scholarly journals Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2405
Author(s):  
Heung-Gu Lee ◽  
Dong-Hyun Kang ◽  
Deok-Hwan Kim
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Response Speed ◽  
Autonomous Driving ◽  
Human Machine Interface ◽  
Driver Assistance Systems ◽  
Human Machine Interaction ◽  
Driving Assistance ◽  
Assistant Systems ◽  
Machine Interface

Currently, the existing vehicle-centric semi-autonomous driving modules do not consider the driver’s situation and emotions. In an autonomous driving environment, when changing to manual driving, human–machine interface and advanced driver assistance systems (ADAS) are essential to assist vehicle driving. This study proposes a human–machine interface that considers the driver’s situation and emotions to enhance the ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used and applied to control semi-autonomous vehicles. The possibility of semi-autonomous driving is confirmed by classifying four driving scenarios and controlling the speed of the vehicle. In the experiment, by using a driving simulator and hardware-in-the-loop simulation equipment, we confirm that the response speed of the driving assistance system is 351.75 ms and the system recognizes four scenarios and eight emotions through bio-signal data.

scholarly journals Advanced Driver Assistant Systems Focused on Pedestrians’ Safety: A User Experience Approach

2021 ◽  
Vol 13 (8) ◽  
pp. 4264
Author(s):  
Matúš Šucha ◽  
Ralf Risser ◽  
Kristýna Honzíčková
Keyword(s):  
Autonomous Vehicles ◽  
Qualitative Data ◽  
Expert Interviews ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistant Systems ◽  
Vehicle Technology ◽  
Assistance Systems ◽  
Technical Solutions ◽  
Near Future

Globally, pedestrians represent 23% of all road deaths. Many solutions to protect pedestrians are proposed; in this paper, we focus on technical solutions of the ADAS–Advanced Driver Assistance Systems–type. Concerning the interaction between drivers and pedestrians, we want to have a closer look at two aspects: how to protect pedestrians with the help of vehicle technology, and how pedestrians–but also car drivers–perceive and accept such technology. The aim of the present study was to analyze and describe the experiences, needs, and preferences of pedestrians–and drivers–in connection with ADAS, or in other words, how ADAS should work in such a way that it would protect pedestrians and make walking more relaxed. Moreover, we interviewed experts in the field in order to check if, in the near future, the needs and preferences of pedestrians and drivers can be met by new generations of ADAS. A combination of different methods, specifically, an original questionnaire, on-the-spot interviewing, and expert interviews, was used to collect data. The qualitative data was analyzed using qualitative text analysis (clustering and categorization). The questionnaire for drivers was answered by a total of 70 respondents, while a total of 60 pedestrians agreed to complete questionnaires concerning pedestrian safety. Expert interviews (five interviews) were conducted by means of personal interviews, approximately one hour in duration. We conclude that systems to protect pedestrians–to avoid collisions of cars with pedestrians–are considered useful by all groups, though with somewhat different implications. With respect to the features of such systems, the considerations are very heterogeneous, and experimentation is needed in order to develop optimal systems, but a decisive argument put forward by some of the experts is that autonomous vehicles will have to be programmed extremely defensively. Given this argument, we conclude that we will need more discussion concerning typical interaction situations in order to find solutions that allow traffic to work both smoothly and safely.

A motivational driver model for the design of a rear-end crash avoidance system

2019 ◽  
Vol 234 (1) ◽  
pp. 10-26
Author(s):  
Hamed Mozaffari ◽  
Ali Nahvi
Keyword(s):  
Driving Simulator ◽  
Human Subjects ◽  
Autonomous Driving ◽  
Driver Model ◽  
Driver Assistance Systems ◽  
Optimal Point ◽  
Time To Collision ◽  
Human Aspects ◽  
Crash Avoidance ◽  
Assistance Systems

A motivational driver model is developed to design a rear-end crash avoidance system. Current driver assistance systems use engineering methods without considering psychological human aspects, which leads to false activation of assistance systems and complicated control algorithms. The presented driver model estimates driver’s psychological motivations using the combined longitudinal and lateral time to collision, the vehicle kinematics, and the vehicle dynamics. These motivations simplify both autonomous driving algorithms and human-machine interactions. The optimal point of a motivational multi-objective cost function defines the decision for the autonomous driving. Moreover, the motivations are used as risk assessment factors for driver–machine interaction in dangerous situations. The system is evaluated on 10 human subjects in a driving simulator. The assistance system had no false activation during the tests. It avoided collisions in all the rear-end crash avoidance scenarios, while 90% of human subjects did not.

scholarly journals A Personalized Behavior Learning System for Human-Like Longitudinal Speed Control of Autonomous Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3672 ◽  
Author(s):  
Chao Lu ◽  
Jianwei Gong ◽  
Chen Lv ◽  
Xin Chen ◽  
Dongpu Cao ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Speed Control ◽  
Autonomous Driving ◽  
Learning System ◽  
Superior Performance ◽  
Level Control ◽  
Cruise Control ◽  
Behavior Learning ◽  
Main Component

As the main component of an autonomous driving system, the motion planner plays an essential role for safe and efficient driving. However, traditional motion planners cannot make full use of the on-board sensing information and lack the ability to efficiently adapt to different driving scenes and behaviors of different drivers. To overcome this limitation, a personalized behavior learning system (PBLS) is proposed in this paper to improve the performance of the traditional motion planner. This system is based on the neural reinforcement learning (NRL) technique, which can learn from human drivers online based on the on-board sensing information and realize human-like longitudinal speed control (LSC) through the learning from demonstration (LFD) paradigm. Under the LFD framework, the desired speed of human drivers can be learned by PBLS and converted to the low-level control commands by a proportion integration differentiation (PID) controller. Experiments using driving simulator and real driving data show that PBLS can adapt to different drivers by reproducing their driving behaviors for LSC in different scenes. Moreover, through a comparative experiment with the traditional adaptive cruise control (ACC) system, the proposed PBLS demonstrates a superior performance in maintaining driving comfort and smoothness.

scholarly journals Driver Take-Over Reaction in Autonomous Vehicles with Rotatable Seats

Safety ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 34
Author(s):  
Shi Cao ◽  
Pinyan Tang ◽  
Xu Sun
Keyword(s):  
Reaction Time ◽  
Interior Design ◽  
Situation Awareness ◽  
Repeated Measures ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Maximum Magnitude ◽  
Significant Difference ◽  
Driving Conditions

A new concept in the interior design of autonomous vehicles is rotatable or swivelling seats that allow people sitting in the front row to rotate their seats and face backwards. In the current study, we used a take-over request task conducted in a fixed-based driving simulator to compare two conditions, driver front-facing and rear-facing. Thirty-six adult drivers participated in the experiment using a within-subject design with take-over time budget varied. Take-over reaction time, remaining action time, crash, situation awareness and trust in automation were measured. Repeated measures ANOVA and Generalized Linear Mixed Model were conducted to analyze the results. The results showed that the rear-facing configuration led to longer take-over reaction time (on average 1.56 s longer than front-facing, p < 0.001), but it caused drivers to intervene faster after they turned back their seat in comparison to the traditional front-facing configuration. Situation awareness in both front-facing and rear-facing autonomous driving conditions were significantly lower (p < 0.001) than the manual driving condition, but there was no significant difference between the two autonomous driving conditions (p = 1.000). There was no significant difference of automation trust between front-facing and rear-facing conditions (p = 0.166). The current study showed that in a fixed-based simulator representing a conditionally autonomous car, when using the rear-facing driver seat configuration (where participants rotated the seat by themselves), participants had longer take-over reaction time overall due to physical turning, but they intervened faster after they turned back their seat for take-over response in comparison to the traditional front-facing seat configuration. This behavioral change might be at the cost of reduced take-over response quality. Crash rate was not significantly different in the current laboratory study (overall the average rate of crash was 11%). A limitation of the current study is that the driving simulator does not support other measures of take-over request (TOR) quality such as minimal time to collision and maximum magnitude of acceleration. Based on the current study, future studies are needed to further examine the effect of rotatable seat configurations with more detailed analysis of both TOR speed and quality measures as well as in real world driving conditions for better understanding of their safety implications.

scholarly journals Driving Simulator Validation for In-Vehicle Human Machine Interface Assessment

2019 ◽  
Vol 63 (1) ◽  
pp. 2104-2108
Author(s):  
Thomas McWilliams ◽  
Bruce Mehler ◽  
Bobbie Seppelt ◽  
Bryan Reimer
Keyword(s):  
Behavior Analysis ◽  
Real World ◽  
Driving Simulator ◽  
Human Machine Interface ◽  
Production Level ◽  
Driving Simulators ◽  
Relative Validity ◽  
Ongoing Process ◽  
Human Machine Interfaces ◽  
Machine Interface

Driving simulator validation is an important and ongoing process. Advances in in-vehicle human machine interfaces (HMI) mean there is a continuing need to reevaluate the validity of use cases of driving simulators relative to real world driving. Along with this, our tools for evaluating driver demand are evolving, and these approaches and measures must also be considered in evaluating the validity of a driving simulator for particular purposes. We compare driver glance behavior during HMI interactions with a production level multi-modal infotainment system on-road and in a driving simulator. In glance behavior analysis using traditional glance metrics, as well as a contemporary modified AttenD measure, we see evidence for strong relative validity and instances of absolute validity of the simulator compared to on-road driving.

Analysis of Preference for Autonomous Driving Under Different Traffic Conditions Using a Driving Simulator

2015 ◽  
Vol 27 (6) ◽  
pp. 660-670 ◽  
Author(s):  
Udara Eshan Manawadu ◽  
◽  
Masaaki Ishikawa ◽  
Mitsuhiro Kamezaki ◽  
Shigeki Sugano ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Driving Experience ◽  
The Road ◽  
Traffic Conditions ◽  
Passenger Vehicles ◽  
New Type ◽  
On The Road ◽  
Near Future

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/08.jpg"" width=""300"" /> Driving simulator</div>Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the preferences and individual driving experiences of novice and experienced drivers of autonomous and conventional vehicles under different traffic and road conditions. We first developed a simplified driving simulator that could connect to different driver-vehicle interfaces (DVI). We then created virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and we implemented fully autonomous driving. We then conducted experiments to clarify how the autonomous driving experience differed for the two groups. The results showed that experienced drivers opt for conventional driving overall, mainly due to the flexibility and driving pleasure it offers, while novices tend to prefer autonomous driving due to its inherent ease and safety. A further analysis indicated that drivers preferred to use both autonomous and conventional driving methods interchangeably, depending on the road and traffic conditions.

scholarly journals Designing Human-Machine Interface for Autonomous Vehicles

2016 ◽  
Vol 49 (19) ◽  
pp. 609-614 ◽  
Author(s):  
S. Debernard ◽  
C. Chauvin ◽  
R. Pokam ◽  
S. Langlois
Keyword(s):  
Autonomous Vehicles ◽  
Human Machine Interface ◽  
Machine Interface

Distraction of Connected Vehicle Human–Machine Interface for Truck Drivers

2020 ◽  
Vol 2674 (9) ◽  
pp. 438-449 ◽  
Author(s):  
Guangchuan Yang ◽  
Mohamed M. Ahmed ◽  
Biraj Subedi
Keyword(s):  
Human Error ◽  
Driving Simulator ◽  
Situational Awareness ◽  
Revealed Preference ◽  
Human Machine Interface ◽  
Truck Drivers ◽  
Connected Vehicle ◽  
Cognitive Distraction ◽  
High Workload ◽  
Machine Interface

Connected vehicle (CV) technology aims to improve drivers’ situational awareness through audible and visual warnings, commonly displayed on a human–machine interface (HMI), thus reducing the likelihood of crashes caused by human error. Nevertheless, the presence of an in-vehicle CV HMI may pose an increasing threat to driver distraction, particularly for truck drivers and under high workload driving conditions. With this concern, this research investigated the effects of a HMI developed by the Wyoming Department of Transportation CV Pilot on truck drivers’ cognitive distraction and driving behavior through a driving simulator experiment. Revealed preference survey and vehicle dynamics data were employed to assess the cognitive distractions of the Pilot’s HMI. Simulation results indicated that when CV warnings were displayed on the HMI, they did not introduce significant effects on participants’ longitudinal and lateral control of the vehicle. Nevertheless, from the revealed preference survey, it was found that approximately 27% of the participants indicated that the CV HMI tended to introduce additional visual workload for them, particularly when approaching an active freeway work zone under reduced visibility condition. In this regard, this research pointed out that the design of CV warnings and HMI displays needs to incorporate drivers’ ability to recognize and react safely to the received CV warnings to minimize the cognitive distractions introduced by the CV HMI.

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