental overload which can be detrimental for driving safety. Compiling evidence suggest that eye and head movements are relevant indicators of visuo-cognitive demands and attention allocation. This study aims to investigate the effects of visual degradation on eye-head coordination as well as visual scanning behavior during a highly demanding task in a driving simulator. A total of 21 emmetropic participants (21 to 34 years old) performed dual-task driving in which they were asked to maintain a constant speed on a highway while completing a visual search and detection task on a navigation device. Participants did the experiment with optimal vision and with contact lenses that introduced a visual perturbation (myopic defocus). The results indicate modifications of eye-head coordination and the dynamics of visual scanning in response to the visual perturbation induced. More specifically, the head was more involved in horizontal gaze shifts when the visual needs were not met. Furthermore, the evaluation of visual scanning dynamics, based on time-based entropy which measures the complexity and randomness of scanpaths, revealed that eye and gaze movements became less explorative and more stereotyped when vision was not optimal. These results provide evidence for a reorganization of both eye and head movements in response to increasing visual-cognitive demands during a driving task. Altogether, these findings suggest that eye and head movements can provide relevant information about visuo-cognitive demands associated with complex tasks. Ultimately, eye-head coordination and visual scanning dynamics may be good candidates to estimate drivers’ workload and better characterize risky driving behavior.

scholarly journals Toward a Better Understanding of In-Vehicle Auditory Warnings and Background Noise

2019 ◽  
pp. 001872081987931
Author(s):  
Edin Šabić ◽  
Jing Chen ◽  
Justin A. MacDonald
Keyword(s):  
Background Noise ◽  
Driving Simulator ◽  
Future Research ◽  
Talk Radio ◽  
Text To Speech ◽  
Noise Condition ◽  
Auditory Displays ◽  
Different Types ◽  
Driving Task ◽  
Auditory Warning

Objective: The effectiveness of three types of in-vehicle warnings was assessed in a driving simulator across different noise conditions. Background: Although there has been much research comparing different types of warnings in auditory displays and interfaces, many of these investigations have been conducted in quiet laboratory environments with little to no consideration of background noise. Furthermore, the suitability of some auditory warning types, such as spearcons, as car warnings has not been investigated. Method: Two experiments were conducted to assess the effectiveness of three auditory warnings (spearcons, text-to-speech, auditory icons) with different types of background noise while participants performed a simulated driving task. Results: Our results showed that both the nature of the background noise and the type of auditory warning influenced warning recognition accuracy and reaction time. Spearcons outperformed text-to-speech warnings in relatively quiet environments, such as in the baseline noise condition where no music or talk-radio was played. However, spearcons were not better than text-to-speech warnings with other background noises. Similarly, the effectiveness of auditory icons as warnings fluctuated across background noise, but, overall, auditory icons were the least efficient of the three warning types. Conclusion: Our results supported that background noise can have an idiosyncratic effect on a warning’s effectiveness and illuminated the need for future research into ameliorating the effects of background noise. Application: This research can be applied to better present warnings based on the anticipated auditory environment in which they will be communicated.

scholarly journals Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Weilong Song ◽  
Guangming Xiong ◽  
Huiyan Chen
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Transition Process ◽  
Autonomous Driving ◽  
Driving Safety ◽  
Time Step ◽  
Reward Function ◽  
Pass Through

Autonomous vehicles need to perform social accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. This leads to many difficult decision-making problems, such as deciding a lane change maneuver and generating policies to pass through intersections. In this paper, we propose an intention-aware decision-making algorithm to solve this challenging problem in an uncontrolled intersection scenario. In order to consider uncertain intentions, we first develop a continuous hidden Markov model to predict both the high-level motion intention (e.g., turn right, turn left, and go straight) and the low level interaction intentions (e.g., yield status for related vehicles). Then a partially observable Markov decision process (POMDP) is built to model the general decision-making framework. Due to the difficulty in solving POMDP, we use proper assumptions and approximations to simplify this problem. A human-like policy generation mechanism is used to generate the possible candidates. Human-driven vehicles’ future motion model is proposed to be applied in state transition process and the intention is updated during each prediction time step. The reward function, which considers the driving safety, traffic laws, time efficiency, and so forth, is designed to calculate the optimal policy. Finally, our method is evaluated in simulation with PreScan software and a driving simulator. The experiments show that our method could lead autonomous vehicle to pass through uncontrolled intersections safely and efficiently.

Optic Flow and Tunnel Vision in the Detection Response Task

2019 ◽  
Vol 61 (6) ◽  
pp. 992-1003 ◽  
Author(s):  
Wim van Winsum
Keyword(s):  
Cognitive Load ◽  
Optic Flow ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Tunnel Vision ◽  
Driving Task ◽  
Detection Response ◽  
Response Task ◽  
Stimulus Eccentricity ◽  
Visual Cognitive

Objective:In a driving simulator, a backwards counting task, a simple steering task, and a fully autonomous driving task were applied to study the independent effects of cognitive load, visual-cognitive-manual load, and optic flow on visual detection response task (vDRT) performance. The study was designed to increase the understanding of the processes underlying vDRT effects.Background:The tunnel vision effect induced by a “steering while driving” task found in a previous study was investigated further in this experiment.Method:Stimulus eccentricity and conspicuity were applied as within-subjects factors.Results:Cognitive load, visual-cognitive-manual load, and optic flow all resulted in increased vDRT response time (RT). Cognitive load and visual-cognitive-manual load both increased RT but revealed no interaction of task by stimulus eccentricity. However, optic flow resulted in a task by stimulus eccentricity interaction on vDRT RT that was evidence of a tunnel vision effect.Conclusion:The results suggested that optic flow may be a factor responsible for tunnel vision while driving, although this does not support the tunnel vision model because it is unrelated to workload. However, the results supported the general interference model for cognitive workload.Application:The results have implications for the diagnosticity of the vDRT. During driving tasks, tunnel vision effects may occur as a result of optic flow, and these effects are unrelated to workload.

scholarly journals Eye-head coordination and dynamic visual scanning as indicators of visuo-cognitive demands in driving simulator

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0240201
Author(s):  
Laura Mikula ◽  
Sergio Mejía-Romero ◽  
Romain Chaumillon ◽  
Amigale Patoine ◽  
Eduardo Lugo ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Contact Lenses ◽  
Relevant Information ◽  
Head Movements ◽  
Driving Safety ◽  
Visual Scanning ◽  
Cognitive Demands ◽  
Driving Task ◽  
Scanning Behavior ◽  
Visual Perturbation

Driving is an everyday task involving a complex interaction between visual and cognitive processes. As such, an increase in the cognitive and/or visual demands can lead to a mental overload which can be detrimental for driving safety. Compiling evidence suggest that eye and head movements are relevant indicators of visuo-cognitive demands and attention allocation. This study aims to investigate the effects of visual degradation on eye-head coordination as well as visual scanning behavior during a highly demanding task in a driving simulator. A total of 21 emmetropic participants (21 to 34 years old) performed dual-task driving in which they were asked to maintain a constant speed on a highway while completing a visual search and detection task on a navigation device. Participants did the experiment with optimal vision and with contact lenses that introduced a visual perturbation (myopic defocus). The results indicate modifications of eye-head coordination and the dynamics of visual scanning in response to the visual perturbation induced. More specifically, the head was more involved in horizontal gaze shifts when the visual needs were not met. Furthermore, the evaluation of visual scanning dynamics, based on time-based entropy which measures the complexity and randomness of scanpaths, revealed that eye and gaze movements became less explorative and more stereotyped when vision was not optimal. These results provide evidence for a reorganization of both eye and head movements in response to increasing visual-cognitive demands during a driving task. Altogether, these findings suggest that eye and head movements can provide relevant information about visuo-cognitive demands associated with complex tasks. Ultimately, eye-head coordination and visual scanning dynamics may be good candidates to estimate drivers’ workload and better characterize risky driving behavior.

Cognition and Driving Ability in Isolated and Symptomatic REM Sleep Behavior Disorder

SLEEP ◽  
2021 ◽  
Author(s):  
David J Sandness ◽  
Stuart J McCarter ◽  
Lucas G Dueffert ◽  
Paul W Shepard ◽  
Ashley M Enke ◽  
...  
Keyword(s):  
Rem Sleep ◽  
Processing Speed ◽  
Driving Simulator ◽  
Rem Sleep Behavior Disorder ◽  
Behavior Disorder ◽  
Driving Safety ◽  
Driving Ability ◽  
Safety Measures ◽  
Sleep Behavior ◽  
Stop Sign

Abstract Study Objectives To analyze cognitive deficits leading to unsafe driving in patients with REM Sleep Behavior Disorder (RBD), strongly associated with cognitive impairment and synucleinopathy-related neurodegeneration. Methods Twenty isolated RBD (iRBD), 10 symptomatic RBD (sRBD), and 20 age- and education-matched controls participated in a prospective case-control driving simulation study. Group mean differences were compared with correlations between cognitive and driving-safety measures. Results iRBD and sRBD patients were more cognitively impaired than controls in global neurocognitive functioning, processing speeds, visuospatial attention, and distractibility (p<0.05). sRBD patients drove slower with more collisions than iRBD patients and controls (p<0.05), required more warnings, and had greater difficulty following and matching speed of a lead car during simulated car-following tasks (p<0.05). Driving-safety measures were similar between iRBD patients and controls. Slower psychomotor speed correlated with more off-road accidents (r=0.65) while processing speed (-0.88), executive function (-0.90) and visuospatial impairment (0.74) correlated with safety warnings in sRBD patients. Slower stimulus recognition was associated with more signal-light (0.64) and stop-sign (0.56) infractions in iRBD patients. Conclusions iRBD and sRBD patients have greater selective cognitive impairments than controls, particularly visuospatial abilities and processing speed. sRBD patients exhibited unsafe driving behaviors, associated with processing speed, visuospatial awareness, and attentional impairments. Our results suggest that iRBD patients have similar driving-simulator performance as healthy controls but that driving capabilities regress as RBD progresses to symptomatic RBD with overt signs of cognitive, autonomic, and motor impairment. Longitudinal studies with serial driving simulator evaluations and objective on-road driving performance are needed.

scholarly journals Online Local Path Planning on the Campus Environment for Autonomous Driving Considering Road Constraints and Multiple Obstacles

2021 ◽  
Vol 11 (9) ◽  
pp. 3909
Author(s):  
Changhyeon Park ◽  
Seok-Cheol Kee
Keyword(s):  
Path Planning ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Campus Environment ◽  
Vehicle Localization ◽  
Time Operation ◽  
National University ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Multiple Obstacles

In this paper, an urban-based path planning algorithm that considered multiple obstacles and road constraints in a university campus environment with an autonomous micro electric vehicle (micro-EV) is studied. Typical path planning algorithms, such as A*, particle swarm optimization (PSO), and rapidly exploring random tree* (RRT*), take a single arrival point, resulting in a lane departure situation on the high curved roads. Further, these could not consider urban-constraints to set collision-free obstacles. These problems cause dangerous obstacle collisions. Additionally, for drive stability, real-time operation should be guaranteed. Therefore, an urban-based online path planning algorithm, which is robust in terms of a curved-path with multiple obstacles, is proposed. The algorithm is constructed using two methods, A* and an artificial potential field (APF). To validate and evaluate the performance in a campus environment, autonomous driving systems, such as vehicle localization, object recognition, vehicle control, are implemented in the micro-EV. Moreover, to confirm the algorithm stability in the complex campus environment, hazard scenarios that complex obstacles can cause are constructed. These are implemented in the form of a delivery service using an autonomous driving simulator, which mimics the Chungbuk National University (CBNU) campus.

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