A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i>

Ekim Yurtsever; Jacob Lambert; Alexander Carballo; Kazuya Takeda

doi:10.1109/access.2020.2983149

Abstract

Automated driving systems (ADSs) promise a safe, comfortable and efficient\ndriving experience. However, fatalities involving vehicles equipped with ADSs\nare on the rise. The full potential of ADSs cannot be realized unless the\nrobustness of state-of-the-art improved further. This paper discusses unsolved\nproblems and surveys the technical aspect of automated driving. Studies\nregarding present challenges, high-level system architectures, emerging\nmethodologies and core functions: localization, mapping, perception, planning,\nand human machine interface, were thoroughly reviewed. Furthermore, the\nstate-of-the-art was implemented on our own platform and various algorithms\nwere compared in a real-world driving setting. The paper concludes with an\noverview of available datasets and tools for ADS development.\n

Keywords

Robustness (evolution)Computer sciencePerceptionEmerging technologiesData scienceSystems engineeringHuman–computer interactionArtificial intelligenceEngineering

Affiliated Institutions

Nagoya University JP

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Publication Info

Year: 2020
Type: article
Volume: 8
Pages: 58443-58469
Citations: 1536
Access: Closed

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APA Style

                            
                                    Ekim Yurtsever, 
                                
                                    Jacob Lambert, 
                                
                                    Alexander Carballo
                                
                                et al.
                            
                            (2020). 
                            A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i>. 
                            IEEE Access
                            , 8
                            
                            , 58443-58469.
                            https://doi.org/10.1109/access.2020.2983149

Identifiers

DOI: 10.1109/access.2020.2983149
arXiv: 1906.05113

Data Quality

Data completeness: 84%