We propose a novel approach for automated vehicle motion planning systems that introduces the likelihood of an information gain at future positions to trajectory optimization. In the same way as human drivers, computer controlled vehicles have to be fully aware of their surroundings and the current driving situation. Even though automated cars have a full 360 degrees field of view through sensor data fusion, objects can be hidden behind other obstacles. We optimize the vehicle's future pose (position and orientation) on the road and within the traffic stream, so that it can perceive as much as possible while fulfilling other constraints related to the overall safety or driving comfort. Our results show that perception benefits from maximizing the entropy in areas of interest (EAI) over field of view (FOV). The computation of an EAI is expensive and achieved by using an optimized algorithm for modern GPGPUs.