Autonomous vehicles integrate complex software stacks for realizing the necessary iterative perception, planning, and action operations. One of the foundational layers of such stacks is the perception one which is comprised of localization, detection, and recognition algorithms for understanding the location and the driving environment around the vehicle. More precisely, localization aims to identify the location of the vehicle on a global coordinate system and is considered one of the most critical parts in the stack since its accuracy and robustness affects the subsequent algorithms of the perception layer and also the following planning and action layers. Due to the rapid and significant interest in self-driving cars, several localization techniques have been proposed with different directions and approaches. Algorithms using prior maps are currently considered the most accurate ones and found almost in all current self-driving car prototypes. Thus, in this paper, we categorize, discuss and analyze the state-of-the-art map-based localization techniques in an attempt to examine their potentials and limitations. We first present techniques and approaches that aim to match prior maps with on-board observations from different sensor modalities. We then review methods that handle the localization problem as a probabilistic one and finally, we also go through the emerging domain of deep-learning localization algorithms and examine their potential in self-driving cars. For all three categories, we provide comparison tables and necessary insights for the optimal localization system design based on different requirements, specifications, and sensor configurations. 

A Survey on Map-Based Localization Techniques for Autonomous Vehicles

Autonomous vehicles integrate complex software stacks for realizing the necessary iterative perception, planning, and action operations. One of the foundational layers of such stacks is the perception one which is comprised of localization, detection, and recognition algorithms for understanding the location and the driving environment around the vehicle. More precisely, localization aims to identify the location of the vehicle on a global coordinate system and is considered one of the most critical parts in the stack since its accuracy and robustness affects the subsequent algorithms of the perception layer and also the following planning and action layers. Due to the rapid and significant interest in self-driving cars, several localization techniques have been proposed with different directions and approaches. Algorithms using prior maps are currently considered the most accurate ones and found almost in all current self-driving car prototypes. Thus, in this paper, we categorize, discuss and analyze the state-of-the-art map-based localization techniques in an attempt to examine their potentials and limitations. We first present techniques and approaches that aim to match prior maps with on-board observations from different sensor modalities. We then review methods that handle the localization problem as a probabilistic one and finally, we also go through the emerging domain of deep-learning localization algorithms and examine their potential in self-driving cars. For all three categories, we provide comparison tables and necessary insights for the optimal localization system design based on different requirements, specifications, and sensor configurations.

Discovering latent topics and trends in autonomous vehicle-related research: A structural topic modelling approach

—Since 2021, the term “Metaverse” has been the most popular one, garnering a lot of interest. Because of its contained environment and built-in computing and networking capabilities, a modern car makes an intriguing location to host its own little metaverse. Additionally, the travellers don’t have much to do to pass the time while traveling, making them ideal customers for immersive services. Vetaverse (Vehicular- Metaverse), which we deﬁne as the future continuum between vehicular industries and Metaverse, is envisioned as a blended immersive realm that scales up to cities and countries, as digital twins of the intelligent Transportation Systems, referred to as “TS-Metaverse”, as well as customized XR services inside each Individual Vehicle, referred to as “IV-Metaverse”. The two sub- categories serve fundamentally different purposes, namely long-term interconnection, maintenance, monitoring, and management on scale for large transportation systems (TS), and personalized, private, and immersive infotainment services (IV). By outlining the framework of Vetaverse and examining important enabler technologies, we reveal this impending trend. Additionally, we examine unresolved issues and potential routes for future study while highlighting some intriguing Vetaverse services.

Vetaverse: Technologies, Applications, and Visions toward the Intersection of Metaverse, Vehicles, and Transportation Systems

The rapid development and deployment of autonomous vehicles (AVs) present unprecedented opportunities and challenges in the transportation sector. While AVs promise enhanced safety, efficiency, and convenience, they also introduce significant cybersecurity vulnerabilities due to their reliance on advanced electronics, connectivity, and artificial intelligence (AI). This review examines the current state of cybersecurity in autonomous vehicles, identifying major threats such as remote hacking, sensor manipulation, data breaches, and denial of service (DoS) attacks. It also explores existing countermeasures including intrusion detection systems (IDSs), encryption, over-the-air (OTA) updates, and authentication protocols. Despite these efforts, numerous challenges remain, including the complexity of AV systems, lack of standardization, latency issues, and resource constraints. This review concludes by highlighting future directions in cybersecurity research and development, emphasizing the potential of AI and machine learning, blockchain technology, industry collaboration, and legislative measures to enhance the security of autonomous vehicles. 

Cybersecurity in Autonomous Vehicles—Are We Ready for the Challenge?

Real-time, accurate, and robust localisation is critical for autonomous vehicles (AVs) to achieve safe, efficient driving, whilst real-time performance is essential for AVs to achieve their current position in time for decision making. To date, no review paper has quantitatively compared the real-time performance between different localisation techniques based on various hardware platforms and programming languages and analysed the relations among localisation methodologies, real-time performance and accuracy. Therefore, this paper discusses the state-of-the-art localisation techniques and analyses their overall performance in AV application. For further analysis, this paper firstly proposes a localisation algorithm operations capability (LAOC)-based equivalent comparison method to compare the relative computational complexity of different localisation techniques; then, it comprehensively discusses the relations among methodologies, computational complexity, and accuracy. Analysis results show that the computational complexity of localisation approaches differs by a maximum of about 10⁷ times, whilst accuracy varies by about 100 times. Vision- and data fusion-based localisation techniques have about 2-5 times potential for improving accuracy compared with lidar-based localisation. Lidar- and vision-based localisation can reduce computational complexity by improving image registration method efficiency. Data fusion-based localisation can achieve better real-time performance compared with lidar- and vision-based localisation because each standalone sensor does not need to develop a complex algorithm to achieve its best localisation potential. Vehicle-to-everything (V2X) technology can improve positioning robustness. Finally, the potential solutions and future orientations of AVs' localisation based on the quantitative comparison results are discussed.

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Real-time performance-focused on localisation techniques for autonomous vehicle: a review

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Real-time performance-focused on localisation techniques for autonomous vehicle: a review