Towards affect-aware vehicles for increasing safety and comfort: recognising driver emotions from audio recordings in a realistic driving study - Requardt, Alicia F; Ihme, Klas; Wilbrink, Marc; Wendemuth, Andreas (IET intelligent transport systems, 2020)
Keywords: 4‐emotion‐class detection problem; adaptive automotive safety applications; affect‐aware vehicles; aggressive driving behaviours; automatic analysis; chance level; driver emotion recognition; driver information systems; driver in‐time monitoring; emotion recognition; emotional driver states; fatal road accidents; feature extraction; feature selection; feature sets; hyper‐parameter optimisation; in‐car assistance; learning (artificial intelligence); low‐expressed emotions; machine learning algorithms; optimal feature set; optimisation; random forests; realistic driving study; real‐world situations; Research Article; road accidents; road safety; small random forests; speech data; speech feature selection; standard emobase feature set; subject data identification; vehicle safety; emotional driver states; random forests; vehicle safety; road safety; emotion recognition; audio recordings; aggressive driving behaviours; speech data; in-car assistance; optimisation; road accidents; standard emobase feature set; feature extraction; driver information systems; automatic analysis; learning (artificial intelligence); real-world situations; machine learning algorithms; 4-emotion-class detection problem; feature selection; subject data identification; low-expressed emotions; feature sets; speech feature selection; small random forests; chance level; fatal road accidents; affect-aware vehicles; driver in-time monitoring; hyper-parameter optimisation; adaptive automotive safety applications; driver emotion recognition; optimal feature set; audio recording; realistic driving study