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How Well Can Driverless Vehicles Hear? An Introduction to Auditory Perception for Autonomous and Smart Vehicles
- Source :
- Marchegiani, L & Fafoutis, X 2022, ' How Well Can Driverless Vehicles Hear? An Introduction to Auditory Perception for Autonomous and Smart Vehicles ', IEEE Intelligent Transportation Systems Magazine, vol. 14, no. 3, pp. 92-105 . https://doi.org/10.1109/MITS.2021.3049425, Marchegiani, L & Fafoutis, X 2022, ' How Well Can Driverless Vehicles Hear? A Gentle Introduction to Auditory Perception for Autonomous and Smart Vehicles ', IEEE Intelligent Transportation Systems Magazine, vol. 14, no. 3, pp. 92-105 . https://doi.org/10.1109/MITS.2021.3049425
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- From sirens to lane markings, the urban environment is full of sounds that are designed to navigate the attention of the driver towards events that require special care. Microphone-equipped autonomous vehicles can also use these acoustic cues for increasing safety and performance. This article explores auditory perception in the context of autonomous driving and smart vehicles in general, examining the potential of exploiting acoustic cues in driverless vehicle technology. With a journey through the literature, we discuss various applications of auditory perception in driverless vehicles, ranging from the identification and localisation of external acoustic objects to leveraging ego-noise for motion estimation and engine fault detection. In addition to solutions already proposed in the literature, we also point out directions for further investigations, focusing in particular on parallel studies in the areas of acoustics and audio signal processing that demonstrate the potential for improving the performance of driverless cars.
- Subjects :
- Auditory perception
Sensors
Computer science
Mechanical Engineering
Location awareness
Autonomous vehicles
Acoustic Signal Processing
Acoustics
Computer Science Applications
Machine Learning
Human–computer interaction
Autonomous Systems
Automotive Engineering
Hidden Markov models
Automobiles
Neural networks
Subjects
Details
- ISSN :
- 19411197 and 19391390
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- IEEE Intelligent Transportation Systems Magazine
- Accession number :
- edsair.doi.dedup.....5c0a01416ae24bef705d3c0b58efb2c9