Low Illumination Enhancement For Object Detection In SelfDriving * Object detection plays an important role in the field of selfdriving. Illumination has a great impact on object detection, but most of the current methods do not solve the problem of object detection in poor light environment well. We propose a network that can optimize the image conversion which is based on the Cycle Generative Adversarial Networks (CycleGAN). We redesign the discriminator network of CycleGAN, add additional discriminators, optimize multiple parts of the network such as loss functions, and add object detection networks after converting the network. The Robot Car dataset of Oxford University is utilized to verify the effectiveness of the proposed method, and the results proved that our method can significantly improve the detection accuracy and increase the detected object number in low illumination environment.

PrivacyPreserving AutoDriving: A GANBased Approach to Protect Vehicular Camera Data The autonomous driving (autodriving) technology has been promoted significantly by the rapid advances in computer vision and deep neural networks. Autodriving vehicles, nowadays, are fully equipped with numerous sensors such as cameras, geosensors, and radar sensors, to capture realtime data inside the vehicles and outside surroundings. Meanwhile, the captured data contains lots of private information about vehicles, drivers and passengers and thus faces a high risk of privacy breaches. Especially, sidechannel information can be mined from camera data to identify vehiclesu0027 locations and even trajectories, raising serious privacy issues. Unfortunately, the issue, how to resist locationinference attack for camera data in autodriving, has never been addressed in literature. In this paper, we intend to fill this blank by developing a GANbased imagetoimage translation method named AutoDriving GAN (ADGAN). Through performance comparisons between ADGAN and the stateoftheart, the superiority of ADGAN can be validated  offering an effective tradeoff between recognition utility and privacy protection for camera data.