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Paper ID:IJSREM67779

Design of an Indian Urban Noise Adaptive Acoustic Vehicle Alerting System Using ESP32-S3 and PC-Based Speed Simulation

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67779

Volume:

10 |

10 |

Oct 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PHG

Pravin Haridasrao Gulhane

Electronics

Siddhant College of Engineering, Sudhambare

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PSD

Prof. Swati Deshmukh

Electronics

Siddhant College of Engineering, Sudhambare

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DAB

Dr. Ashwini Bade

Electronics

Siddhant College of Engineering, Sudhambare

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DPP

Dr. Prabhat Pallav

Electronics

Siddhant College of Engineering, Sudhambare

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Abstract

Low-speed electric vehicles can be difficult to detect acoustically, especially in Indian urban traffic where horns, engines, roadside activity, and construction create rapidly varying spectral masking. This paper presents a low-cost adaptive Acoustic Vehicle Alerting System prototype in which an ESP32-S3 analyses ambient sound and selects a bounded warning profile according to the dominant noise bands. An INMP441 digital microphone supplies 16 kHz audio for windowed 1024-point fast Fourier transform processing, while a MAX98357A amplifier drives the warning loudspeaker. For laboratory development, vehicle speed, direction, and enable state are generated by a Python web application and transmitted through USB serial instead of a vehicle Controller Area Network. The proposed partition keeps all time-critical sensing, adaptation, and audio generation on the embedded controller while using the computer only as a repeatable test instrument. Feasibility analysis shows that the selected interfaces and processing sequence fit the ESP32-S3 platform and support controlled evaluation of latency, switching stability, relative sound level, and pedestrian detectability. The architecture is intended for academic prototyping and is not a regulatory compliance claim.

KEYWORDS

acoustic vehicle alerting system electric vehicle ESP32-S3 fast Fourier transform adaptive audio USB serial urban noise.

Cite This Article

Pravin Haridasrao Gulhane, Prof. Swati Deshmukh, Dr. Ashwini Bade, Dr. Prabhat Pallav, "Design of an Indian Urban Noise Adaptive Acoustic Vehicle Alerting System Using ESP32-S3 and PC-Based Speed Simulation," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-4, Oct 2026.

Full Article/Conclusion

An Indian urban noise adaptive AVAS is feasible as a college prototype using an ESP32-S3, an I2S microphone, a digital audio amplifier, and a PC-based speed simulator. The proposed design performs real-time spectral analysis and bounded profile selection locally on the embedded controller, while USB serial provides a simple and repeatable substitute for vehicle CAN data during development. The architecture supports measurable experiments in latency, stability, spectral response, and relative detectability without overstating regulatory readiness. Its main engineering value is the disciplined combination of embedded DSP, controlled adaptation, and traceable test instrumentation.

References

[1] United Nations Economic Commission for Europe, “UN Regulation No. 138: Quiet Road Transport Vehicles,” WP.29 Vehicle Regulations, Rev. 1 and subsequent amendments. Available: https://unece.org/transport/vehicle-regulations-wp29/standards/addenda-1958-agreement-regulations-121-140 [2] National Highway Traffic Safety Administration, “TP-141-01 Minimum Sound Requirements for Hybrid and Electric Vehicles,” U.S. Department of Transportation, 2020. Available: https://www.nhtsa.gov/sites/nhtsa.gov/files/documents/tp-141-01_minimum_sound_requirements_for_hybrid_and_electric_vehicles.pdf [3] T. S. Berge and F. Haukland, “Adaptive acoustic vehicle alerting sound, AVAS, for electric vehicles: Results from field testing,” SINTEF Digital, Report 2019:00062, 2019. [4] E. Sabic, D. Henning, and J. MacDonald, “Adaptive Auditory Alerts for Smart In-Vehicle Interfaces,” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 63, no. 1, 2019, doi: 10.1177/1071181319631404. [5] Espressif Systems, “Inter-IC Sound (I2S) - ESP32-S3,” ESP-IDF Programming Guide. Available: https://docs.espressif.com/projects/esp-idf/en/v5.0.2/esp32s3/api-reference/peripherals/i2s.html [6] Espressif Systems, “ESP-DSP Library and Examples,” documentation. Available: https://docs.espressif.com/projects/esp-dsp/en/latest/esp32/index.html [7] Analog Devices, “MAX98357A PCM Input Class D Audio Power Amplifier,” product documentation. Available: https://www.analog.com/en/products/max98357a.html

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Publication Date

10/12/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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