A survey of autonomous driving: common practices and emerging technologies 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 is 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 including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development. The paper provides a structured and comprehensive overview of state-of-the-art automated driving related hardware-software practices. Moreover, emerging trends such as end-to-end driving and connected systems are discussed in detail. The paper also presents a detailed summary of available datasets, software stacks, and simulation tools. Another contribution of this paper is the detailed comparison and analysis of alternative approaches through implementation. We implemented some state-of-the-art algorithms in our platform using open-source software. Comparison of existing overview papers and our work is shown in Table 1. The paper is written in eight sections. Section II is an overview of present challenges. Details of automated driving system components and architectures are given in Section III. Section IV presents a summary of state-of-the-art localization techniques followed by Section V, an in-depth review of perception models. Assessment of the driving situation and planning are discussed in Section VI and VII respectively. In Section VIII, current trends and shortcomings of human machine interface are introduced. Datasets and available tools for developing automated driving systems are given in Section IX. The paper discusses the challenges and prospects of automated driving, including the social impact, technical challenges, system components and architecture, sensors and hardware, localization and mapping, perception, and other related topics. It also discusses the current state of automated driving, including the levels of automation, the challenges of urban driving, and the potential of connected systems. The paper concludes with a discussion of the future of automated driving and the need for further research and development.A survey of autonomous driving: common practices and emerging technologies 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 is 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 including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development. The paper provides a structured and comprehensive overview of state-of-the-art automated driving related hardware-software practices. Moreover, emerging trends such as end-to-end driving and connected systems are discussed in detail. The paper also presents a detailed summary of available datasets, software stacks, and simulation tools. Another contribution of this paper is the detailed comparison and analysis of alternative approaches through implementation. We implemented some state-of-the-art algorithms in our platform using open-source software. Comparison of existing overview papers and our work is shown in Table 1. The paper is written in eight sections. Section II is an overview of present challenges. Details of automated driving system components and architectures are given in Section III. Section IV presents a summary of state-of-the-art localization techniques followed by Section V, an in-depth review of perception models. Assessment of the driving situation and planning are discussed in Section VI and VII respectively. In Section VIII, current trends and shortcomings of human machine interface are introduced. Datasets and available tools for developing automated driving systems are given in Section IX. The paper discusses the challenges and prospects of automated driving, including the social impact, technical challenges, system components and architecture, sensors and hardware, localization and mapping, perception, and other related topics. It also discusses the current state of automated driving, including the levels of automation, the challenges of urban driving, and the potential of connected systems. The paper concludes with a discussion of the future of automated driving and the need for further research and development.