Understanding Why Engine Noise Feels Loud in Hybrid Vehicles with AI
Graduate School of Science and Engineering
Chuo University
1-13-27 Kasuga
Bunkyo-ku, Tokyo, 112-8551
Japan
Shimpei Nagae
Nissan Motor Co., Ltd.
Kanagawa, Japan
Takeshi Toi
Chuo University
Tokyo, Japan
Popular version of 4aNSa2 – Development of a Machine Learning Model to Predict Engine Noise Perception Considering Regional and Driving Environment Differences
Presented at the 189th ASA Meeting
Read the abstract at https://eppro02.ativ.me//web/index.php?page=Session&project=ASAASJ25&id=3980090
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
When driving a hybrid vehicle, many people notice the moment when the quiet electric drive suddenly switches to the engine — and the engine can feel “loud,” even when the actual sound level is modest. Why does this happen? And does the way drivers perceive this noise differ across countries? In this study, we used machine learning to predict how people judge engine noise annoyance and to uncover insights that may help make future hybrid vehicles more comfortable.
Figure 1. AI Model for Predicting Engine Noise Perception
Video 1. On-Road Driving Example for Data Collection
We conducted on-road evaluations in Japan, the United States, and the United Kingdom. During each test, we simultaneously recorded in-cabin sound, vehicle parameters, and drivers’ ratings of engine noise on a three-level scale (“Not noisy,” “Noisy,” “Very noisy”), creating a dataset for AI training. In Japan, we used the series-hybrid Nissan Note e-POWER. In the U.S., where this model is not sold, we reproduced its engine sound on the Nissan Ariya EV, and in the U.K. we used the Qashqai e-POWER engine sound played on the Ariya. Because vehicles, drivers, and road environments differed across regions, the study provided a stringent test of model generality.
Figure 2. On-Road Evaluation Conditions in Japan, the U.S., and the U.K.
Figure 2. On-Road Evaluation Conditions in Japan, the U.S., and the U.K.First, we tested how accurately AI could predict annoyance using only in-cabin sound data such as loudness and sharpness etc. In Japan, prediction accuracy reached about 57%. When we added three vehicle parameters — engine speed, acceleration torque, and vehicle speed — accuracy increased to 67%, demonstrating that driving conditions, not just sound, play an important role in annoyance perception. The same trend was observed in the U.S. and the U.K.
Figure 3. Prediction Accuracy Improvements Using Vehicle Data and Time History
Figure 3. Prediction Accuracy Improvements Using Vehicle Data and Time HistoryHowever, the relative importance of the three vehicle parameters differed by region. In Japan and the U.S., engine speed contributed most strongly to predictions. In contrast, in the U.K., acceleration torque was the most influential factor. This likely reflects the presence of many roundabouts in the U.K. test route, where frequent acceleration and deceleration lead drivers to value the coherence between engine sound and vehicle motion. This aligns with the author’s own experience living in the U.K. for three years.
Next, we incorporated several seconds of engine-speed history into the vehicle parameters. In all regions, adding this short-term history improved prediction accuracy. Although the optimal history length differed slightly — around 5.5 seconds in Japan and 6.5 seconds in the U.S., — the common finding was clear: people judge engine noise not from a single moment but from the pattern of change over several seconds.
Figure 4 Prediction Improvement When Engine-Speed History Is Added
Figure 4 Prediction Improvement When Engine-Speed History Is AddedDespite differences in vehicles, traffic environments, and evaluation routes, considering “vehicle operating conditions” together with “recent temporal changes” consistently improved the AI’s ability to predict annoyance across all regions. These findings provide valuable clues for designing hybrid vehicles that feel smoother and more comfortable for drivers around the world.
Figure 1: Machine learning identifies crowd behaviors from crowd noise. We helped machine learning models recognize four behaviors: applauding, chanting, cheering, and distracting the other team. Image courtesy of byucougars.com.