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A Survey of Autonomous Driving: Common Practices and Emerging Technologies
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Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions: localization, mapping, perception, planning, and human machine interface, were thoroughly reviewed. Furthermore, the state-of-the-art was implemented on our own platform and various algorithms were compared in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.
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Cited by 1 Pith paper
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End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learning
A co-learning matrix over imitation-learning branches, plus classification-style steering losses, raises reach-destination success in unseen CARLA towns by about 62% over the CIL baseline.
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