An important problem in computer vision, particularly in object detection, is being able to perceive the objects even under challenging illumination conditions. Being robust to these conditions is especially important in applications such as autonomous driving. Despite the significance of the problem, existing autonomous driving systems use deep object detection networks with low-dynamic range (LDR) images during both the training phase and the testing phase. In this thesis, we investigate whether high-dynamic range (HDR) images can provide better performance for object detection in autonomous driving systems. For this end, we provide a comprehensive analysis of the effect of dynamic range on object detection performance. Moreover, we propose a novel framework to jointly optimize deep-learning-based tone-mapping operators and object detection networks using a Generative Adversarial approach.