Apparatus and method for latency compensation in automotive augmented reality applications

Abstract

An augmented reality head-up display (AR HUD) system is disclosed for accurately overlaying navigation and safety information in real time. The system integrates sensor data from a camera, inertial measurement unit, steering angle sensor, and wheel rotation sensors, which are fused by a non-linear Kalman fdter and vehicle kinematics model to produce a precise ego-state estimate. A self-calibration module continuously adapts parameters to compensate for sensor drift and aging, while a self-diagnosis module evaluates residual errors to maintain functional safety. Latency measurement and prediction modules identify photon-to-photon delay (Atptp) between sensor capture and HUD projection, and apply predictive corrections to moving objects and ego-motion. This ensures that displayed overlays remain spatially aligned with real-world objects. The modular architecture supports integration with existing vehicle controllers and communication protocols, providing a scalable, cost-effective solution for robust automotive AR HUD applications.

Description

Atty Docket: BM 30 US Patent ApplicationAPPARATUS AND METHOD FOR LATENCY COMPENSATION IN AUTOMOTIVE AUGMENTED REALITY APPLICATIONSBACKGROUND OF THE INVENTIONField of the Invention

[0001] The present invention relates generally to augmented reality (AR) systems for automotive applications and, more specifically, to methods and apparatus for reducing end-to-end latency in automotive augmented reality head-up displays (AR HUDs). The invention lies in the field of software-based latency compensation, sensor fusion, and predictive modeling for real-time alignment of virtual overlays with real-world driving environments.

[0002] More particularly, the invention addresses the persistent challenge of visual misalignment between projected AR content, such as navigation guidance, hazard alerts, and vehicle status information, and the actual road scene caused by delays inherent in data acquisition, processing, and rendering pipelines. The disclosed system integrates multi-sensor input sources (including ADAS cameras, inertial measurement units, steering angle sensors, and wheel rotation sensors) with advanced predictive algorithms, such as Extended Kalman Filters (NLKF) and vehicle kinematic models, to dynamically compensate for latency.

[0003] This field also encompasses self-calibration and self-diagnosis mechanisms that ensure long-term operational reliability by correcting for sensor drift, environmental variability, and hardware degradation. The invention is therefore situated at the intersection of automotive electronics, computer vision, and driver assistance technologies, with specific relevance to improving driver safety, comfort, and situational awareness through enhanced AR HUD accuracy and robustness.Description of Related Art

[0004] The automotive industry has made considerable progress in integrating augmented reality (AR) technology into vehicles, particularly through the development of Head-Up DisplaysAtty Docket: BM 30 US Patent Application(HUDs). AR HUDs are designed to overlay critical driving information directly onto the windshield in the driver’s line of sight. Such information typically includes navigation instructions, hazard warnings, speed and vehicle status indicators, and advanced driver assistance system (ADAS) outputs. By enabling drivers to access this information without diverting their gaze from the road, AR HUDs improve situational awareness and enhance both convenience and safety.

[0005] Despite these advancements, AR HUD systems continue to suffer from the persistent problem of latency. End-to-end latency arises from the cumulative delays in sensor data acquisition, processing, rendering, and projection onto the HUD. Even modest delays result in misalignment between virtual overlays and real -world objects, undermining the reliability of the system. Such discrepancies can degrade driver trust, diminish the effectiveness of navigation and hazard detection, and even induce motion sickness or visual discomfort. Numerous studies in the field of AR have documented these adverse effects, underscoring the need for robust compensation strategies to address latency in real-time driving environments.

[0006] Existing efforts to address latency have focused predominantly on hardware-based improvements, including the use of higher-performance processors, high-speed cameras, and displays with faster refresh rates. While such measures can incrementally reduce latency, they are constrained by physical limits, increased power consumption, and prohibitive cost, particularly in the context of mass-market automotive platforms. Parallel efforts to employ software-based compensation have so far relied on simplistic motion prediction models that are ill-suited for real-world driving. These models frequently fail to capture the complexity of vehicle dynamics, the variability of road environments, and long-term issues such as sensor drift, resulting in reduced system accuracy over time.

[0007] Another notable shortcoming in current AR HUD solutions is the absence of selfcalibration mechanisms. Sensors may undergo drift due to thermal variation, vibration, component wear, or installation tolerances. Without automated recalibration, the accuracy of AR overlays degrades over time, necessitating costly manual adjustments or leaving the driver with unreliableAtty Docket: BM 30 US Patent Applicationvisual guidance. This lack of self-calibration poses a significant barrier to the scalability and longterm adoption of AR HUD systems in production vehicles.

[0008] The literature reflects this gap. For instance, latency-related challenges and their impact on AR usability have been documented in research such as Lincoln’s dissertation on motion-to-photon delay (incorporated herein by reference). These works confirm that unsynchronized AR overlays lead to driver distraction and motion sickness, yet fall short of proposing comprehensive, vehicle-specific solutions capable of operating under dynamic, high-speed driving conditions.

[0009] As noted, most prior approaches are heavily reliant on hardware upgrades, which do not scale economically across automotive product lines. In contrast, the present invention leverages state-of-the-art vehicle hardware platforms and system architectures but introduces novel, software-based latency compensation mechanisms that outperform conventional techniques while remaining cost-effective and scalable.

[0010] Furthermore, existing predictive models do not adequately address the intricacies of dynamic vehicle motion. They often fail under conditions of rapid acceleration, sharp steering, or environmental noise. Coupled with the absence of self-calibration, these shortcomings have limited widespread deployment of AR HUDs in everyday vehicles, confining advanced AR HUDs to concept cars or premium automotive lines.

[0011] To overcome these limitations, the present invention introduces an advanced, softwarecentric latency compensation system specifically designed for automotive AR HUD applications. The invention combines multi-sensor fusion (ADAS camera, IMU, steering angle, and wheel rotation sensors) with predictive filtering (such as Extended Kalman Filter), vehicle kinematic modeling, and real-time compensation algorithms. Complementary self-calibration and selfdiagnosis modules ensure the system remains accurate, adaptive, and reliable throughout the lifetime of the vehicle.Atty Docket: BM 30 US Patent Application

[0012] This inventive approach represents a significant improvement over the prior art by offering: a cost-effective software-based solution that reduces reliance on expensive hardware; Dynamic, real-time latency compensation capable of sustaining accuracy under variable driving conditions; a self-calibration module that autonomously corrects for drift and environmental influences; and a self-diagnosis feature that quantifies confidence in the latency compensation result and adapts or disables AR outputs as needed to ensure driver safety. Together, these features provide a robust, scalable solution that advances the state of AR HUD technology, improving driver trust, safety, and comfort.SUMMARY OF THE INVENTION

[0013] Latency in augmented reality (AR) systems, particularly in automotive head-up displays (HUDs), presents a critical technical challenge. End-to-end system delay leads to misalignment between projected virtual objects and the actual driving scene, causing reduced safety, diminished driver confidence, and degraded user experience. Misaligned overlays can also induce motion sickness and driver distraction, especially under dynamic driving conditions where precise synchronization is essential.

[0014] Addressing latency is therefore fundamental to enabling broader adoption of AR HUDs. Accurate and real-time alignment of augmented content not only enhances navigation guidance and hazard detection, but also improves driver trust, situational awareness, and roadway safety. A reliable latency compensation system further allows AR HUDs to be deployed across a wider range of vehicle classes, including mass-market models, rather than being confined to premium or concept vehicles. In this context, the present invention emphasizes self-adjusting capabilities to ensure robust operation under diverse driving conditions, while also incorporating self-diagnostic features to monitor performance in real time and adaptively disable or restrict AR outputs when reliability falls below acceptable thresholds.Atty Docket: BM 30 US Patent Application

[0015] It is therefore an object of the present invention to provide a system and method for realtime latency compensation in automotive AR HUDs. The invention integrates multiple hardware and software components into a cohesive architecture designed for low-latency, high-accuracy performance. Core hardware elements include an advanced driver assistance system (ADAS) camera for environmental perception, an inertial measurement unit (IMU) for motion sensing, steering angle sensors, and wheel rotation sensors. These devices supply complementary streams of data reflecting both external scene information and vehicle dynamics.

[0016] The data inputs are processed by a set of specialized software modules, including an object tracking module, a localization module, and a latency compensation module. The compensation module employs a non-linear modifications of Kalman Filter (NLKF, such as Extended Kalman Filter - EKF, Unscented Kalman Filter - UKF or others) in combination with a vehicle kinematics model to estimate and correct delays arising from sensor processing and rendering cycles. A selfcalibration module ensures long-term stability by detecting and correcting for drift, misalignment, and system degradation, while a self-diagnosis module continuously evaluates confidence in the output. The processed results are delivered to a rendering module, which projects precisely aligned augmented visuals onto the HUD.

[0017] The system offers several significant benefits: ensures real-time performance by minimizing end-to-end delays and synchronizing AR overlays with real-world objects; delivers high accuracy through predictive modeling that combines NLKF and vehicle kinematics; maintains long-term reliability by incorporating automated, periodic, and event-triggered selfcalibration; integrates seamlessly with existing automotive electronic architectures, enabling cost-effective deployment across multiple vehicle platforms; and enhances safety and driver comfort by reducing motion sickness, improving navigation guidance, and providing reliable hazard detection.

[0018] The technical literature acknowledges the persistence of latency challenges in AR HUD systems. Research has highlighted the shortcomings of basic prediction models and emphasizedAtty Docket: BM 30 US Patent Applicationthe importance of adaptive, software-based approaches. Similarly, industry case studies confirm that self-calibration techniques can sustain system accuracy over time, yet no prior solution combines these with a comprehensive latency compensation framework that includes selfdiagnosis.

[0019] For example, U.S. Patent Application Publication No. US2024 / 0428526A1 describes AR systems that capture images, identify objects, and render augmented visuals in real time, using predictive algorithms and optimized pipelines. While effective at accelerating object recognition and rendering, such systems still encounter delays when synchronizing overlays with the driver’s view in highly dynamic environments.

[0020] Similarly, U.S. PatentNo. 11,676,346 discloses AR vehicle interfacing techniques that use multisensor data fusion and Kalman filtering to generate augmented visuals. Although this approach improves obstacle detection and hazard awareness, it remains limited by latency in multisensor integration and lacks mechanisms for automated self-calibration and self-diagnosis.

[0021] Accordingly, while prior systems address certain aspects of latency, none combine predictive algorithms, sensor fusion, self-calibration, and self-diagnosis into a unified architecture tailored for real-time AR HUD latency compensation. The present invention fills this gap, providing a scalable, software-driven solution that advances the art by ensuring continuous accuracy, adaptability, and safety in real-world driving conditions.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0022] Further advantageous features and details of the various embodiments of this disclosure will become apparent from the ensuring description of a preferred exemplary embodiment or embodiments and further with the aid of the drawings. The features and combinations of features recited below in the description, as well as the features and feature combination shown after that in the drawing description or in the drawings alone, may be used not only in the particularAtty Docket: BM 30 US Patent Applicationcombination recited by also in other combinations on their own without departing from the scope of the disclosure.

[0023] Figure 1 is a block diagram of the latency compensation method and for an augmented reality head-up display (AR HUD) system, showing sensor inputs, fusion modules, and the rendering pipeline.

[0024] Figure 2 is a timing diagram illustrating photon-to-photon latency (Atptp) between the earliest sensor capture and AR HUD display, and the use of predictive correction to compensate for motion occurring during that interval.

[0025] Figure 3 is a flowchart illustrating a method for reducing latency in vehicle augmented reality applicationsDETAILED DESCRIPTION OF THE INVENTION

[0026] The following detailed description is directed to certain specific embodiments and examples and is not intended to limit the scope of the invention. Unless otherwise indicated, the same reference numerals are used to denote the same or corresponding parts throughout the figures. All numeric ranges are inclusive of their endpoints unless stated otherwise.

[0027] The present invention provides a system and method for reducing latency in automotive augmented reality (AR) applications, particularly for Head-Up Displays (HUDs). The system integrates multiple sensors, predictive algorithms, and real-time compensation techniques to align augmented visuals with real-world objects with high accuracy. By utilizing an non-linear Kalman Filter (such as EKF), vehicle kinematics modeling, self-calibration, and self-diagnosis mechanisms, the invention effectively reduces latency and maintains system accuracy over time.

[0028] Referring now to Figure 1, a system overview of the proposed augmented reality (AR) head-up display (HUD) latency compensation framework is illustrated. The system comprises a combination of hardware and software components configured to track vehicle motion, estimate latency effects, and compensate for display misalignment in real time. As shown in Figure 1, aAtty Docket: BM 30 US Patent Applicationsensor array (100) includes: an Advanced Driver Assistance System (ADAS) camera (102) that captures images of the surrounding environment and detects objects such as vehicles, road signs, and lane markers; a GPS module (103) that measures the vehicle position, attitude and uncertainty of the calculated position; an inertial measurement unit (IMU) (104) that measures vehicle acceleration and angular velocity; a steering angle sensor (106) that records steering wheel input to anticipate directional changes; and wheel rotation sensors (108) that provide vehicle speed and acceleration data.

[0029] The collected sensor data is transmitted to a suite of processing modules (120). These include an object tracking module (122) for identifying and following dynamic objects in the vehicle’s field of view, and a localization module (124) that fuses sensor inputs to estimate realtime vehicle position and orientation. A latency compensation module (126) applies a non-linear modification of Kalman Filter (such as EKF) and vehicle kinematics modeling to predict and correct display latency. A rendering module (128) generates latency-compensated AR overlays, which then are handled by Display Manager (129) for projection on the AR HUD (130). To ensure long-term stability, a self-calibration module (132) periodically adjusts system parameters, while a self-diagnosis module (134) monitors latency compensation accuracy and provides diagnostic information enabling adaptive system behavior.

[0030] The latency compensation method implemented by the system of Figure 1 operates in multiple stages. Step 1 : Data Collection and Preprocessing. The ADAS camera (102) continuously captures image frames of the driving environment, while the IMU (104), steering angle sensor (106), and wheel rotation sensors (108) generate vehicle motion data. All sensor inputs are synchronized through a timestamp alignment process to ensure temporal consistency. Step 2: Object Tracking and Localization. The object tracking module (122) detects and monitors dynamic objects, and the localization module (124) integrates sensor fusion outputs to provide high-accuracy vehicle pose estimates. Step 3: Latency Measurement and Estimation. Latency delay (At) is determined by comparing timestamps of sequential object detections. Using kinematic modeling, the system estimates the displacement of tracked objects during the latency period. StepAtty Docket: BM 30 US Patent Application4: Predictive Latency Compensation. The latency compensation module (126) applies the NLKF to predict corrected object positions, with further refinement from vehicle kinematics data such as steering angle and acceleration. Step 5: Augmented Reality Rendering. The rendering module (128) provides the picture with augmented objects to the Display Manager (129), which projects the latency-corrected objects onto the HUD (130), ensuring that augmented visuals remain spatially aligned with the real-world environment even under dynamic driving conditions.

[0031] A central innovation of the present invention is the self-calibration module (132), which provides long-term stability and robustness to the latency compensation system. This module operates in two complementary modes: continuous calibration during real-time driving and periodic background recalibration cycles. The module compares predicted vehicle state outputs from the ego-localization NLKF (124) with ground-truth indications from the sensor array, including the ADAS camera (102), GPS (103), IMU (104), steering angle sensor (106), and wheel rotation sensors (108). Where systematic offsets or drift are detected, the module adjusts sensor alignment parameters, latency filter coefficients, and NLKF tuning constants. In addition, historical driving data is stored and processed using adaptive learning techniques that gradually refine calibration thresholds over time. By doing so, the module ensures that the system remains robust against sensor aging, gradual misalignment, and varying environmental conditions. This ongoing self-calibration avoids the need for manual service recalibration and enhances long-term deployment reliability.

[0032] The system also includes a self-diagnosis module (134) that ensures operational safety and reliability by continuously monitoring the quality of latency compensation. This module receives outputs from the steady-object from the Localization Module (124) and the moving-object from Object-tracking module (122). These outputs are compared against expected motion trajectories derived from the ego state generated by the NLKF. The module evaluates residual error metrics, covariance matrices, and confidence intervals to determine whether the rendered AR overlays remain within safe alignment bounds. If the monitored accuracy drops below a defined threshold, the module can automatically: (i) retune parameters in the self-calibration module (132); (ii)Atty Docket: BM 30 US Patent Applicationtransmit diagnostic alerts to the vehicle’s central ECU for service action; or (iii) temporarily disable or degrade AR overlay features to prevent driver distraction or misinformation. In this way, the self-diagnosis module (134) provides a fail-safe mechanism that protects against misleading or unsafe augmented visualizations, thereby reinforcing the trustworthiness of the HUD.

[0033] The invention is designed for seamless integration with modem automotive electronic architectures. The data from Sensors Array (100) flows between modules via standardized automotive communication interfaces, such as Controller Area Network (CAN) bus or automotive Ethernet. This ensures compatibility with advanced driver assistance systems (ADAS), semi-autonomous driving controllers, and existing HUD display units. The modular software design allows deployment on either central electronic control units (ECUs) or on dedicated ADAS perception processors, depending on the manufacturer’s system design. This architectural flexibility promotes cross-platform compatibility and scalability for mass production, enabling adoption across a wide range of vehicle platforms.

[0034] The architecture described provides multiple advantages over existing AR HUD solutions: real-time predictive compensation using NLKF and kinematic models, ensuring accurate AR overlays even under highly dynamic vehicle maneuvers; automated self-calibration that maintains performance over the lifetime of the vehicle, eliminating reliance on manual recalibration; built-in self-diagnosis safeguards that identify sensor degradation or algorithmic failures and respond through adaptive compensation or fail-safe disabling; cost-effective, software-centric design that leverages advanced algorithms instead of requiring prohibitively expensive, high-speed hardware upgrades; and scalable, modular integration that supports straightforward deployment in massmarket vehicles without disrupting existing sensor and HUD architectures.

[0035] The system integrates multi-sensor fusion, Non-linear Kalman fdtering, kinematic modeling, self-calibration, and self-diagnosis into a closed-loop architecture. This design achieves consistent and accurate alignment of augmented visuals with real -world objects, even under challenging driving conditions. Moreover, as further illustrated in Figure 2, the invention directlyAtty Docket: BM 30 US Patent Applicationaddresses the problem of photon-to-photon latency by explicitly measuring and compensating delays. Together, Figures 1 and 2 illustrate a complete solution that is accurate, safe, adaptive, and production-ready for next-generation automotive AR HUDs.

[0036] As shown in Figure 1, the sensor array (100) collects raw inputs: imagery from the ADAS camera (102), position and attitude from GPS (103), acceleration and angular velocity from the IMU (104), steering data from the steering angle sensor (106), and speed / rotation data from the wheel sensors (108). These signals are fused by the ego-localization NLKF (124) into an ego-state estimate representing vehicle position, orientation, and motion. The self-calibration module (132) continuously fine-tunes these inputs, while the filter parameter dynamically updates the weighting factors used by downstream filters. The steady-object filter and moving-object filter from object tracking module (122) then process this data to predict the positions of static features (e.g., lanes, signs) and dynamic entities (e.g., vehicles, pedestrians). The self-diagnosis module (134) crossverifies these predictions, feeding back adjustments where necessary. Final validated outputs are passed to the Display Manager (129), which generates augmented overlays for the HUD (130).

[0037] The ADAS camera (102) provides high-resolution visual imagery of the environment, enabling real-time object detection and classification of traffic signs, lane markings, vehicles, and obstacles. The GPS (103) regularly provides ego vehicle position, attitude and their accuracy. The IMU (104) delivers continuous motion data in the form of linear accelerations and angular velocities. The steering angle sensor (106) supplies steering input, critical for predicting directional changes, while the wheel rotation sensors (108) yield wheel speed and rotational dynamics, improving longitudinal motion estimates. Together, these complementary signals are fused to provide a highly accurate and dynamic model of the vehicle’s real-time state.

[0038] The latency compensation module (126) is responsible for quantifying and mitigating delays between real-world object motion and AR display projection. As illustrated in Figure 2, the overall photon-to-photon latency (Atptp) is measured using synchronized timestamps from sensor acquisition and rendering pipeline outputs. NLKF-based prediction, in combination with theAtty Docket: BM 30 US Patent Applicationvehicle kinematics model, estimates how both the ego-vehicle and external objects have moved during At. The corrected positions are then delivered to the rendering pipeline (129-130), ensuring that augmented overlays remain aligned with real-world features. This predictive correction minimizes perceived lag, prevents motion sickness, and preserves driver trust in the system.

[0039] As illustrated in Figure 2, the system measures and compensates for a photon-to-photon latency interval (Atptp) that arises between sensor capture and visual rendering on the augmented reality head-up display (AR HUD). This interval represents the total time required for image acquisition, signal conditioning, sensor fusion, processing by the extended Kalman filter (NLKF) 126, and final transmission through the rendering pipeline. Because Atptp can vary dynamically with processing load, sensor update rates, and communication delays, the system continuously monitors and updates the measured latency rather than assuming a static value.

[0040] The latency period Atptp is a composite delay that originates from multiple sources: (i) sensor frame rate limitations, particularly with video, radar or LiDAR sensors and their fusion from ADAS module; (ii) analog-to-digital conversion and buffering delays; (iii) computational latency within the central electronic control unit (ECU) or dedicated graphics processor; and (iv) communication latency associated with transmitting sensor data across in-vehicle networks such as CAN, FlexRay, or automotive Ethernet. Each of these contributions may vary with temperature, network load, or system resource contention. Consequently, AtPtPis characterized as a stochastic variable rather than a fixed constant.

[0041] During Atptp, the relative position of tracked objects may change significantly due to either object motion (e.g., another vehicle crossing an intersection) or ego-motion of the host vehicle (e.g., turning or braking). Without correction, the AR HUD overlay would be rendered at an outdated position, resulting in visual misalignment and potential driver confusion. To address this, the system employs the non-linear modification of Kalman filter (NLKF) 126 in conjunction with a kinematics model of the host vehicle to predict the displacement of objects during the measured Atptp. The NLKF integrates sensor data (radar, camera, inertial measurement unit) with motionAtty Docket: BM 30 US Patent Applicationmodels to extrapolate the object’s future state vector (position, velocity, orientation) at time t + At. The corrected coordinates are then forwarded to the rendering subsystem.

[0042] In one implementation, the NLKF state vector xtincludes at least the position, velocity, and orientation of detected objects, while the system model accounts for vehicle kinematics and object dynamics. The prediction step estimates xt+&t= f xt, ut) + wt, where f(.) represents the kinematic model, utare control inputs (e.g., steering angle, wheel speed), and wtis process noise. Measurement updates from sensors are fused using the observation model zt= h xt) + vt, where vtrepresents measurement noise. By explicitly propagating the state vector forward by At, the NLKF generates latency-compensated predictions that align with the time of display.

[0043] The latency monitoring and prediction function may be distributed across hardware and software components. A hardware timer or dedicated timestamping module may capture frame arrival times and rendering events, enabling precise At measurement at sub-millisecond resolution. Prediction logic, including the NLKF, may be executed on a real-time ECU or an embedded GPU depending on computational requirements. In some embodiments, a low-level microcontroller performs coarse latency measurement, while higher-level processors carry out predictive state estimation and rendering synchronization.

[0044] The system dynamically adapts to fluctuating latency conditions. For example, if the rendering pipeline is temporarily slowed by concurrent graphics tasks (e.g., dashboard animations), the measured At is immediately updated and fed into the NLKF prediction horizon. Likewise, if the network load increases (e.g., burst of messages on automotive Ethernet), the system increases the prediction interval accordingly. This adaptive approach ensures that overlays remain spatially coincident with real -world objects despite transient delays.

[0045] Because prediction inherently involves uncertainty, the system also computes confidence bounds on the predicted object position. The NLKF covariance matrix provides an estimate of positional uncertainty, and overlays may be adaptively rendered with transparency, reduced size,Atty Docket: BM 30 US Patent Applicationor damped motion when confidence falls below a predetermined threshold. Such adaptive rendering reduces the risk of perceptual instability or visual jitter. In one embodiment, confidence-weighted blending is used, whereby predicted positions are averaged with sensor updates once they arrive, thereby smoothing transitions and preventing abrupt overlay jumps.

[0046] This predictive correction significantly reduces perceived visual lag, especially during rapid ego-motion such as lane changes, sudden braking, or tight cornering. By mitigating spatial lag between overlays and their corresponding real -world objects, the system reduces the onset of simulator sickness or motion-induced nausea, which are known issues in AR / VR contexts. Furthermore, accurate prediction enables reliable presentation of navigation cues, hazard warnings, and object annotations even under high-speed driving conditions, enhancing both safety and driver confidence.

[0047] While Figure 2 emphasizes predictive correction for AR HUD overlays, the same latency measurement and prediction subsystem may be applied to additional functions, including driver monitoring displays, advanced driver-assistance system (ADAS) feedback loops, or cooperative vehicle-to-vehicle (V2V) communication. In such cases, predictive alignment ensures temporal consistency across subsystems and minimizes the effect of asynchronous data arrival.

[0048] Figure 3 illustrates a flowchart of a method (11) for reducing latency in augmented reality (AR) applications for vehicles. The method is executed in real time on a vehicle computing platform that may include one or more electronic control units (ECUs), a graphics processor, and a sensor fusion module. The method ensures that augmented obj ects proj ected on a head-up display (HUD) remain visually aligned with the driver’s real -world perspective, despite inherent sensor and display pipeline delays.

[0049] The process begins with a system initialization routine (301), during which sensors are synchronized to a common time base, calibration parameters are loaded, and the HUD rendering subsystem is primed. Clock synchronization, for example via Controller Area Network (CAN) busAtty Docket: BM 30 US Patent Applicationor Precision Time Protocol (PTP), ensures that subsequent sensor inputs are aligned with submillisecond accuracy.

[0050] In a first step (303), the method receives and synchronizes real-time inputs from a plurality of onboard sensors. These sensors include an advanced driver assistance system (ADAS) camera providing forward-facing image frames, an inertial measurement unit (IMU) delivering acceleration and angular velocity signals, a steering angle sensor, and one or more wheel rotation sensors providing wheel speed and displacement data. Each sensor stream is timestamped at acquisition, filtered for noise, and normalized into a common data structure. Optional additional sources, such as GPS or odometry sensors, may also be fused into this input layer. The output of this step is synchronized, pre-processed sensor packets aligned to a common time reference.

[0051] Next, the method processes the sensor data in an object tracking module (305) to detect features and objects within the vehicle’s environment. From the ADAS camera, lane markings, vehicles, pedestrians, and infrastructure cues are identified, while IMU and wheel data provide additional context for motion cues. Objects are associated across consecutive frames and tracked using dedicated filters, maintaining both the identity and state of each object. The system differentiates between steady objects, such as lane lines or road signs, and moving objects, such as vehicles or pedestrians, since these categories require distinct predictive compensation strategies. The output of this step is a structured list of detected and tracked objects including class, position, velocity, and confidence values.

[0052] In a subsequent step (307), the method estimates the real-time position and orientation of the vehicle. A localization module integrates data from the IMU, steering angle sensor, wheel sensors, and optionally GPS to determine the ego vehicle’s position and orientation. Fusion is performed using a non-linear modification of Kalman Filter (NLKF, such as EKF or UKF) that models vehicle kinematics. The module outputs a fused ego-state including position, heading, roll, pitch, yaw rate, and velocity. These ego-states serve as the reference against which object positions are interpreted and projected for AR rendering.Atty Docket: BM 30 US Patent Application

[0053] The method on the next step (309) measures a latency delay (Atptp), defined as the time difference between sensor acquisition and HUD display output. Latency sources may include image capture, signal processing, prediction, and rendering delays. Once Atptp is determined, the NLKF and vehicle kinematics model are used to forward-propagate both the ego vehicle state and tracked object states over the latency interval. This results in a predicted “future” configuration of objects as they will appear at the precise moment of display. The output of this step is predicted vehicle and object positions advanced to the display time horizon Atptp.

[0054] Using the predicted states, the method applies a predictive latency compensation algorithm (311). This algorithm corrects for displacement between augmented objects and their real-world counterparts by projecting their anticipated positions into the HUD coordinate frame. For moving objects, prediction incorporates both ego-vehicle motion and object-specific motion models. For steady references such as lanes, compensation relies primarily on ego-vehicle propagation. In some embodiments, multi-stage filtering supplements NLKF predictions with neural network refinements for complex or highly dynamic conditions.

[0055] The latency-compensated coordinates are then provided to a rendering module (313), which generates augmented overlays projected onto the HUD combiner glass. Visuals are aligned in perspective with the driver’s forward view and updated in real time at the HUD’s refresh rate. To maintain smooth visual continuity, the rendering module may apply an adaptive refresh rate mechanism, ensuring that AR elements remain synchronized with vehicle speed and motion dynamics.

[0056] At predetermined intervals, the method executes a self-calibration process (315). This process compares predicted versus observed positions of steady reference objects, such as lane markings, and adjusts calibration parameters accordingly. The module dynamically corrects for sensor misalignment, IMU drift, or temperature-induced performance variations. Filter tuningAtty Docket: BM 30 US Patent Applicationparameters, such as NLKF process and measurement noise covariance, are adaptively updated to sustain long-term reliability without requiring manual recalibration.

[0057] The method further performs a self-diagnosis operation (317) to evaluate the accuracy of the latency compensation process. A confidence score is generated based on residuals, reprojection error, and system consistency checks. If the score falls below a first threshold, certain non-critical AR overlays may be suppressed. If it falls below a stricter threshold, all AR overlays may be disabled to prevent misaligned or misleading projections. Diagnostic results may be logged for service and safety reporting.

[0058] The process ends (319) when the AR HUD system is disabled, but under normal operation, the steps of the method repeat continuously, synchronized with the vehicle’s sensor sampling and display refresh rates. This ensures a continuously updated, latency-compensated AR experience that remains robust even under rapidly changing driving conditions.

[0059] It will be appreciated that the foregoing description is illustrative and not restrictive. Many variations of the invention will become apparent to those of ordinary skill in the art upon review of the disclosure. The scope of the invention should, therefore, be determined with reference to the appended claims.

Claims

Atty Docket: BM 30 US Patent ApplicationWhat is claimed is:

1. A system for reducing latency in augmented reality (AR) applications for vehicles, the system comprising:• a plurality of sensors comprising at least an advanced driver assistance system (ADAS) camera, an inertial measurement unit (IMU), and one or more vehicle dynamics sensors, the sensors being configured to collect image, motion, and vehicle dynamics data;• a localization module configured to fuse the sensor data using a non-linear Kalman filter (NLKF) and a vehicle kinematics model to estimate a real-time ego state of the vehicle;• a latency compensation module configured to predict and correct motion-induced delays by applying NLKF-based prediction and kinematic modeling;• a self-calibration module configured to adaptively adjust sensor offsets, filter parameters, and NLKF settings to maintain long-term system accuracy; and• a rendering module configured to project latency-compensated augmented reality objects in real time onto a head-up display (HUD) in alignment with real-world objects.

2. The system of claim 1, wherein the plurality of sensors comprise an advanced driver assistance system (ADAS) camera, an inertial measurement unit (IMU), and one or more vehicle dynamics sensors, and wherein the plurality of modules include an object tracking module, a localization module, and a latency compensation module configured to apply a non-linear Kalman filter (NLKF) together with a vehicle kinematics model, and further comprising a rendering module configured to project augmented objects in real time onto the head-up display (HUD) and a self-calibration module configured to maintain long-term system accuracy.

3. The system of claim 2, wherein the plurality of sensors further comprises a steering angle sensor and one or more wheel rotation sensors, and wherein the system further comprises a selfdiagnosis module configured to monitor latency compensation accuracy and provide error detection and corrective feedback.Atty Docket: BM 30 US Patent Application4. The system of claim 3, wherein the latency compensation module employs a multi-stage filtering approach comprising non-linear Kalman filter (NLKF) processing in combination with a neural network-based motion prediction model to improve accuracy under dynamic driving conditions.

5. The system of claim 3, wherein the self-calibration module is configured to dynamically adjust sensor alignment parameters to compensate for drift arising from temperature variation, road surface conditions, or sensor degradation over time.

6. The system of claim 3, wherein the rendering module applies an adaptive refresh rate control mechanism configured to synchronize projection of augmented objects on the HUD with real-world motion across varying vehicle speeds.

7. The system of claim 3, wherein the self-diagnosis module generates an error confidence score representing reliability of latency compensation, and wherein the system is configured such that, if the score falls below a predefined threshold, the self-diagnosis module selectively disables or modifies augmented reality functionalities to prevent misaligned or erroneous display output.

8. The system of claim 3, wherein the localization module integrates global positioning system (GPS) data with inertial measurement unit (IMU) and wheel sensor inputs to refine vehicle position estimation in environments with degraded satellite reception, including tunnels and dense urban areas.

9. The system of claim 3, wherein the rendering module prioritizes display of critical driving information, including lane boundaries, lead vehicle position, and traffic signs, and selectively defers non-critical AR content during periods of high computational load to ensure real-time responsiveness.Atty Docket: BM 30 US Patent Application10. The system of claim 3, wherein the self-diagnosis module is further configured to transmit diagnostic alerts to a vehicle electronic control unit (ECU) via a controller area network (CAN) bus or automotive Ethernet interface, thereby enabling coordinated safety responses within the vehicle architecture.

11. A method for reducing latency in augmented reality (AR) applications for vehicles, the method comprising:• receiving real-time sensor data from an advanced driver assistance system (ADAS) camera, an inertial measurement unit (IMU), a steering angle sensor, and one or more wheel rotation sensors;• detecting and tracking objects within the vehicle’s environment by processing the sensor data in an object tracking module;• estimating a real-time vehicle position and orientation using a localization module that integrates data from the ADAS camera, the IMU, the steering angle sensor, and the wheel rotation sensors;• measuring a latency delay (At) by synchronizing sensor timestamps and pipeline outputs, and estimating displacement of objects during At using a non-linear fdter (NLKF) and a vehicle kinematics model;• predicting and compensating for the latency delay by applying a predictive algorithm that corrects misalignment between augmented visuals and real-world objects;• rendering latency-compensated augmented visuals in real time onto a head-up display (HUD) using a rendering module;• executing a self-calibration process at predetermined intervals, wherein calibration adjusts sensor alignment and fdter parameters based on detected discrepancies between predicted and actual object positions; and• performing a self-diagnosis operation that monitors accuracy of the latency compensation, generates an error confidence score, and modifies or disables augmented reality functions when the confidence score falls below a threshold.Atty Docket: BM 30 US Patent Application12. The method of claim 11, wherein the latency compensation algorithm applies an Non-linear Kalman Filter (NLKF) configured to jointly estimate ego-vehicle motion and predict positions of moving objects during the photon-to-photon latency period (Atptp).

13. The method of claim 11, wherein the self-calibration process dynamically adjusts sensor alignment parameters and Non-linear Kalman Filter (NLKF) tuning coefficients based on discrepancies between predicted and observed object trajectories, thereby compensating for sensor drift, thermal variations, or aging effects.

14. The method of claim 11, wherein the self-diagnosis analysis generates a confidence score representing the accuracy of latency compensation and selectively disables or adapts augmented reality functions when the score falls below a predefined threshold.15 The method of claim 11, wherein the rendering module applies an adaptive refresh rate that varies as a function of estimated latency delay (AtPtP) and vehicle speed, thereby synchronizing AR projection with real-world dynamics under varying driving conditions.16 The method of claim 11, wherein the localization module integrates GPS data with IMU and wheel sensor inputs to refine position estimation in areas with degraded satellite coverage, including tunnels, parking garages, and urban environments.

17. The method of claim 11, wherein object tracking employs a multi-stage approach comprising initial feature detection from camera input followed by non-linear Kalman filter-based temporal tracking of the detected features.

18. The method of claim 11, wherein the predictive latency compensation algorithm further comprises a neural network module trained on historical driving data to enhance prediction accuracy in complex maneuvers such as lane changes or emergency braking.Atty Docket: BM 30 US Patent Application19. The method of claim 11, wherein the system logs calibration and diagnosis data over time and applies adaptive thresholds derived from machine learning models to continuously improve latency compensation accuracy.

20. The method of claim 11, wherein:• the self-diagnosis analysis transmits diagnostic alerts to a vehicle electronic control unit (ECU) via a controller area network (CAN) or automotive Ethernet communication interface; and• the rendering module prioritizes and selectively displays augmented visuals corresponding to safety-critical objects, including leading vehicles, road hazards, or traffic signals, when system resources are constrained.

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