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arxiv: 1906.05113 · v3 · pith:QP7VYJ6R · submitted 2019-06-12 · cs.RO · cs.SY· eess.SY

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:QP7VYJ6Rrecord.jsonopen to challenge →

classification cs.RO cs.SYeess.SY
keywords drivingadssautomatedemergingstate-of-the-artwerealgorithmsarchitectures
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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

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. nuScenes: A multimodal dataset for autonomous driving

    cs.LG 2019-03 accept novelty 8.0

    nuScenes provides the first public autonomous-driving dataset that includes synchronized 360-degree data from cameras, radars, and lidar together with 3D bounding-box annotations across 1000 scenes.