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The degree of autonomy in vehicles depends directly on the performance of their sensor systems. The transition to even more autonomously driven cars therefore requires the development of robust sensor systems with different skills. Especially in adverse and changing weather conditions (rain, snow, fog, etc.), conventional sensor systems such as cameras perform unreliably. Moreover, data evaluation has to be performed in real-time, i.e. within a fraction of seconds, in order to safely guide the car through traffic and to avoid a crash with any obstacle. Therefore, we propose to use a so called timegated-single-pixel-camera, which combines the principles of time gating and compressed sensing [1]. In a single pixel camera, the amount of recorded data can be significantly reduced compared to a conventional camera by exploiting the inherent sparsity of scenes. The lateral information is gained with the help of binary masks in front of a simple photodiode. We optimize the pattern of the masks by including them as trainable parameters within our data evaluation neural network. Additionally, our camera is able to cope with adverse weather conditions due to the underlying time gating principle. The feasibility of our method is demonstrated by simulated and measured data as well.