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In recent decades, many learning-based autonomous driving systems have been proposed, and researchers have also created toolkits for developing these systems. These toolkits allow developers to train their models easily, and then test them using simulators. Existing toolkits for learning-based autonomous driving systems have some limitations however, which include inability to reuse modules or to perform accurate comparisons with model-based systems, as well as a lack of support for middle-to-middle models. As a solution, in this paper we introduce Deepware, an end-to-end toolkit for developing and evaluating learning-based autonomous driving models. Deepware includes the tools needed for collecting and evaluating datasets, training models, and evaluating models on simulators or in real-world environments using actual vehicles. Unlike existing toolkits, we used ROS as our platform, which is a set of software frameworks for robot software development widely used in autonomous driving systems as middleware, which allows cooperation with model-based systems. This approach also allows system modules to be shared when building models. In addition, it allows the comparison of learning-based and model-based methods under the same conditions. Moreover, by extracting features from model-based systems, our toolkit can also support middle-to-middle models. The proposed Deepware toolkit and dataset are available at: https://github.com/shunchan0677/deepware. |
