Driving policy transfer via modularity and abstraction. In this work, we train a driving policy in simulation and then deployed in the physical world without any fine tuning. The vehicle drives based on a segmentation map and high-level commands such as turn left at the next intersection. As a result, our driving policy is not only able to follow the lane, but is also able to take various turns based on input by a high-level plenner or operator. What is more, our driving policy generalizes to various previously unseen environments. We address the problem of transfer via modularity and abstraction, combining the benefits of deep learning and classic modular pipelines for autonomous driving. Our architecture compresses three modules, a perception module, driving policy, and low-level controller. We first validate the system in the Carla simulator. We train the driving policy in Carla, turn one, whether one. As expected, our driving policy learns to drive very well in this setting. Note that the segmentation network was trained on cityscapes and produces noisy segmentation in Carla. This is by design to allow the driving policy to generalize and learn robustness to noise. We test the driving policy in Carla, turn two, whether two. A visually very different setup and show good generalization performance. We then deploy our system on a robotic vehicle in diverse, real-world, urban environments. Our tests include different environments and weather conditions. These include overcast weather with dry roads in Europe. But also winter weather with wet and snowy roads. We also evaluate our model on a different continent in a very different environment in sunny weather. Despite very different textures in the background, very different lighting conditions, and different road width, our driving policy generalizes well. We show that the learned policy is able to take various turns and successfully complete several routes of about one kilometer. Please refer to the paper for further details. While there is certainly room for further improvement, we find the driving performance very impressive, considering that this driving policy was only trained in simulation. We show that the driving policy even generalizes to a completely different time of day and is able to control the vehicle in the evening during low light conditions. Again, the driving policy is able to follow the lane and complete various turns. Please observe and enjoy the sunset. In the future, we plan to add traffic sign and object detection to stop for traffic lights, stop signs, and other vehicles, and avoid dynamic obstruction. We also plan to extend our segmentation to more classes and complement it with other representations such as depth and light hour. Thank you.