Exploration and Evaluation of Psychological Burden Factors Experienced by Vehicle Occupants in Traffic Environments
-A Data-Driven Approach Using Natural Language Processing-

Nichika Asai (Salesforce Japan)･Ritsuko Iwai (R-IH, RIKEN)･Takatsune Kumada (Kyoto University)

In recent years, autonomous driving technology has rapidly advanced; however, the subjective comfort of vehicle occupants has not yet been sufficiently investigated. Therefore, this study explored traffic environments that induce psychological burden on vehicle occupants, proposed an automatic questionnaire generation method using natural language processing and large language models, and examined the relationship between the constructed psychological burden scale and occupants' personality traits.

Effects of Foreground Visual Stimuli on Ride Comfort Evaluation in Autonomous Vehicles

Yuya Satsu･Junya Tatsuno (Kindai University)･Kazuma Ishimatsu (Jikei University)･Setsuo Maeda (Nottingham Trent University)

This paper aims to develop guidelines for the interior design of fully autonomous vehicles, as such vehicles do not require 360-degree windows and therefore allow for the introduction of new interior concepts. To investigate the effects of presenting visual information different from the surrounding scenery on ride comfort evaluation, driving simulator experiments were conducted using three displays. During the experiment, 20 participants evaluated the discomfort caused by whole-body vibration while passing through a vibration section on the test course. The results suggest that differences in foreground visual stimuli may affect ride comfort evaluation even under identical vibration conditions.

Objective Evaluation Method for In-Cabin Sound Quality in Electric Vehicles (Second Report)
-Sound Quality Evaluation Method for In-Cabin Sound-

Kenya Fujii･Kenji Torii (Honda R&D)

The authors have been investigating methods for quantifying the in-cabin sound quality of electric vehicles. In the previous report, sound quality evaluation indices for in-cabin electric powertrain noise were developed. Based on this work, a new subjective evaluation experiment targeting overall in-cabin sound, rather than only electric powertrain noise, was conducted. This report presents the results and clarifies the important sound quality factors required for comprehensive enhancement of in-cabin sound quality in electric vehicles.

Objective Evaluation Method for Interior Sound Quality in Electric Vehicles (Third Report)
-Preliminary Validation of Interior Sound Quality Indices and Proposal of a Sound Quality Map-

Kenji Torii･Kenya Fujii (Honda R&D)

The authors have previously shown that reducing loudness and tonal noise originating from the electric powertrain is important for improving the in-cabin sound quality of electric vehicles, and corresponding objective evaluation indices were developed. In this report, the validity of these indices is verified through subjective evaluation experiments conducted by experts. In addition, a sound quality map for visualizing sound quality differences among vehicles is proposed.

Analysis of the Effect of Driving Workload on an Engine Noise Perception Prediction Model Using Time History of Vehicle Parameters

Shinichi Suganuma (Chuo University)･Shimpei Nagae (Nissan Motor)･Takeshi Toi (Chuo University)

In a previous study using on-road driving data, the highest accuracy was obtained when a 5.5-second time history of engine speed was added to an engine noise perception prediction model that used vehicle parameters as explanatory variables. To clarify how variations in driving workload influence the time-history effect, the present study used a driving simulator in which driving workload was changed by varying driving environments and driving conditions. The resulting variation in the time-history effect was examined to analyze the cognitive structure of engine noise perception.

Contribution to improving drivability through the development of a quantitative system for NV sensory evaluation

Hiroyuki Hiroyuki Kimoto Kimoto･Shunsuke Rikitake･Masao Yano･Yuta Yamakita･Kunihiro Nobuhara･Hitoshi Yoshimura (Toyota Motor)

While NV analysis and evaluation are crucial for improving stability, quietness, and feel, it has been difficult to assess the sensory that do not rely on skilled workers. we present our method for assessing the effect of static electricity elimination on improving drivability and feel using our data analysis system (WAVEBASE).