This talk presents our investigation on using Bayer data instead of 3-channel processed RGB images with deep neural network (DNN). The amount of data captured by HDR, high-resolution automotive camera is tremendous. However, is the data format currently used optimal for the intended use case? Camera data can be processed with traditional computer vision methods or DNN algorithms. Often, these algorithms consume 3-colour channel data (Red-Green-Blue), generated from raw/Bayer data captured by the image sensor. By working with Bayer data, it is possible to reduce transmitted amount of data, needed power, and processing time. However, there is currently very little work on optimising DNN for single channel inputs, and there are no complete or fully annotated public Bayer datasets for assisted and automated driving functions. Interestingly, preliminary investigation demonstrates that current DNNs can be used with raw data without a significant performance degradation, and possibly can be further optimised and tuned to achieve even better results. Additionally, different methods to convert (with minimal modifications) existing datasets into different Bayer formats are discussed. In this way, big curated datasets can be re-used for creating Bayer based deep learning models.