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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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    <description></description>
    <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>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Evaluating User Acceptance and Effectiveness of Cognitive Measurements and Intervention for Shared Autonomy</title>
      <link>https://rip.trb.org/View/2690985</link>
      <description><![CDATA[Vehicles equipped with automated driving systems (ADS) have become more widespread in the trucking industry. On the one hand, ADS are known to be susceptible to occasional errors in environment perception, but on the other, ADS can demonstrate safer and more efficient behavior in situations where the driver is cognitively impaired. Shared autonomy systems thus have the potential to combine the best of both paradigms. Some early instantiations of such shared autonomy ADS use measurements of the human cognitive state to perform interventions, either in the form of sensory feedback, and/or by actively taking over the driving task. The main objective of this project is to address the gap in research on the effectiveness and acceptance of cognition-aware shared-autonomy methods with respect to the overall system safety. Qualitative data will be collected through semi-structured interviews with truck drivers and systematically encoded into operational design requirements and hypothesis-driven performance metrics that directly inform the design of cognition-aware shared autonomy systems. The research team will perform a driving simulator study that enables a controlled evaluation of adaptive cognition-aware intervention policies, including rule-based and data-driven triggering mechanisms that dynamically adjust system behavior based on real-time cognitive interventions. Researchers will study how specific design choices in cognition-aware intervention policies (e.g., trigger thresholds, modality selection, and intervention persistence) influence system acceptance, misuse, and compliance, enabling actionable design guidance beyond descriptive acceptance analysis. The datasets collected inform policy on the use of ADS in both drayage and long-haul trucking. This project will develop a methodology for designing and evaluating cognition-aware behavioral interventions that couple driver monitoring outputs with explicit control and feedback policies, enabling reproducible comparison across intervention strategies and deployment contexts.]]></description>
      <pubDate>Thu, 09 Apr 2026 14:23:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2690985</guid>
    </item>
    <item>
      <title>Ensuring PNT Resiience</title>
      <link>https://rip.trb.org/View/2676001</link>
      <description><![CDATA[With CARMEN+ support the research team has characterized the timing properties of modulation from the Starlink constellation in order to assess its suitability for providing opportunistic pseudorange-based positioning, navigation, and timing (PNT) as a backup to Global Navigation Satellite System (GNSS). With the same purpose, the team has also uncovered key features of the OneWeb signal structure and has demodulated its data for the first time. The findings have indicated that opportunistic pseudorange-based PNT is not feasible using Starlink signals without aiding from a network of ground receivers. But given such a network, the team has achieved 10-meter-level positioning and 30-ns timing using Starlink signals. The next phase will extend this project along several lines: (i) characterize the modulation timing stability of OneWeb as the team has done with Starlink, (ii) deploy a network of 2 or 3 reference stations so that all ephemeris and transmission time modeling errors may be eliminated, (iii) employ super-resolution techniques to more precisely estimate modulation (e.g., Starlink frame) time of arrival, and (iv) analyze the pattern of assigned beams and side beams from Starlink satellites to predict how many unique satellites would typically be available for a PNT solution, and with what dilution of precision. For these studies, the team will capture and analyze broadband Starlink, OneWeb, and Kuiper data with their own RF equipment from multiple stations. The team believes that the outcome of this work will be of great importance, namely, a backup PNT system with global reach, decimeter positioning, nanosecond timing, inherent signal authentication (via cross-checking unpredictable broadband payload data and against a reference network), and improved resistance to jamming compared to traditional GNSS. Furthermore, the team aims to transfer this technology to their project partners for commercialization.]]></description>
      <pubDate>Mon, 02 Mar 2026 19:15:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676001</guid>
    </item>
    <item>
      <title>Weather Radar Augmented Positioning (WRAP) Technology for Aerial Vehicles</title>
      <link>https://rip.trb.org/View/2675937</link>
      <description><![CDATA[Problem Statement: As the modern transportation
navigation systems increasingly rely on Global Positioning System/Global Navigation Satellite System (GPS/GNSS) signals, the potential transportation safety risks also
increase significantly should there be intruptions in
transmitting or receiving GPS/GNSS signals in the
events such as strong solar wind activities,
unintentional interferences, or intentional jamming.
To mitigate these transportation safety risks, several
alternative positioning technologies are being
actively developed. These technologies include using
ground-base GPS/GNSS pseudolites and using signal
of opportunities (SOP) transmitted from cellular
towers and LEO communication satellites. However,
the deployment of ground-based GPS/GNSS
pseudolites are still very limited and facing several regulation hurdles over potentially causing
interferences and human RF exposure risks. The coverage of cellular signals are still lacking in rural
or remote areas, and not available at higher altitude above the ground level (AGL), and using LEO
satellite signals for navigation are more complex and expensive. This project will explore the
utilization of the radar signals transmitted from the existing NEXRAD WSR-88D Weather Radar
Network for navigating aerial vehicles. These signals are within the designated 2700-3000 MHz
frequency band with 25 MHz bandwidth. The two key advantage of these SOP signals are strong
signal strength and wide coverage of almost entire US.
Objectives: The main research objectives of this project include demonstrating the feasibility of
using the strong ubiquatus weather radar signals for aerial vehicle navigation needs, defining the
key system hardware and software requirements, and identifying performance limitations.
Scope: This 12-month research effort will include (1) characterizing the NEXRAD signal strengths
and waveforms, (2) investigating distributed receiving antenna strategy, (3) developing positioning
algorithms, and (4) analyzing the positions accuracy and limitations.]]></description>
      <pubDate>Mon, 02 Mar 2026 15:37:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2675937</guid>
    </item>
    <item>
      <title>Safe and Personalized Control of Autonomous Vehicles with On-Board Vision Language
Models: System Design and Real-World Validation
</title>
      <link>https://rip.trb.org/View/2625313</link>
      <description><![CDATA[This project focuses on enhancing autonomous vehicle control systems by integrating on-board Vision-Language Models (VLMs) for safe and personalized driving experiences. Building on the previously awarded Center for Connected and Automated Transportation
(CCAT) project on “CAV Pilot Development and Deployment in Midwest Winter,” this research addresses critical challenges in autonomous vehicle development regarding limited on-board computational resources by implementing lightweight VLM frameworks and Retrieval-Augmented Generation (RAG)-based memory modules. The project will validate the system’s ability to handle challenging urban scenarios, reduce human takeover rates, and adapt to diverse environmental conditions.
]]></description>
      <pubDate>Thu, 13 Nov 2025 15:43:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625313</guid>
    </item>
    <item>
      <title>Navigation System for Snowplows in Low Visibility Situations</title>
      <link>https://rip.trb.org/View/2344960</link>
      <description><![CDATA[In severe winter storms, sometimes visibility degrades to the point that it is no longer safe to continue plowing, and crews are called back to the garage or other safe place until the storm subsides. When this occurs, there are no longer any plows keeping drifts back so roads can quickly cover, and in some cases, drift shut. This severely limits traffic and emergency services from using the road until plows can return. The research team will develop and retrofit an existing snowplow with a suite of sensors and mapping systems along with a driver assistance interface that will guide the operator when visibility is too low for regular operation. In addition,  route guidance, the system will also provide alerts for unexpected obstacles such as stalled/slow-moving vehicles, people, or debris. The system designed in phase 1 will not take over any control and only guide the operator. The main focus for this project will be the economic efficiency and field readiness of the solution. The team will include the following critical innovations to achieve an economical and practical solution to enable agency-level deployment: (i) economical mapping, (ii) economical instrumentation, (iii) shovel ready technology, and (iv) a multi-disciplinary team with expertise in  implementing real-world autonomy solution.]]></description>
      <pubDate>Tue, 27 Feb 2024 19:22:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2344960</guid>
    </item>
    <item>
      <title>R-PNT Virtual Conflict Simulation</title>
      <link>https://rip.trb.org/View/2329759</link>
      <description><![CDATA[This project will entail developing a virtual testbed for modeling various cyber and cyber-physical attacks and designing defense mechanisms to mitigate the effects of these attacks. As part of this effort, simulations will be conducted to evaluate the network-wide effect of such attacks and to evaluate the adequacy of various defense mechanisms in resolving and recovering from these attacks.

While this five-year project will address numerous cyber and cyber-physical attacks, including GNSS jamming and spoofing, the focus of the effort in the first year will be on spoofing of routing attacks, given that these have occurred with Google and Waze. Specifically, in the case of Google, an artist in Berlin tricked Google Maps into creating traffic jam alerts by pulling 99 phones – with their location services on – slowly around the German capital in a handcart1. As part of this effort, the INTEGRATION agent-based traffic simulation model will be modified to allow for the sharing of erroneous real-time travel time information that will impact the dynamic feedback traffic router (similar to the Google maps router). This will be tested on at least one network for different attack locations and intensities to quantify network-wide impacts of such attacks. In addition, various filtering techniques will be devised to try to identify anomalies in the data and rectify data as a means of defense against such attacks.]]></description>
      <pubDate>Wed, 31 Jan 2024 15:58:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2329759</guid>
    </item>
    <item>
      <title>Multi-Vehicle/Infrastructure Jammer/Spoofer Detection and Localization</title>
      <link>https://rip.trb.org/View/2329757</link>
      <description><![CDATA[This project will follow three paths in parallel, all focused on developing vehicle strategies that provide improved knowledge of and resilience to positioning uncertainty, in particular, of the potential risk of spoofing. The first path is focused on developing resilient connected and automated vehicle (CAV) applications given uncertain PNT services; the second is developing resilience techniques through a multi-agent community approach; and the third is to conduct research on collaborative radio-frequency interference (RFI) localization.

(1) The main focus of the first research path is to develop CAV applications that are “aware” of their positioning uncertainty and the potential risk of spoofing, and adapt to make them more robust in terms of safety, mobility, and environmental factors. This will consist of several tasks, including: (a) searching CAV application literature to identify any applications that are adaptable in terms of positioning and spoofing uncertainty; (b) identifying a variety of CAV fundamental maneuvers that can be targeted for position uncertainty adaptive algorithms; (c) designing these adaptive algorithms for a subset of fundamental maneuvers (e.g. vehicle merging), followed by comprehensive testing both in simulation and in the real world; and (d) developing the means for estimating and communicating position uncertainty and the risk of undetected spoofed PNT services.

(2) The main focus of the second research path is to develop resiliency techniques using a multi-agent community approach where a diversity of connected vehicles and infrastructure are operating in close proximity. Within this community, the impacts of jamming could be mitigated by community alerts by directing vehicles to switch to non-GNSS PNT or to avoid a particular area. During spoofing, the spoofed GNSS signals would have had to be generated based on only one vehicle’s predicted trajectory; however, they
would be received by all vehicles within a given neighborhood of the broadcaster. All other vehicles within the reception volume would be receiving inconsistent GNSS signals, which would enable community detection of spoofing. This research will quantify the performance of this detection approach. The research team will analyze a number of scenarios and the impact of transportation threats.

(3) In the third research path, the research team will demonstrate the ability of multiple connected vehicle receivers to detect and localize a common RFI source. The research team will determine the circumstances under which such a collaborative RFI detection and localization scheme are possible. For example, if two receivers are within reach of an RFI source, using time-differenced measurements over larger than 100-meter separation distances can enable time-of-arrival localization. Phase differences can be more challenging to achieve but reduce the baseline requirement to meter level. The research team will quantify the resulting localization performance in example use-cases]]></description>
      <pubDate>Wed, 31 Jan 2024 15:39:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2329757</guid>
    </item>
    <item>
      <title>Radio-Frequency Signal Augmentation to Reduce PNT Jamming and Spoofing Risks</title>
      <link>https://rip.trb.org/View/2329755</link>
      <description><![CDATA[GNSS is vulnerable to jamming because the power of GNSS signals received near the Earth’s surface is extremely weak, as low as a tenth of a millionth of a billionth of a Watt. Higher power signals from Low-Earth Orbiting (LEO) satellites intended for communication can be used as an opportunistic means of navigation, but only with significant risks because the LEO service providers have no commitment to navigation users. In contrast, recently-modernized and emerging dedicated LEO constellations can provide positioning navigation and timing (PNT) with quantifiable performance. In particular, CARNATIONS industrial partners Satelles, Inc. and Xona Space Systems, two PNT LEO satellite constellation operators, provide signals that are secure, powerful, reliable, and independent of GNSS. This project aims at (1) designing LEO satellite-based resilient PNT (R-PNT) algorithms and (2) evaluating them for transportation applications.

(1) The research team proposes to develop new methods to combine LEO and GNSS satellite signals. Navigation continuity is maximized when signals are tightly integrated as early as possible in the navigation system’s processing pipeline. However, preserving the independence of data sources is key to detecting insidious spoofing threats by checking for inconsistencies between individual pieces of information. Loosely-coupled implementations can be more integrity-efficient than tightly-coupled ones for GNSS fault detection and exclusion (FDE) in transportation applications. The proposed research aims at deriving optimal LEO/GNSS algorithms that maximize integrity while maintaining continuity. These algorithms depend on robust models of satellite signal uncertainty that the research team will also establish.

(2) The research team proposes to evaluate the integrity performance of optimal LEO-GNSS PNT algorithms in specific transportation operations, including port and connected vehicle
operations. LEO satellites are more powerful, but their scope of application, for example in high-multipath environments between truck trailers and containers, has yet to be determined. The research team will evaluate the LEO-based PNT coverage in multi-layered maps. These maps will display suggested routes with maximal PNT integrity, will account for building signal obstructions, and will dynamically change with LEO satellite motion.

US DOT Priorities: This research project directly targets the US DOT’s research priority area of Reducing Transportation Cybersecurity Risks. GNSS augmentation using other radio-frequency PNT solutions can help ensure resilience to jamming and spoofing attacks. The research team will be investigating LEO/GNSS integration methods and will be quantifying their integrity and continuity to establish multi-layered R-PNT performance maps for multi-modal surface transportation applications.

The research team will develop the tools and methods to rigorously quantify LEO-based PNT performance. Transferring cutting-edge LEO-based PNT technology into practice for transportation modernization requires mediation between the US DOT and industry agents. The team will leverage their experience acting as neutral mediators in this research program.]]></description>
      <pubDate>Wed, 31 Jan 2024 15:07:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2329755</guid>
    </item>
    <item>
      <title>Defending Against GNSS Jamming and Spoofing by Multi-Sensor Integration</title>
      <link>https://rip.trb.org/View/2329754</link>
      <description><![CDATA[While GNSS is the primary means to provide absolute position information in transportation systems, radio frequency methods to detect anomalous GNSS signals may not enable their exclusion in all events. The multiple sensors incorporated into advanced vehicles and transportation systems offer unique opportunities to combat nefarious activities such as GNSS jamming and spoofing to maintain PNT accuracy and integrity. This project will involve three research directions related to GNSS multi-sensor augmentation.

(1) INS Augmentation. Spoofing relies on accurate prediction of a victim GNSS antenna’s future trajectory to compute and broadcast RF signals to fool the receiver tracking loops on the target vehicle. The vehicle sprung mass, lane curvature, and human driving all add uncertainty around the predicted trajectory, making it difficult to predict GNSS antenna motion. Therefore, the research team questions the ability of a spoofer to predict a target vehicle trajectory with sufficient accuracy to avoid detection. The research team will investigate whether an integrated INS/GNSS with a position-domain innovation sequence detector is sensitive enough to detect the onset of spoofing by monitoring the accumulated time history of normalized KF innovations.

(2) Virtual Augmentation for Ground Vehicles. Unlike aircraft, ground vehicles are subject to kinematic constraints. For example, their lateral (“cross-track”) motion is subject to nonholonomic constraints (i.e., under no-slip conditions, the rear wheels can only move longitudinally, not laterally). Encoders on the four wheels provide information about wheel velocity and slip. Both the wheel speed information and the kinematic constraints can be incorporated into PNT algorithms. The research team will investigate the utility of such methods for detection of anomalous PNT information (e.g., jamming and spoofing).

(3) Multi-sensor Augmentation. Jamming and spoofing only affect the GNSS receiver; therefore, information extracted from additional on-board sensors (e.g., cameras, lidar, radar, ultrasound, IMU, wheel encoders) offer unique opportunities for enhancing PNT resilience. Research will focus on PNT solutions incorporating data from the diversity of sensors to improve both PNT accuracy and detection of anomalous PNT information from all sensors.]]></description>
      <pubDate>Wed, 31 Jan 2024 15:02:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2329754</guid>
    </item>
    <item>
      <title>Receiver Signal Processing to Resist GNSS Jamming and Spoofing Attacks</title>
      <link>https://rip.trb.org/View/2329746</link>
      <description><![CDATA[This project will follow two paths in parallel, both involving advanced Global Navigation Satellite System (GNSS) receiver signal processing. The first is focused on defending against spoofing, the second against jamming.

(1) GNSS signal correlation monitoring approaches have been proposed as powerful means to detect spoofing. A sampled signal can be represented in the form of a complex number, I (in-phase) and Q (quadrature), as a function of code delay and Doppler offset. Existing monitoring concepts use the magnitudes of these complex samples, performing a two-dimensional sweep in code delay and Doppler. Spoofing is detectable if two or more correlation peaks are distinguishable in the search space. In practice, this method is not reliable when multipath is present and for spoofed signals closely matching the true ones.

The research team instead proposes to use the original complex correlation samples to directly decompose the received signal into its components—true, spoofed, and multipath—including their signal amplitudes, Doppler frequencies, code delays, and carrier phases. This new method will allow the research team to detect the difficult cases that existing receiver-based monitoring techniques cannot, where the spoofed and true signals are nearly aligned in code delay and Doppler, but their complex correlation shows distinct peaks.

(2) Carrier tracking in GNSS receivers is especially vulnerable to jamming. The function is generally implemented using a Phase Lock Loop (PLL), which reconstructs the received carrier and produces the carrier-phase ranges essential to high-precision navigation.

During a jamming event, the additive noise pumped into the PLL leads to accumulated error in carrier reconstruction and ultimately loss of phase lock. The PLL is a feedback control system, where the averaged I and Q samples serve as the sensor inputs to a classical controller. However, carrier ‘tracking’ can also be understood as an estimation problem amenable to Kalman filtering. Kalman filter implementations are more flexible than PLLs because their component dynamic and measurement models can be designed to suit the needs of specific scenarios, including jamming resistance. A major challenge in using a Kalman filter for GNSS carrier phase tracking is that it is a hybrid stochastic estimation problem, requiring simultaneous estimation of discrete navigation data bits and continuous carrier phase. To overcome the problem, the research proposes to develop new algorithms using data-adaptive multiple model filters and direct phase estimation of GNSS dataless pilot signals. These methods will allow the research team to much longer averaging times to improve jamming resistance.]]></description>
      <pubDate>Wed, 31 Jan 2024 14:35:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2329746</guid>
    </item>
    <item>
      <title>GNSS Anti-Jam &amp; Anti-Spoof Antenna Technology for Multimodal Transportation</title>
      <link>https://rip.trb.org/View/2329726</link>
      <description><![CDATA[One strategy for toughening receivers is direction-of-arrival sensing. The technique relies on a multi-element GNSS antenna or the equivalent. Such techniques are uniquely well suited to the detection and mitigation of jamming and spoofing attacks on land, air, and sea vehicles. The research team has examined and developed several multi-element technologies such as controlled reception pattern antennas (CRPA) based on commercial off-the-shelf (COTS) components and dual polarization antennas (DPA). CRPA and DPA enable spoofing detection as they are sensitive to the direction of arrival (DOA) of each incoming signal. Spoofing can be detected if the indicated DOAs do not align with anticipated DOA or if the DOAs of all satellite signals come from one direction as may be expected with a single-antenna spoofer. Additionally, CRPAs and DPAs can produce nulls, mitigating the effects of interference. The research team proposes two developments with the goal of transferring these capabilities to manufacturers for use in civil applications.

(1) CRPA development has not been widely explored for civilian transportation due to export restrictions on the number of antenna elements and their capabilities. However, some restrictions have recently been relaxed. Additionally, the technology to have large arrays of antennas is widely available (e.g., used in 5G technology which have about 1000 elements) and not cost prohibitive (Starlink base stations cost $599). The research team has developed small arrays (4 elements or less) compliant with current restrictions. The research team will explore arrays that meet current restrictions and those that do not, the latter to explore the possible benefits to R-PNT in advanced transportation systems of further relaxation of restrictions.

(2) DPA for spoofer detection is a newer technology that utilizes an antenna that can receive both left-hand and right-hand circularly polarized (LHCP, RHCP) signals to
induce DOA sensitivity. This concept came out of a 2016 Stanford Ph.D. thesis. The research team later demonstrated that it can be built using COTS parts. The research team flight tested this concept in 2019. Two implementations were tested – one based on COTS GNSS chipset where the estimation of DOA effects took several seconds due to a serial search process needed to find the incoming phase offset between the LHCP and RHCP. The other used custom software receiver processing to directly solve for the phase ambiguity. One challenge with this concept is building a system that can make continuous DOA calculations using COTS hardware. A second challenge is to develop techniques for handling DOA errors and measurement ambiguity (e.g., 180-degree ambiguity).]]></description>
      <pubDate>Wed, 31 Jan 2024 14:12:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2329726</guid>
    </item>
    <item>
      <title>Administration of Highway and Transportation Agencies. A Framework for Data Exchanges Between Transportation Agencies and Third-Party Mapping Organizations</title>
      <link>https://rip.trb.org/View/2325858</link>
      <description><![CDATA[The objective of this research is to develop a framework for a comprehensive data specification that allows for a two-way exchange of information between transportation agencies and third-party mapping organizations so each can ingest data with uniform attributes and metadata.]]></description>
      <pubDate>Wed, 24 Jan 2024 15:38:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2325858</guid>
    </item>
    <item>
      <title>Transportation Research Synthesis: Public Education on Automated Driver Assist Systems (ADAS)</title>
      <link>https://rip.trb.org/View/2322583</link>
      <description><![CDATA[Contractor will conduct a review of publications addressing the development and efficacy of Automated Driver Assist Systems (ADAS) public education programs. Results of this literature search will be augmented by a state-of-practice online survey distributed to selected AASHTO member states.]]></description>
      <pubDate>Tue, 16 Jan 2024 16:55:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2322583</guid>
    </item>
    <item>
      <title>Towards GNSS-less Navigation: Exploiting Terrestrial and LEO Satellite Signals of Opportunity</title>
      <link>https://rip.trb.org/View/2321512</link>
      <description><![CDATA[Today’s vehicular navigation systems extract position information from a suite of diverse and complementary onboard sensors. For example, a global navigation satellite system (GNSS) receiver provides stable absolute position information and an inertial measurement unit (IMU) and other dead reckoning sensors (e.g., wheel encoders) provide short-term accurate information. After prolonged periods of GNSS signal unavailability, the position solution degrades to unsafe levels as error-corrupted dead reckoning information is integrated without correction from an absolute position information source. Vehicle-mounted sensors (e.g., cameras or lidar) can reduce IMU drift during GNSS unavailability by tracking features in the environment (e.g., walls, light poles, trees, etc.) and then inferring the vehicle’s relative motion with respect to the features via a simultaneous localization and mapping (SLAM) framework. However, after extended periods of time without GNSS aiding corrections, the vehicles’ position estimate will still drift due to the accumulation of sensor errors (e.g., camera scale factor and lidar range errors due to dust and water particles). Over the past decade, signals of opportunity (SOPs); such as AM/FM radio, cellular, digital television, and low Earth orbit (LEO) satellite signals; have been studied and demonstrated as an effective backup or alternative source of absolute positioning information, providing corrections to an inertial navigation system (INS) in the absence of GNSS signals. SOPs possess several desirable characteristics for vehicular navigation: (1) available in most environments of interest; (2) difficult to jam all SOPs, since their signals are scattered throughout the spectrum; (3) produce low geometric dilution of precision, since their transmitters are geometrically diverse; (4) signal reception with carrier-to-noise ratio that is often tens of decibels (dBs) higher than that of GNSS signals; (5) free to use with SOP navigation receivers that do not require network subscriptions; and (6) no deployment cost, since their infrastructure is already operational and maintained by service providers. This project will study the achievable performance of GNSS-less navigation with SOPs, with a focus on cellular 5G and LEO. The study will compare the performance as a function of: (1) number of utilized transmitters (terrestrial 5G alone, LEO alone, and a fusion of both); (2) differential versus non-differential frameworks; (3) fusion with other onboard sensors; and (4) sensitivity to model mismatch.
]]></description>
      <pubDate>Thu, 11 Jan 2024 16:07:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2321512</guid>
    </item>
    <item>
      <title>Development of Concept Approaches to Assessing ADS Perception Performance and Its Relationship to System Safety Performance. (approved in 2020)</title>
      <link>https://rip.trb.org/View/2050307</link>
      <description><![CDATA[This project will identify and assess concepts for isolating and measuring the performance of autonomous driving systems (ADS) object perception capabilities. THIS IS A CARRYOVER PROJECT FROM FY2020]]></description>
      <pubDate>Tue, 25 Oct 2022 10:24:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2050307</guid>
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