利用人工智能的情感识别技术提高车辆安全性

Moyank Giri, Muskan Bansal, Aditya Ramesh, D. Satvik, U. D
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引用次数: 0

摘要

谈到汽车,安全是最需要改进的一个方面。提高道路安全最有效的方法是考虑驾驶员因素,因为已经发现超过90%的道路事故是由于驾驶员的过错。驾驶员的情绪在确保驾驶安全方面起着至关重要的作用。虽然外部参数有助于提高安全性,但除非驾驶员情绪稳定,否则这些外部参数并没有多大帮助。因此,检测驾驶员的情绪并加以增强可以显著提高道路安全。因此,本文涉及到识别和改善驾驶员的情绪稳定性,以大幅提高汽车安全。人工智能(AI)技术帮助自动化和改善了驾驶的许多方面,并创造了一个舒适的乘客体验环境。本文打算利用其中的一些人工智能技术,通过考虑语音和面部表情来检测驾驶员的情绪状态,如快乐、悲伤、愤怒、惊讶、恐惧、厌恶或中性,然后根据检测到的情绪产生警报,包括安全的音频和视觉警报,最后通过音乐推荐系统提供适当的建议来改善驾驶员的情绪状态。本研究利用深度学习模型(即CNN模型)对音频和视频进行情感自动检测,视频情感检测和音频情感检测的验证准确率分别为83%和78%。本文还详细介绍了一种用于结合音频和视频情感的开发算法的使用。此外,本研究使用Spotify API进行音乐推荐系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Safety in Vehicles using Emotion Recognition with Artificial Intelligence
Safety is the singular most important aspect to improve when it comes to automobiles. The most efficient way to improve road safety would be to consider the driver factor since it has been found that over 90% of all road accidents are due to driver's fault. The emotion of the driver plays a crucial role in ensuring safety while driving. While external parameters can help improve safety, these external parameters are not of much help unless the driver is emotionally stable. Thus, detecting the driver's emotion and enhancing it could significantly improve road safety. Hence, this paper involves identifying and improving the emotional stability of drivers in order to drastically raise automobile safety. Artificial Intelligence (AI) technologies have helped automate and improve many aspects of driving, and have created an environment with a comfortable passenger experience. This paper intends to use some of these AI technologies to detect the emotional state of the driver as Happy, Sad, Angry, Surprised, Fear, Disgust or Neutral by considering both speech and facial expressions, and then generate alerts based on the detected emotion which includes audio and visual alerts for safety, and finally improve the driver's emotional state using appropriate suggestions provided by a music recommendation system. This study makes use of deep learning models (i.e., CNN Models) for automatic emotion detection from audio and video where the validation accuracy obtained for video emotion detection and audio emotion detection is 83% and 78% respectively. The paper also details the use of a developed algorithm for combination of the emotions from Audio and Video. Furthermore, this study uses the Spotify API for the music recommendation system.
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