GNSS/INS/LiDAR-based Multi-Sensor Fusion (MSF) systems facilitate the efficient integration of multiple navigation sensors to deliver stable and dependable positioning outcomes in Autonomous Vehicles (AVs). Generally, AVs are bound to operate in diverse scenarios. However, state-of-the-art spoofing attack algorithms can identify vulnerable periods of MSF systems and execute aggressive GNSS spoofing in certain specific scenarios with high success rates. The present study proposes a security defense algorithm for MSF systems based on an enhanced Kalman filter, incorporating the Measurement Variance Monitoring (MVM) and Minimum Constraint of Covariance Matrix (MCCM) model. MVM effectively constrains the impact of GNSS signals with large positioning errors, thereby mitigating the adverse effects caused by spoofing attacks. MCCM fully leverages LiDAR information between two consecutive GNSS signals to enhance the credibility of LiDAR measurements, thus improving the positioning correction capability. Furthermore, considering the diverse application environments of AVs, this paper primarily focuses on sensor uncertainty variation and validates different models in varying scenarios. Ultimately, real-world data demonstrate the effectiveness of the proposed anti-spoofing model, which can significantly mitigate positioning errors caused by GNSS spoofing attacks without affecting navigation accuracy.

Global Navigation Satellite System Real-time Kinematic (GNSS-RTK) is an indispensable source for the absolute positioning of autonomous systems. Unfortunately, the performance of the GNSS-RTK is significantly degraded in urban canyons, due to the notorious multipath and Non-Line-of-Sight (NLOS). On the contrary, LiDAR/inertial odometry (LIO) can provide locally accurate pose estimation in structured urban scenarios but is subjected to drift over time. Considering their complementarities, GNSS-RTK, adaptively integrated with LIO was proposed in this paper, aiming to realize continuous and accurate global positioning for autonomous systems in urban scenarios. As one of the main contributions, this paper proposes to identify the quality of the GNSS-RTK solution based on the point cloud map incrementally generated by LIO. A smaller mean elevation angle mask of the surrounding point cloud indicates a relatively open area thus the correspondent GNSS-RTK would be reliable. Global factor graph optimization is performed to fuse reliable GNSS-RTK and LIO. Evaluations are performed on datasets collected in typical urban canyons of Hong Kong. With the help of the proposed GNSS-RTK selection strategy, the performance of the GNSS-RTK/LIO integration was significantly improved with the absolute translation error reduced by more than 50%, compared with the conventional integration method where all the GNSS-RTK solutions are used.

Accurate positioning and mapping are significant for autonomous systems with navigation requirements. In this paper, a coarse-to-fine loosely-coupled (LC) LiDAR-inertial odometry (LC-LIO) that could explore the complementariness of LiDAR and inertial measurement unit (IMU) was proposed for the real-time and accurate pose estimation of a ground vehicle in urban environments. Different from the existing tightly-coupled (TC) LiDAR-inertial fusion schemes which directly use all the considered ranges and inertial measurements to optimize the vehicle pose, the method proposed in this paper performs loosely-couped integrated optimization with the high-frequency motion prediction, which was produced by IMU integration based on optimized results, employed as the initial guess of LiDAR odometry to approach the optimality of LiDAR scan-to-map registration. As one of the prominent contributions, thorough studies were conducted on the performance upper bound of the TC LiDAR-inertial fusion schemes and LC ones, respectively. Furthermore, the experimental verification was performed on the proposition that the proposed pipeline can fully relax the potential of the LiDAR measurements (centimeter-level ranging accuracy) in a coarse-to-fine way without being disturbed by the unexpected IMU bias. Moreover, an adaptive covariance estimation method employed during LC optimization was proposed to explain the uncertainty of LiDAR scan-to-map registration in dynamic scenarios. Furthermore, the effectiveness of the proposed system was validated on challenging real-world datasets. Meanwhile, the process that the proposed pipelines realized the coarse-to-fine LiDAR scan-to-map registration was presented in detail. Comparing with the existing state-of-the-art TC-LIO, the focus of this study would be placed on that the proposed LC-LIO work could achieve similar or better accuracy with a reduced computational expense.

Hostile spoofing attacks on the Global Navigation Satellite System (GNSS) receiver increase the risk of catastrophic consequences to autonomous driving systems. This paper addresses the problem of vulnerability of Kalman filter (KF) under spoofing attack. A state-of-the-art spoofing attack method based on maximizing the lateral deviation is utilized for verification and analysis. To analyze the vulnerability in actual road scenarios better, an analytic error model of the mechanism of GNSS spoofing is derived. Except for the uncertainty of the initial MSF state, the uncertainty of the uncertainty of Light Detection And Ranging (LiDAR) and the uncertainty of GNSS, a new factor in spoofing attack, the update frequency of different sensors, is investigated in the paper, which in fact is a key factor to increase the immunity Multi-Sensors Fusion (MSF) systems. Experiments were performed in a typical urban scenario of the KAIST dataset. When the update frequency ratio between GNSS and LiDAR is 1, 2, 5, and 10, successful spoof attacks can be performed if the Standard Deviation (STD) of GNSS is smaller than 4m, 2.7m, 1.1m, and 0.7m respectively. Therefore, experiments confirm that the uncertainty of GNSS and the update frequency ratio between LiDAR and GNSS are critical for spoofing attacks, which provides an indication for designing a defense strategy in the future.

The inherent vulnerability of the Global Navigation Satellite System (GNSS) leads to the ease of implementation of spoofing attacks. The latest GNSS spoofing attack schemes still suffer from low success rate, long attack time, and poor concealment. To improve the success rate, an efficient GNSS spoofing attack method for a vehicle-mounted Multi Sensors Fusion (MSF) system is proposed based on the scenario classification models with a spatial database. Firstly, the influence of the two typical urban scenarios, which are 1) the road with buildings on both sides and 2) tunnels, on the GNSS spoofing attack is analyzed. Then a dynamic Bayesian network model considering the sky visibility generated with the 3D building models and tunnel models inside the spatial database is established to quantify the difficulty of the attack. Furthermore, the scenarios of the victim can be classified into high-risk and low-risk scenarios. When the vehicle is just out of the tunnel or in open scenarios, attackers can select these high-risk scenarios and implement aggressive spoofing attacks. Then the efficiency of the GNSS spoofing attack can be significantly improved. Finally, the proposed attack scheme is demonstrated by actual world data with simulated spoofing attacks in urban areas.