Abstract
Intelligent vehicles and connected vehicles have garnered more and more attention recently, and both require accurate positions of the vehicles in their operation, which relies on navigation sensors such as Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), Light Detection And Ranging (LiDAR) and so on. GNSS is the key sensor to obtain high accuracy positions in the navigation system, because GNSS Real Time Kinematic (RTK) with correct ambiguity resolution (AR) can provide centimeter-level absolute position. But AR may fail in the urban occlusion environment because of the limited satellite visibility for single vehicles. The navigation data from multiconnected vehicles can improve the satellite geometry significantly, which is able to help improve the AR, especially in occlusion environment. In this work, the GNSS, INS, and LiDAR data from multiconnected vehicles are jointly processed together to improve the GNSS RTK AR, and to obtain high accuracy positioning results, using a scan-to-map matching algorithm based on an occupancy likelihood map (OLM) for the relative position between the connected vehicles, a Damped Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) method with least-squares for a relative AR between the connected vehicles, and a joint RTK algorithm for solving the absolute positioning for the vehicles by involving the relative position and relative ambiguity constraints. The experimental results show that the proposed approach can improve the AR for the connected vehicles with higher ratio values, success rates, and fixed rates, and achieve high-precision cooperative absolute positions compared with traditional GNSS RTK methods, especially in occlusion environments such as below a viaduct.
Highlights
With the advance of computer science and 5G technology, intelligent transportation systems, autonomous vehicles, and connected vehicles are developing rapidly
It is hard to get continually accurate positions below the viaduct, which makes it difficult to evaluate the performance of our integrated system, so we only analyze the geometrical distribution of satellites above the vehicles’ horizon in the experiments below the viaduct, we choose measurements from the same satellites collected from the open area to simulate the observation condition below the viaduct, and the Real Time Kinematic (RTK) results in the open area are assumed to be the ground truth of the absolute positions of the two vehicles
We proposed a cooperative Global Navigation Satellite System (GNSS)-RTK ambiguity resolution (AR) method by sharing the sensor data between the connected vehicles
Summary
With the advance of computer science and 5G technology, intelligent transportation systems, autonomous vehicles, and connected vehicles are developing rapidly. An essential function of these systems is accurate positioning. Their effectiveness heavily depends on high accuracy navigation results. The positions of the vehicles must be highly reliable to prevent failures of these systems during operation. Integrated navigation technology is commonly used for these systems, which can fully use the complementary characteristics of different navigation subsystems and greatly improve the accuracy and reliability of the integrated navigation system. The integration of the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) is a traditional integrated navigation system
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