A Modular Approach Towards Autonomous Driving in Sim4CV
We present a novel and modular deep learning based approach towards autonomous driving. By using the deep network for pathway estimation only (thus de-coupling it from the underlying car controls), we show that tasks such as lane change, obstacle avoidance, and guided driving become straightforward and very simple to implement. Furthermore, changes regarding the vehicle or its behaviour can be applied easily on the controller side without changing the learned network. Our approach even works without any need for human-generated or hand-crafted training data (although manually collected data can be included if available), thus avoiding the high cost and tedious nature of manually collecting training data. We demonstrate the effectiveness of our approach by measuring the performance on different diversely arranged environments and maps, showing that it can outperform the capabilities of human drivers significantly.