Recently, deep neural networks have greatly improved autonomous driving. However, as a great deal of training data is required, most studies have employed simulators. Generalization of such driving is key in terms of safety. The simulated environments feature only small variations in favorable conditions and thus cannot be used for benchmarking. Therefore, we developed a new open-source (OpenAI Gym-like) off-road environment featuring differently structured forests, plateaus, deserts, and snowfields. The dynamic topographical structures make the off-road environment a very challenging generalization problem. Our offroad environment can precisely evaluate autonomous driving in terms of generalization. Additionally, we proposed an evaluation method based on the success rate of driving tasks, enabling effective driving ability measurement. Furthermore, we evaluate the performance of existing end-to-end driving methods in our off-road environment. The results show that the end-to-end driving methods lack generalization ability and fail to generalize to unseen environments. Our off-road environment can help autonomous driving researchers develop a better, generalizable driving system. Unreal engine-level assets and codes are available at github.com a . We briefly introduce our model in https://www.youtube.com/watch?v=YIKd5bcHcGA.

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