Real-Time Performance-Focused Localization Techniques for Autonomous Vehicle: A Review

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 realtime 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 realtime 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.