A Survey of Vehicle Localization: Performance Analysis and Challenges

Vehicle localization plays a crucial role in ensuring the safe operation of autonomous vehicles and the development of intelligent transportation systems (ITS). However, there is insufficient effort to compare the performance and challenges of different vehicle localization algorithms. This paper aims to address this gap by analyzing the comprehensive performance of existing advanced vehicle localization techniques and discussing their challenges. Firstly, we analyze the self-localization methods based on active and passive sensors. The results show that, the light detection and ranging (LiDAR) and vision-based localization techniques can reach high accuracy. However, they have high computational complexity. Only using the inertial measurement unit (IMU), global positioning system (GPS), radar, and ultrasonic sensors may not realize localization result with high accuracy. Then, we discuss V2X-based cooperative localization methods, analyze the multi-sensor based localization techniques and compare the comprehensive performance among all methods. Although the artificial intelligence (AI) techniques can effectively enhance the efficiency of vision-based localization algorithms, the high computational complexity still should be considered. In addition, since the IMU, GPS, radar, and ultrasonic sensors have good performance in terms of the availability, reliability, scalability, and cost-effectiveness, they can be used as auxiliary sensors to achieve good comprehensive performance through data fusion techniques. Finally, we propose the challenges of different techniques and look forward to future work.