The article "Localization and Mapping for Self-Driving Vehicles: A Survey" by Anas Charroud et al. provides a comprehensive review of the current approaches and challenges in localization and mapping for autonomous vehicles. The authors highlight the importance of robust localization and mapping in achieving safe and efficient autonomous driving. They discuss various methods for feature extraction from sensors, including LiDAR and cameras, categorizing them into semantic, non-semantic, and deep learning approaches. The survey also covers methods for building vehicle environment maps, both commercial and academic, and explores different localization techniques, emphasizing their mathematical characteristics and priorities. Additionally, the article addresses security issues in self-driving vehicles and their potential impacts on energy consumption, pollution, and smart city integration. The authors conclude with a discussion on the future directions and challenges in the field, emphasizing the need for effective feature extraction methods that are robust, efficient, and safe.The article "Localization and Mapping for Self-Driving Vehicles: A Survey" by Anas Charroud et al. provides a comprehensive review of the current approaches and challenges in localization and mapping for autonomous vehicles. The authors highlight the importance of robust localization and mapping in achieving safe and efficient autonomous driving. They discuss various methods for feature extraction from sensors, including LiDAR and cameras, categorizing them into semantic, non-semantic, and deep learning approaches. The survey also covers methods for building vehicle environment maps, both commercial and academic, and explores different localization techniques, emphasizing their mathematical characteristics and priorities. Additionally, the article addresses security issues in self-driving vehicles and their potential impacts on energy consumption, pollution, and smart city integration. The authors conclude with a discussion on the future directions and challenges in the field, emphasizing the need for effective feature extraction methods that are robust, efficient, and safe.