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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Covert UAV Directional Spoofing Algorithm Evading Multiple Anti-Spoofing Techniques</title>
      <link>https://trid.trb.org/View/2761452</link>
      <description><![CDATA[The proliferation of unmanned aerial vehicle (UAV) applications in contemporary society has introduced significant security vulnerabilities. While existing spoofing methodologies can deceive UAVs into deviating from preset trajectories by manipulating global navigation satellite system (GNSS) signals, their practical efficacy remains constrained by prevalent anti-spoofing mechanisms. This study proposes an innovative covert directional spoofing framework designed to circumvent advanced anti-spoofing systems, including Kalman filter innovation-based monitoring, least-squares residual receiver autonomous integrity monitoring (LSR-RAIM), and parameter rationality checks. The spoofing algorithm incorporates UAV dynamics and navigation models, with radar employed to measure the UAV's real-time position and velocity. Mathematical models of anti-spoofing algorithms are constructed, and the range of spoofing measurements that can avoid detection is established. The spoofing algorithm needs to know the preset trajectory of the target UAV and gradually changes the direction of UAV. A closed-loop spoofing strategy is introduced to dynamically adjust the deceptive gain matrix and modify counterfeit measurements. Experimental results demonstrate the algorithm's capability to successfully divert a UAV 230 meters from designated flight paths while inducing a 5° northward directional deviation within 250 seconds. Minor fluctuations in Kalman innovation, navigation parameters, and sensor zero biases were observed during the spoofing attack. The proposed algorithm demonstrates reliable capability in directing UAVs along predetermined spoofing trajectories while remaining undetectable to integrated anti-spoofing mechanisms.]]></description>
      <pubDate>Thu, 27 Aug 2026 10:04:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761452</guid>
    </item>
    <item>
      <title>Clustering-Assisted Observation Domain Optimization for GNSS Multi-Fault Detection and Correction</title>
      <link>https://trid.trb.org/View/2606487</link>
      <description><![CDATA[With the rise of autonomous and semi-autonomous vehicles, effective fault detection and mitigation (FDM) methods have become essential in meeting the integrity requirements for precise and reliable Global Navigation Satellite System (GNSS)-based positioning. The existing GNSS-only FDM methods are ineffective in scenarios involving multiple faulty observations due to either their theoretical model limitations or high computational costs. In this paper, a novel GNSS multi-fault detection and correction method is developed that achieves a balance between computational complexity and performance. The proposed method incorporates an Expectation Maximization (EM) framework to jointly estimate an approximate maximum likelihood of states and latent model parameters in the presence of observation outliers, i.e., faults. However, the EM algorithm is known for its high computational complexity. To reduce the computational complexity of EM, an importance sampling step based on unsupervised clustering is introduced. As demonstrated by the results and analysis herein, the proposed method outperforms the existing Least-squares Residuals method, achieving an average improvement of up to 48% in positioning accuracy. Additionally, the computational complexity of the proposed method is an order of magnitude lower than the state-of-the-art Solution Separation method. The method enhances positioning reliability at a lower computational cost and does not require additional infrastructure, hence, can be readily integrated into standalone real-time GNSS applications]]></description>
      <pubDate>Mon, 13 Oct 2025 08:49:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606487</guid>
    </item>
    <item>
      <title>Sampled-Data Neural Network Observer for Motion State Estimation of Full Driving Automation Vehicle</title>
      <link>https://trid.trb.org/View/2554101</link>
      <description><![CDATA[The lateral state information of vehicles is pivotal for their lateral control and safety monitoring of motion states. In light of the high cost associated with a vehicle's lateral speed sensor, this study focuses on estimating the lateral velocity of autonomous vehicles using the vehicle's longitudinal speed and yaw rate. The main difficulty is that the state estimation is affected by sampled and delayed sensor data measurements as well as unknown modeling uncertainties. To overcome these challenges, this article proposes a novel sampled data neural network observer. The proposed observer consists of two components: a continuous state observer and a compensating injector. The compensating injector is devised to offset the information loss during sampling by digital sensors. Additionally, a radial basis function neural network is employed to approximate the unknown dynamics and modeling uncertainties in the vehicle systems. The weights of the neural network are updated using a newly designed weight update law. Furthermore, to tackle the challenge of both sampling and delay measurement, an additional integrator is incorporated in the designed compensating injector. This additional integrator aims to mitigate the impact of sensor data delays on the estimation process. Finally, the stability of the proposed method is demonstrated using the Lyapunov technique. The effectiveness of the proposed sampled data neural network observer is verified through simulation and experiment performed on a full driving automation vehicle experimental platform.]]></description>
      <pubDate>Tue, 27 May 2025 09:33:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2554101</guid>
    </item>
    <item>
      <title>Online Semi-Supervised Transformer for Resilient Vehicle GNSS/INS Navigation</title>
      <link>https://trid.trb.org/View/2506192</link>
      <description><![CDATA[The Inertial Navigation Systems (INS) and Global Navigation Satellite Systems (GNSS) integrated navigation system is widely employed for vehicular positioning. However, obstacles in the wireless signal path often lead to GNSS outages, diminishing the reliability of positioning. Many deep learning-based methods have been proposed for their high accuracy and outstanding generalization capabilities. Nevertheless, they overlook the constraints posed by limited computational resources and memory capacity within real-time, online navigation systems. To strike a balance between positioning accuracy and model complexity, this paper proposes an Online Semi-supervised Transformer (OSS-Transformer) model for GNSS/INS integrated navigation that functions effectively during GNSS signal outages and has low computational requirements. This model comprises three critical components: 1) A Transformer-based architecture for adaptive feature extraction; 2) A self-supervised Masked Language Model (MLM) task to understand contextual relationships and feature distributions within INS data; 3) An improved memory replay technique for efficient online network training. Verified by the road test, the proposed OSS-Transformer based integrated navigation system reaches a maximum error of 6.02 m during 40 s outages and 9.81 m during 100 s outages, significantly outperforming traditional deep-learning methods. Additionally, the improved memory replay technique reduces loss by 26.23% compared to conventional methods, making it feasible on navigation devices with constrained computational resources and memory.]]></description>
      <pubDate>Tue, 18 Feb 2025 10:43:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2506192</guid>
    </item>
    <item>
      <title>GNSS/IMU Integrated Navigation Methods with NLOS Mitigation</title>
      <link>https://trid.trb.org/View/2475336</link>
      <description><![CDATA[With the development of computer technology, intelligent vehicles have become a popular orientation at present. This paper provides a solution for the traditional GNSS/IMU integrated navigation to mitigate the influence of non-line-of-sight (NLOS) environments and achieve high-precision navigation. The LOS/NLOS switching is treated as a transition in a Markov chain, and the DIMM filtering is applied to obtain GNSS-filtered data free from the influence of NLOS. GNSS/IMU integrated navigation is loose coupling in that the method combines IMU information with processed GNSS measurement using the ESKF (Error-State Kalman Filter) to achieve accurate target tracking and positioning by utilizing the state difference as the state variable. Simulation results demonstrate that the proposed method significantly improves tracking and positioning accuracy by effectively mitigating NLOS effects compared to traditional methods. This research provides an effective solution for real-world autonomous vehicle navigation systems and optimizes the city driving experience.]]></description>
      <pubDate>Mon, 23 Dec 2024 10:36:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475336</guid>
    </item>
    <item>
      <title>Resilient V2X Communication for Cooperative and Remote Driving </title>
      <link>https://trid.trb.org/View/2444700</link>
      <description><![CDATA[This project focuses on developing a Global Navigation Satellite System (GNSS) spoofing detection and mitigation system for Connected and Autonomous Vehicles (CAVs) by leveraging information shared between vehicles and other signals of opportunity. The main goal is to detect jamming and spoofing attacks, particularly in vehicle-to-vehicle scenarios where cooperative operations are targeted. In collaboration with Illinois Tech's CARNATIONS team and StarNav, the project will utilize real-world data and onboard sensors such as LiDAR and cameras to enhance detection capabilities.

The research is structured into two tasks: (1) detecting GNSS meaconing, where attackers retransmit GNSS signals with a delay, misleading vehicles about their position and timing, and (2) identifying and mitigating targeted GNSS spoofing attacks, where attackers aim to control the navigation system of a lead vehicle in a platoon. These tasks will involve analyzing the spoofing effects on non-target vehicles and determining key detection information, such as signal arrival time and direction.

The detection system will be incrementally developed through hardware-in-the-loop simulations and real-world data analysis, exploring the potential for re-localization of affected vehicles during spoofing attacks. The project aligns with U.S. Department of Transportation priorities by enhancing transportation safety and resilience, particularly in highway platooning scenarios. The outcome will be a robust CAV application that remains resilient against GNSS spoofing threats.






]]></description>
      <pubDate>Tue, 22 Oct 2024 16:08:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2444700</guid>
    </item>
    <item>
      <title>Examining and Enhancing Vehicle Spoofing Detection Capabilities in CAV Applications: Real-World Testing </title>
      <link>https://trid.trb.org/View/2444698</link>
      <description><![CDATA[This project addresses the rising incidence of GNSS spoofing attacks by enhancing detection capabilities in Connected and Autonomous Vehicles (CAVs). The focus is on leveraging information shared by surrounding vehicles and signals of opportunity to identify jamming and spoofing threats during vehicle-to-vehicle maneuvers. Conducted in collaboration with the CARNATIONS team at Illinois Tech and StarNav, the project explores detection strategies using real-world spoofing data and onboard sensors such as LiDAR and cameras.

The research is divided into two tasks: (1) detecting GNSS meaconing, which involves analyzing the impact of signal retransmission delays on non-target vehicles and identifying key detection information, such as timing and signal direction, and (2) detecting targeted GNSS spoofing attacks, particularly in scenarios where the lead vehicle of a platoon is compromised, focusing on the detection and mitigation of manipulated satellite data.

The team will develop a layered defense strategy, implementing detection systems through hardware-in-the-loop simulations and real-world testing to explore vehicle re-localization during attacks. This research aligns with the U.S. Department of Transportation’s priority of enhancing transportation system safety and resilience by countering GNSS spoofing threats in CAV applications, particularly in highway platooning.]]></description>
      <pubDate>Tue, 22 Oct 2024 16:11:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2444698</guid>
    </item>
    <item>
      <title>Development of a Generalized Integrity Monitoring Framework For CAV Application</title>
      <link>https://trid.trb.org/View/2444697</link>
      <description><![CDATA[CAV applications can be broadly classified into three major categories: safety, mobility, and environmental. Mobility and environmental applications require a coarse positioning accuracy (5-10 m) and lane-level positioning accuracy (< 1 m), while safety-critical applications require a where-in-lane level accuracy (< 0.2 m). Uncertainty in positioning information can sabotage driving functionalities and cause a safety concern. Position uncertainty of a vehicle can be attributed to the sensor suite available on the vehicle, along with any additional external sensor information that may aid the in-vehicle sensors. Furthermore, a fully connected and Automated vehicle (CAV) may turn itself into a degraded CAV, AV-only, CV-only, or Human-driven vehicle (HDV) while experiencing communication or control loss. The type, quantity, placement, and measurement uncertainty of sensors play a great role in determining the navigation performance of a vehicle. Additionally, in a mixed traffic scenario, there exist various types of positioning solutions, vehicles, sensor modalities, communications capabilities, and applications. It is unlikely that two vehicles having the same set of sensors and computation hardware will output similar navigation performance. Therefore, it is important to study and analyze the integrity of positioning systems under various conditions. 

Integrity monitoring (IM) methods have been extensively studied and developed for in-vehicle sensor systems primarily consisting of GNSS receivers (e.g. RAIM) often integrated with IMUs, vehicle odometry, and perception sensors such as radar, camera, and LiDAR. The localization performance and safety of a vehicle have mainly been studied from an ego vehicle's perspective focusing on enabling automated driving functions through onboard sensors and compute platform. Further, there is a surge in V2V/V2I/V2P/V2X research focusing on information sharing between road agents for improved positioning, navigation, and control. Given the number of sensor sources and the amount of data shared between road agents, it is imperative to develop integrity monitoring frameworks for cooperative scenarios. Based on the research team's initial literature survey, despite the growing interest in this area, there is a noticeable gap in the current literature that addresses the "cooperative-IM (integrity monitoring)" framework, indicating the need for further research to propose new Required Navigation Performance (RNP) parameters that may support CAV applications.  

As a part of the research team's smart intersection projects (City of Riverside & City of Rialto), it has conducted various Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) based cooperative positioning experiments, where perception data from both onboard and roadside sensors i.e. camera, LiDAR, is shared among the vehicles and infrastructure through C-V2X communication. The research team has successfully demonstrated improved vehicle detection and positioning accuracy (0.3 meters with Roadside LiDAR). Furthermore, the research team is currently experimenting with Cohda wireless MK6 modules to transmit basic safety messages (BSMs) and other information among vehicles in a V2V setting. However, the question remains whether the empirically determined Required Navigation Performance (RNP) values hold consistent across different driving conditions, positioning hardware, and communication topologies. Inconsistencies in RNP parameters would lead to the failure of CAV applications. ]]></description>
      <pubDate>Tue, 22 Oct 2024 16:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2444697</guid>
    </item>
    <item>
      <title>Improving GNSS Resiliency Using Edge AI Solutions</title>
      <link>https://trid.trb.org/View/2444693</link>
      <description><![CDATA[Improving the resilience of Global Navigation Satellite Systems (GNSS) is crucial, especially in challenging environments where global positioning systems (GPS) signals can be weak or disrupted. Enabling Edge Artificial Intelligence (Edge AI) offers promising solutions that employ AI algorithms and models on edge devices without constant reliance on cloud infrastructures; especially in highly dense blockage environments, is an interesting area of research. Our strategy will explore how Edge AI can enhance GNSS resiliency in challenging scenarios by bringing intelligence to the edge node. 

  

In dense urban environments, where reliable measurements are often inaccessible due to obstacles, Edge AI techniques can play a crucial role. When deploying Edge AI in dense blockage environments, such as urban canyons or indoor spaces, we will consider the following: 

  

1.    Edge Computing Infrastructure for GNSS Services: 

Setting up edge servers or devices close to the GNSS receivers to process AI algorithms locally. This minimizes latency and ensures real-time decision-making. 

Opting for low-power, compact edge devices that can handle AI workloads efficiently. 

2.    AI Algorithms for Signal Enhancement: 

Employing machine learning models to enhance GNSS signals affected by multipath reflections, interference, and blockages. 

Techniques like deep learning, Kalman filtering, and particle filters will be investigated for positioning accuracy enhancement by mitigating noisy measurements. 

3.    GNSS Abnormalities Detection and Mitigation 

Create a more robust GNSS system 

Employe multiple positioning sources for redundancy and come up with the concept of multi- level accuracy support 

Employe AI for GNSS abnormalities detection, abnormal behavior includes attacks and failures 

Come up with GNSS abnormalities mitigation techniques 

4.    Cooperative Learning 

Allow devices to share positioning information and learn in a cooperative approach using distributed learning 

This will allow low end devices to benefit from more capable devices with higher accuracy GNSS support 

5.    Map-Based Localization: 

Pre-existing maps will be leveraged, or local maps of the environment will be created. These maps can help in predicting GNSS signal blockages and aid in localization. 

Techniques like SLAM (Simultaneous Localization and Mapping) will be considered. 

6.    Dynamic Adaptation and Hybrid Positioning (Coexistence Networking Strategies (CNS): 

Implementing adaptive algorithms that adjust parameters based on real-time conditions. 

Hybrid positioning approaches provide redundancies and improve availability in challenging environments. For example, if a GNSS signal is blocked due to tall buildings, the system will be complemented by additional technologies such as Wi-Fi or Bluetooth for localization. 

This project will employ Edge AI solutions that are tailored to the challenging environments and use cases. Regular testing and validation will be implemented to ensure reliable performance. ]]></description>
      <pubDate>Tue, 22 Oct 2024 16:32:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2444693</guid>
    </item>
    <item>
      <title>High-Precision Positioning, Perception and Safe Navigation for Automated Heavy-Duty Mining Trucks</title>
      <link>https://trid.trb.org/View/2394838</link>
      <description><![CDATA[Autonomous driving technology has achieved significant breakthroughs in open scenarios, enabling the deployment of excellent positioning, detection, and navigation algorithms on passenger vehicles. However, there has been limited research attention devoted to autonomous driving for specialized vehicles in non-open scenarios. This manuscript introduces a perception system designed for heavy-duty mining transportation trucks operating in open-pit mines, which are typical of non-open scenarios. The system comprises four independent algorithms: high-precision fusion positioning, multi-task 2D detection, 9 Degrees of Freedom (9 DoF) 3D head, and autonomous navigation technology. Experimental verification demonstrates the effectiveness of these methods in addressing the challenges posed by mining environments, ultimately leading to enhanced safety and efficiency for trucks. This research outcome, through the comprehensive examination of positioning, detection, and navigation, aims to address the challenges encountered by mining trucks during operations. Its significance lies in enhancing automation levels in mining scenarios.]]></description>
      <pubDate>Tue, 23 Jul 2024 17:43:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2394838</guid>
    </item>
    <item>
      <title>Enhanced Land Vehicle Positioning in Challenging GNSS Urban Environments Utilizing Automotive Radars</title>
      <link>https://trid.trb.org/View/2341421</link>
      <description><![CDATA[Positioning services for land vehicle navigation have long relied on global navigation satellite systems (GNSS); however, GNSS cannot maintain an accurate position while traveling under bridges, around tall buildings and under tree canopies due to signal blockage or multipath. Integration with the onboard motion sensors can bridge the positioning solution for a short duration but cannot sustain adequate positioning performance during extended GNSS outages. The proposed research overcomes the limitations of current positioning technologies for land vehicles by integrating a GNSS receiver, onboard motion sensors, and an electronic scanning radar (ESR) used in present land vehicles for adaptive cruise control. A new ESR-based static object detection method is developed using newly designed criteria to estimate the forward velocity. The authors also propose a radar odometry method to obtain the vehicle position based on the radar scans. Integration with onboard motion sensors based on Extended Kalman filtering (EKF) is designed and realized to achieve accurate positioning in degraded vision and challenging GNSS environments. The proposed multi-sensor positioning solution is examined using several road test trajectories in challenging urban environments in Toronto. It shows RMS error as low as 3 m over more than 90% of the traveled distance.]]></description>
      <pubDate>Fri, 08 Mar 2024 15:32:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2341421</guid>
    </item>
    <item>
      <title>Vehicle heading angle and IMU heading mounting angle improvement leveraging GNSS course angle</title>
      <link>https://trid.trb.org/View/2222536</link>
      <description><![CDATA[Using vehicle chassis information can significantly improve the accuracy of GNSS/INS fusion system when GNSS signals are unavailable. The IMU mounting angles play an important role when fusing vehicle chassis information with GNSS/INS system. However, the IMU mounting angles cannot be directly measured conveniently. This paper proposed a new method to simultaneously improve the estimation accuracy of both the vehicle heading angle and the IMU heading mounting angle by leveraging GNSS course angle as an additional measurement. Firstly, an error state Kalman filter is constructed with state variables including the attitude errors, velocity errors, position errors, gyro, and acceleration bias errors, IMU mounting angle and vehicle velocity scale factor. The GNSS course angle is augmented to the Kalman filter as a measurement when the vehicle travels in straight line. Then, the observability analysis of the GNSS/INS/Onboard sensors fusion system is carried out and the results show that the observability of the system using GNSS course angle is better than that of only using vehicle velocity and non-holonomic constraint (NHC) as measurements if the heading mounting angle and pitch mounting angle are not zero. Finally, the experiment results show that the accuracy of the vehicle heading angle and the IMU heading mounting angle can be improved by 14% judging from the lateral velocity error of vehicle compared to that of only using vehicle velocity and NHC as measurements.]]></description>
      <pubDate>Wed, 23 Aug 2023 10:14:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2222536</guid>
    </item>
    <item>
      <title>Monitoring the dynamic response of a pedestrian bridge by using low-cost GNSS receivers</title>
      <link>https://trid.trb.org/View/2146842</link>
      <description><![CDATA[The development of low-cost global navigation satellite system (GNSS) receivers with carrier-phase measurement capacity has led to low-budget GNSS applications of higher accuracy and precision. Recent studies have mainly been carried out with those low-cost receivers for landslide monitoring and achieved promising results. In this study, the performance of two closely-spaced high-rate low-cost GNSS receivers was assessed against the robotic total station (RTS) and geodetic GNSS receiver in monitoring the dynamic response of a major pedestrian suspension bridge at the mid-span. Potential accuracy improvement by the combination of two low-cost GNSS time-series was also examined. It was proved that multi-GNSS solution is required to resolve potential outliers and offsets of the low-cost GNSS time-series, due to cycle slip induced errors. The analysis of the low-cost GNSS time-series showed that the low-cost GNSS receivers can estimate (i) the main dominant frequencies of the bridge with the same accuracy as the geodetic-grade GNSS receiver and (ii) the amplitude of the bridge response with difference of ∼3 mm with respect the geodetic GNSS receiver due to higher noise level. This study revealed the prospect of utilising low-cost GNSS sensors in monitoring dynamic displacement with frequency of 1–3 Hz, corresponding to relatively rigid structures (e.g., short span bridges, etc.).]]></description>
      <pubDate>Wed, 24 May 2023 09:06:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2146842</guid>
    </item>
    <item>
      <title>GNSS Spoofer Localization for Vehicles Based on Doppler and Clock Drift Double Difference</title>
      <link>https://trid.trb.org/View/2156240</link>
      <description><![CDATA[With the development of vehicle navigation, autonomous driving and unmanned aerial vehicles (UAV), increasing spoofing attacks of global navigation satellite systems (GNSS) have emerged towards vehicles, which can mislead users by false position, velocity and time (PVT) solutions, resulting in serious consequences. Spoofer localization is a necessary step to fundamentally address the spoofing attacks. Most of the conventional spoofer localization methods are based on time of arrival (TOA) or time difference of arrival (TDOA), where time synchronization is indispensable and therefore is hard to apply to the existing vehicles equipped with independent commercial receivers. To cope with this problem, in this paper the authors develop a flexible method for vehicles to locate a stationary spoofer based on Doppler and clock drift double difference (DCDD). DCDD is calculated using Doppler measurements and clock drift solutions of one vehicular receiver and another reference receiver. Generally, Doppler frequency of GNSS signals changes slowly with time, which releases the restriction for high-precision time synchronization. DCDD represents the Doppler frequency caused by the vehicle motion, from which the spoofer position can be estimated. Utilizing DCDD, an iterative weighted least squares (WLS) algorithm is used to solve the spoofer position, and a closed-form initial value is given to improve the convergence rate. The authors evaluate the localization performance by the derived Cramér-Rao lower bound (CRLB) in the presence of vehicle position and velocity errors. Simulations and field vehicle experiments are conducted to verify the feasibility and effective of the proposed method.]]></description>
      <pubDate>Tue, 23 May 2023 10:09:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2156240</guid>
    </item>
    <item>
      <title>Radar-Based Multisensor Fusion for Uninterrupted Reliable Positioning in GNSS-Denied Environments</title>
      <link>https://trid.trb.org/View/2082284</link>
      <description><![CDATA[Multi-sensor integration is necessary to provide high-precision navigation solutions for autonomous vehicles. Land-vehicles often rely on global navigation satellite systems (GNSS) to acquire its position. However, there are some environments where GNSS signals are unavailable, such as indoor-parking garages, tunnels, and under bridges. Additional sensors are required to allow reliable positioning regardless of location. The vehicle’s on-board low cost inertial sensors (accelerometers and gyroscopes) are used for positioning in GNSS-denied environments. Despite their fidelity in short-term usage, inertial navigation systems (INS) are susceptible to drifts in their positioning solution due to the inherent inertial sensor errors, causing positioning errors over time in prolonged scenarios, such as indoor-parking garages. Modern land-vehicles can be equipped with a diversified set of perception systems (e.g. LiDAR, cameras) to provide information about the surrounding environment. These systems are well-studied in literature to provide accurate positioning. Yet, the performance of these systems may degrade in weather conditions such as heavy snow or rain, and may fail in degraded vision environments. The proposed research addresses some of the limitations of current positioning technologies for land vehicles by integrating low-cost on-board motion sensors with the all-weather electronic scanning radar (ESR) systems presently used in adaptive cruise control. This research employs a method of estimating the vehicle position based on multiple ESR systems. Integration with the on-board motion sensors guarantees continuous positioning estimation for uninterrupted navigation. The multi-sensor system integration utilizes extended Kalman filter (EKF) with a unique dynamic tuning approach for reliable and uninterrupted positioning. The proposed solution was examined in real GNSS-denied scenario of an indoor parking. This research has reached an uninterrupted self-contained EKF-based multi-sensor positioning system providing less than 2m error 90% of the time in a GNSS-denied environment for up to four minutes.]]></description>
      <pubDate>Tue, 25 Apr 2023 09:49:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2082284</guid>
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