Our paper entitled “Autoencoder Based Inter-Vehicle Generalization for In-Cabin Occupant Classification” has been accepted for publication at IEEE Intelligent Vehicles Symposium (IV 2021). We highlight the challenges for generalizing in between different vehicle interiors when a limited amount of variation is available during training (i.e. only a single vehicle and a few class instances). We propose an autoencoder based approach which performs better than classification models trained from scratch. Moreover, the autoencoder approach can transform images from unseen vehicles into the vehicle seen during training.