1A1-G05 Development of a Driving Simulator to Evaluate User Experience for Advanced Intelligent Vehicles

2015 ◽  
Vol 2015 (0) ◽  
pp. _1A1-G05_1-_1A1-G05_4
Author(s):  
Mitsuhiro KAMEZAKI ◽  
Udara MANAWADU ◽  
Masaaki ISHIKAWA ◽  
Shigeki SUGANO
Keyword(s):  
User Experience ◽  
Driving Simulator ◽  
Intelligent Vehicles

Elicitation of mental states and user experience factors in a driving simulator

2013 ◽  
Author(s):  
A. Hess ◽  
J. Jung ◽  
A. Maier ◽  
R. Taib ◽  
K. Yu ◽  
...  
Keyword(s):  
User Experience ◽  
Driving Simulator ◽  
Mental States

scholarly journals Improving Passenger Experience and Trust in Automated Vehicles Through User-Adaptive HMIs: “The More the Better” Does Not Apply to Everyone

2021 ◽  
Vol 3 ◽  
Author(s):  
Franziska Hartwich ◽  
Cornelia Hollander ◽  
Daniela Johannmeyer ◽  
Josef F. Krems
Keyword(s):  
User Experience ◽  
Driving Simulator ◽  
Individual Characteristics ◽  
User Requirements ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Perceived Safety ◽  
Information Availability ◽  
Related Information ◽  
Expected Benefits

Automated vehicles promise transformational benefits for future mobility systems, but only if they will be used regularly. However, due to the associated loss of control and fundamental change of in-vehicle user experience (shifting from active driver to passive passenger experience), many humans have reservations toward driving automation, which question their sufficient usage and market penetration. These reservations vary based on individual characteristics such as initial attitudes. User-adaptive in-vehicle Human-Machine Interfaces (HMIs) meeting varying user requirements may represent an important component of higher-level automated vehicles providing a pleasant and trustworthy passenger experience despite these barriers. In a driving simulator study, we evaluated the effects of two HMI versions (with permanent vs. context-adaptive information availability) on the passenger experience (perceived safety, understanding of driving behavior, driving comfort, driving enjoyment) and trust in automated vehicles of 50 first-time users with varying initial trust (lower vs. higher trust group). Additionally, we compared the user experience of both HMIs. Presenting driving-related information via HMI during driving improved all assessed aspects of passenger experience and trust. The higher trust group experienced automated driving as safest, most understandable and most comfortable with the context-adaptive HMI, while the lower trust group tended to experience the highest safety, understanding and comfort with the permanent HMI. Both HMIs received positive user experience ratings. The context-adaptive HMI received generally more positive ratings, even though this preference was more pronounced for the higher trust group. The results demonstrate the potential of increasing the system transparency of higher-level automated vehicles through HMIs to enhance users’ passenger experience and trust. They also consolidate previous findings on varying user requirements based on individual characteristics. User group-specific HMI effects on passenger experience support the relevance of user-adaptive HMI concepts addressing varying needs of different users by customizing HMI features, such as information availability. Consequently, providing full information permanently cannot be recommended as a universal standard for HMIs in automated vehicles. These insights represent next steps toward a pleasant and trustworthy passenger experience in higher-level automated vehicles for everyone, and support their market acceptance and thus the realization of their expected benefits for future mobility and society.

scholarly journals The Impact of Situational Complexity and Familiarity on Takeover Quality in Uncritical Highly Automated Driving Scenarios

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 115 ◽  
Author(s):  
Marlene Susanne Lisa Scharfe ◽  
Kathrin Zeeb ◽  
Nele Russwinkel
Keyword(s):  
Driving Simulator ◽  
Intelligent Vehicles ◽  
Automated Driving ◽  
Strong Focus ◽  
Cognitive Assistance ◽  
The Subject ◽  
Perceived Complexity ◽  
Study Participants ◽  
The Impact ◽  
Highly Automated Driving

In the development of highly automated driving systems (L3 and 4), much research has been done on the subject of driver takeover. Strong focus has been placed on the takeover quality. Previous research has shown that one of the main influencing factors is the complexity of a traffic situation that has not been sufficiently addressed so far, as different approaches towards complexity exist. This paper differentiates between the objective complexity and the subjectively perceived complexity. In addition, the familiarity with a takeover situation is examined. Gold et al. show that repetition of takeover scenarios strongly influences the take-over performance. Yet, both complexity and familiarity have not been considered at the same time. Therefore, the aim of the present study is to examine the impact of objective complexity and familiarity on the subjectively perceived complexity and the resulting takeover quality. In a driving simulator study, participants are requested to take over vehicle control in an uncritical situation. Familiarity and objective complexity are varied by the number of surrounding vehicles and scenario repetitions. Subjective complexity is measured using the NASA-TLX; the takeover quality is gathered using the take-over controllability rating (TOC-Rating). The statistical evaluation results show that the parameters significantly influence the takeover quality. This is an important finding for the design of cognitive assistance systems for future highly automated and intelligent vehicles.

scholarly journals $$360^{\circ }$$ real-time and power-efficient 3D DAMOT for autonomous driving applications

2022 ◽  
Author(s):  
Carlos Gómez-Huélamo ◽  
Javier Del Egido ◽  
Luis Miguel Bergasa ◽  
Rafael Barea ◽  
Elena López-Guillén ◽  
...  
Keyword(s):  
Embedded System ◽  
Object Detection ◽  
Real Time ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Intelligent Vehicles ◽  
Evaluation Tool ◽  
Automated Driving ◽  
Safe Driving ◽  
Power Efficient

AbstractAutonomous Driving (AD) promises an efficient, comfortable and safe driving experience. Nevertheless, fatalities involving vehicles equipped with Automated Driving Systems (ADSs) are on the rise, especially those related to the perception module of the vehicle. This paper presents a real-time and power-efficient 3D Multi-Object Detection and Tracking (DAMOT) method proposed for Intelligent Vehicles (IV) applications, allowing the vehicle to track $$360^{\circ }$$ 360 ∘ surrounding objects as a preliminary stage to perform trajectory forecasting to prevent collisions and anticipate the ego-vehicle to future traffic scenarios. First, we present our DAMOT pipeline based on Fast Encoders for object detection and a combination of a 3D Kalman Filter and Hungarian Algorithm, used for state estimation and data association respectively. We extend our previous work ellaborating a preliminary version of sensor fusion based DAMOT, merging the extracted features by a Convolutional Neural Network (CNN) using camera information for long-term re-identification and obstacles retrieved by the 3D object detector. Both pipelines exploit the concepts of lightweight Linux containers using the Docker approach to provide the system with isolation, flexibility and portability, and standard communication in robotics using the Robot Operating System (ROS). Second, both pipelines are validated using the recently proposed KITTI-3DMOT evaluation tool that demonstrates the full strength of 3D localization and tracking of a MOT system. Finally, the most efficient architecture is validated in some interesting traffic scenarios implemented in the CARLA (Car Learning to Act) open-source driving simulator and in our real-world autonomous electric car using the NVIDIA AGX Xavier, an AI embedded system for autonomous machines, studying its performance in a controlled but realistic urban environment with real-time execution (results).

Challenges and Requirements of Immersive Media in Autonomous Car: Exploring the Feasibility of Virtual Entertainment Applications

i-com ◽  
2019 ◽  
Vol 18 (2) ◽  
pp. 105-125
Author(s):  
Carolin Wienrich ◽  
Kristina Schindler
Keyword(s):  
User Experience ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Simulator Sickness ◽  
Significant Difference ◽  
Driving Scenario ◽  
Immersive Media

Abstract This paper investigated the influence of VR-entertainment systems on passenger and entertainment experience in vehicles with smooth movements. To simulate an autonomous driving scenario, a tablet and a mobile VR-HMD were evaluated in a dynamic driving simulator. Passenger, user and entertainment experience were measured through questionnaires based on comfort/discomfort, application perception, presence, and simulator sickness. In two experiments, two film sequences with varying formats (2D versus 3D) were presented. In Experiment 1, the established entertainment system (tablet + 2D) was tested against a possible future one (HMD + 3D). The results indicated a significantly more favorable experience for the VR-HMD application in the dimensions of user experience (UX) and presence, as well as low simulator sickness values. In Experiment 2, the film format was held constant (2D), and only the device (tablet versus HMD) was varied. There was a significant difference in all constructs, which points to a positive reception of the HMD. Additional analyses of the HMD device data for both experiments showed that the device and not the film format contributed to the favorable experience with the HMD. Additionally, the framework to evaluate the new application context of VR as an entertainment system in autonomous vehicles was discussed.

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