Deep Neural-Network (DNN) based Object Detection is one of the most important and time-consuming stages of Autonomous Driving software in cars. In non-critical domains, the performance and energy requirements of object detection can be reduced at the cost of accuracy in the detected objects. This is not the case in a critical domain like automotive, for which a delicate balance between performance/energy overheads and accuracy of object detection must be found. We propose IntPred to achieve such a balance by leveraging on the fact that, with high frame rates, objects do not move significantly across frames. IntPred tailors object interpolation for the case of object detection in autonomous driving frameworks, in line with approaches devised for other domains, thus heavily reducing the performance requirements of full-fledged DNN-based object prediction. IntPred results in comparable accuracy to the original object detection, while saving more than 70% of the computations. The latter allows using lower-performance and cheaper platforms resulting in saving energy and reducing heat dissipation: for instance, in an NVIDIA Jetson TX2 platform, specific for autonomous driving systems, our technique increases the frame processing rate by 4.6x. IntPred also allows consolidating additional applications onto the same platform.