A New Open-Source Off-Road Environment for Benchmark Generalization of Autonomous Driving

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 condi...

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Bibliographic Details
Published inIEEE access Vol. 9; pp. 136071 - 136082
Main Authors Han, Isaac, Park, Dong-Hyeok, Kim, Kyung-Joong
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
autonomous driving
Autonomous vehicles
Benchmark testing
Benchmarks
Generalization
imitation learning
off-road environments
Open source software
Performance evaluation
Plateaus
reinforcement learning
Roads
Simulators
Training
Urban areas
Visualization
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Summary: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 off-road 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 https://github.com/lssac7778/Off-road-Benchmark . We briefly introduce our model in https://www.youtube.com/watch?v=SERSv0TFUwQ&t=44s .
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3116710