Ai-driven adaptive projection system for dynamic environmental safety and communication

Abstract

A projection system and method for enhancing safety utilizes an artificial intelligence (Al) processing unit, a sensor suite including LIDAR and cameras, and one or more laser or LED projection units. The Al processing unit analyzes real-time data from the sensor suite to dynamically command the projection units to project adaptive visual notifications in response to environmental changes. In a preferred embodiment, the system is configured as a vehicle-mounted unit for a service vehicle, such as a sanitation truck. This embodiment autonomously manages traffic by projecting directional cues for other vehicles and may further include a worker protection subsystem for wirelessly transmitting alerts to a haptic wearable device worn by a worker upon detection of a threat. Applications extend from general roadway management to specific industrial, residential, and emergency response scenarios.

Description

[0001] AI-DRIVEN ADAPTIVE PROJECTION SYSTEM FOR DYNAMIC ENVIRONMENTAL SAFETY AND COMMUNICATION

[0002] FIELD OF THE INVENTION

[0003]

[0001] The present invention relates generally to the field of intelligent safety systems. More specifically, the invention pertains to systems and methods utilizing adaptive visual projections, driven by artificial intelligence and real-time sensor data, to enhance safety and communication in dynamic environments, including a specific application for vehicle-mounted traffic management and worker protection.

[0004] [2] BACKGROUND OF THE INVENTION

[0005] [3] The field of the invention relates to intelligent projection systems. Projection systems utilizing laser and Light Emitting Diode (LED) components have become increasingly prevalent in various sectors, including traffic management on roadways, safety monitoring in residential areas, and operational guidance in industrial settings. However, conventional systems are often limited in their capabilities, typically relying on static or pre-programmed projections. A significant deficiency in the prior art is the lack of an intelligent fusion of multi-source projection technologies - such as multi-wavelength lasers and LEDs - with a continuous learning Artificial Intelligence (Al) capable of dynamically adapting visual notifications based on a real-time analysis of complex sensor data. The field of projection systems has seen significant advancements, particularly with the integration of laser and LED technologies in various sectors such as traffic management, residential safety, and industrial applications. These systems traditionally rely on static or pre-programmed projections that provide basic illumination or signals to users but lack adaptive capabilities to respond to dynamic environments and dynamic situations. Furthermore, traditional systems may struggle with energy efficiency and may not offer adequate customization to meet specific safety needs in diverse applications.

[0006] [4] A particularly acute and unsolved problem exists in the field of urban roadway safety, specifically concerning slow-moving or frequently stopping sendee vehicles, such as municipal sanitation trucks, maintenance fleets, and infrastructure vehicles. These vehicles, by their operational nature, create temporary and dynamic obstructions in active traffic lanes. This scenario poses a significantand well-documented risk to field workers who operate on foot in the vehicle's vicinity, placing them in constant peril from passing traffic. Indeed, the occupation of sanitation worker is consistently ranked among the most hazardous due to such vehicle-related accidents. Current safety protocols for these situations predominantly rely on passive or static visual warnings, such as flashing strobe lights or reflective decals. These static measures are often insufficient to actively manage traffic flow and fail to provide approaching drivers with clear, dynamic guidance, leading to confusion and delayed reactions.

[0007] [5] Existing projection systems are often limited in their ability to react to changing environmental conditions. These systems typically lack real-time processing and intelligence to discern and adapt to evolving scenarios, such as sudden changes in traffic patterns or unexpected household hazards. Current technologies may utilize simple detection mechanisms but do not provide the advanced prediction and adaptive learning required to maintain high safety standards in real-time scenarios. For instance, traffic signal systems or emergency alert systems might function independently without the capability to integrate environmental data in a meaningful way that allows for instantaneous risk assessment and alert dissemination. Moreover, the prior art in supervised safety systems rarely integrates a comprehensive suite of sensors capable of monitoring extensive environmental variables, such as temperature fluctuations or the presence of smoke. These systems often do not employ machine learning algorithms to improve their operation over time through adaptive learning. The lack of environmentally responsive features, such as anomaly detection and real-time data analysis, poses significant challenges in ensuring optimal protection and user interaction in both residential and public settings by lack of ability to produce effective visual communication. What is needed is a projection system that incorporates intelligent, adaptive decision-making processes capable of responding dynamically to environmental changes. Such a system would utilize real-time data analysis and machine learning to predict potential risks and prioritize safety alerts, thus enhancing safety and efficiency. The system should provide customizable solutions that accommodate specific safety requirements for various applications including public safety, home monitoring, and industrial and commercial use. By addressing these deficiencies with a combination of advanced sensor integration, adaptive learning capabilities, and efficient energy use, it would fulfill the need for a more effective responsive and environmentally effective visual communication by led and laser projection system.[6] There is, therefore, a long-felt but unsolved need for a vehicle-mounted, autonomous system capable of actively and intelligently managing traffic in real-time around a service vehicle, thereby creating a dynamic safety envelope for workers without reliance on external, fixed infrastructure. Such a system must move beyond mere passive alerts to provide proactive, intelligible guidance to other road users, effectively transforming the service vehicle itself from a static hazard into a dynamic traffic management node. This necessitates an intelligent fusion of real-time environmental sensing with adaptive visual projection technologies, capable of analyzing complex scenarios and communicating clear, unambiguous instructions to prevent accidents and protect human lives.

[0008] DESCRIPTION OF THE PRIOR ART

[0009] [7] To provide a comprehensive context for the present invention, the following discussion addresses several documents that illustrate various approaches within the fields of safety lighting, traffic management, and projection systems. While these documents disclose certain relevant technologies, they also highlight the technological gaps and unsolved needs that are addressed by the embodiments disclosed herein. The subsequent analysis of each document is intended to clarify the points of departure and distinct contributions of the present invention in relation to the existing art.

[0010] [8] US10665095B2, titled " Pedestrian safety lighting device and system", inventor Robert York, provides a lighting system to enhance pedestrian safety. In contrast, the present system achieves novelty by utilizing advanced Al processing for dynamic hazard projection and real-time adaptability, significantly expanding beyond simple lighting to include interactive and adaptive interfaces.

[0011] [9] IN202341080313A, titled " An loT-enabled adaptive traffic light system for pedestrian safety and a method for the same", inventor Dr. S. Raja, describes an loT traffic light system. The present technology is patentable due to its comprehensive adaptive projection capabilities, interactive interfaces, and integration of machine learning, allowing for sophisticated environmental and hazard responsiveness that is not within the scope of IN202341080313A.

[0010] US10755562B2, titled " Traffic sign and system for increasing awareness of the same", inventor Renato Martinez Openiano, involves traffic signs for improving visibility. The present system introduces an innovative integration of Al and adaptable projection technology, offering a dynamic, interactive interface and comprehensive environmental adaptability that distinguishes it from static sign systems like those in US10755562B2.

[0012]

[0011] US9129526B2, also under Jonathan Y. Walther, provides a comprehensive traffic management methodology. The present technology distinguishes itself by leveraging Al and adaptive laser / LED projections to dynamically interact and respond with its environment, a novelty absent from the static models in US9129526B2.

[0013]

[0012] US20150145698A1, titled " Road and path lighting system", inventor Walter Werner, proposes a lighting approach for roads. The present system's patentability arises from its Al-driven adaptive projections providing real-time situational responsiveness, vastly expanding beyond the scope of fixed lighting solutions in US20150145698A1.

[0014]

[0013] US20220146066A1, titled " Flow management light", inventor Steven Rosen, describes lighting to manage traffic flow. The novel approach of the claimed system lies in its Al-enabled projections that adapt holistically to diverse environments, something the flow-focused control in US20220146066A1 does not encompass.

[0015]

[0014] IN202341066028A, titled " Smart traffic light control system using loT intelligence", inventor Asha M. Raikar, innovates in loT traffic control. However, the differentiation of the hereto claimed technology lies in real-time adaptable projection techniques that go beyond traffic lights to offer comprehensive safety enhancements through integrated Al-driven learning and real-time data analysis.

[0016]

[0015] US11651682B2, titled " Predictive traffic management system", inventor Roy Prannoy, focuses on traffic prediction. The current technology's bespoke novelty stems from its real-time Al-driven projections providing adaptive safety alerts, effectively moving ahead of mere predictive analytics captured in US 11651682B2.

[0017]

[0016] US20170160094A1, " Vehicle-mounted projection method and system", inventor Yu Zhang, involves vehicle projection. The claimed technology is distinct owing to environmental adaptivityand comprehensive hazard detection through Al, surpassing the vehicular limit to cover broader spatial applications.

[0018]

[0017] US20240321090A1, titled " Method and system for providing blind spot warning to vehicles", inventor Behrooz Karimian, focuses on vehicle blind spots. The claimed system is patentable by utilizing real-time adaptive projections with Al integration, offering a holistic safety solution that outstrips targeted vehicular warnings in US20240321090A1.

[0019]

[0018] US20140267415A1, titled " Road marking illumination system and method", inventor Xueming Tang, provides illumination for road markings. The present invention introduces novel adaptive projections as interactive safety aids, which are more extensive than static illumination methods present in US20140267415A1.

[0020]

[0019] W02020076280A1, " Autonomous in-vehicle virtual traffic light system", inventors Elsheemy Mohamed Roshdy, centers on vehicle-based virtual lights. The novelty of the claimed system is in its Al and projection adaptability for diverse environments, making it more robust than the vehiclecentric focus of W02020076280A1.

[0021]

[0020] US9947219B2, titled " Monitoring of a traffic system", inventor Oliver Rolle, involves traffic system observation. Patentability is achieved through interactive, Al-enabled dynamic projections providing situational adaptability beyond mere monitoring capabilities in US9947219B2.

[0022]

[0021] US8819313B1, another " Traffic management system", inventor Jonathan Y. Walther, represents traditional traffic management approaches. The current patent's inventive step is its comprehensive system of interactive projections led by Al, providing dynamic adaptability not seen in the static methods of US8819313B1.

[0023]

[0022] US11056004B1, " Traffic signal system for congested trafficways", inventor Amanda Reed, focuses on traffic signals in congestion. The claimed system exhibits novelty through real-time dynamic Al-driven projections offering holistic environmental safety, a feature beyond static signaling.

[0024]

[0023] US10933807B2, titled " Visual hazard avoidance through an on-road projection system for vehicles", inventor Tynan J. Garrett, focuses on vehicle projections for hazard avoidance. Meanwhile, the inventive step in the new technology is in its encompassing environmental adaptivity tied to Al, expanding its utility beyond vehicular systems.

[0024] GB2457668A, " Traffic control system with display and integrated communications technology", inventor Prince Noah Davidson, outlines traffic displays. In contrast, the new technology encapsulates a real-time responsive system, integrating Al projection capabilities extending into environmental safety across multiple settings, surpassing display-based systems in scope.

[0025]

[0025] US7148813B2, titled " Light emitting traffic sign having vehicle sensing capabilities", inventor Frederick T. Bauer, involves light-emitting signage. The unique invention here involves Al and adaptive learning for dynamic projections beyond static light emissions, capturing situational nuances not addressed in US7148813B2.

[0026]

[0026] As demonstrated by the foregoing analysis, while individual elements of projection, sensing, and traffic control exist in isolation, the prior art does not teach or suggest the synergistic combination and holistic system architecture disclosed herein. The key distinction lies not merely in the inclusion of a single feature, but in the intelligent fusion of real-time, multi-source environmental data with an Al-driven decision-making engine that commands a dynamic and adaptive projection unit. This integrated approach transforms the system from a passive indicator into an active, responsive safety and management tool. The embodiments described in the present disclosure thus represent a significant technical advancement over the piecemeal solutions found in the existing art.

[0027] SUMMARY OF THE INVENTION

[0028]

[0027] The present disclosure is directed to systems and methods for enhancing safety and communication through adaptive visual projections. In one aspect, a projection system is provided, comprising at least one projection unit, such as a laser or LED projector, a sensor suite for monitoring an environment, and an artificial intelligence (Al) processing unit. The Al processing unit is configured to analyze real-time data from the sensor suite and, in response, dynamically command the projection unit to adjust visual notifications to correspond to detected environmental changes, thereby creating an intelligent, responsive safety system.

[0029]

[0028] The Al processing unit may be further configured to perform predictive adjustments based on historical data, optimize energy consumption based on ambient conditions, and execute emergency protocols to display critical alerts. The system may also be configured to receive input from a user interface, allowing for customization of the visual notifications, and may interpret detected usergestures as commands to alter the projections. Embodiments of the system may be configured for fixed installation on infrastructure such as a building facade, or to function as dynamic smart signage for applications like traffic guidance or parking status indication.

[0030]

[0029] In another aspect, a method for dynamically projecting safety indicators is provided. The method comprises gathering real-time environmental data using a sensor suite, analyzing the data with an Al processing unit to identify a specific condition or hazard, and commanding a projection system to generate an adaptive visual projection corresponding to the identified condition. The method may be applied in various contexts, such as projecting onto a roadway to guide traffic, into a residential environment to warn of household risks, or within an industrial site to delineate hazardous zones. The method may further include refining prediction models over time using machine learning and integrating with a network of adjacent systems for coordinated regional management.

[0031]

[0030] In a particularly advantageous embodiment, a vehicle-mounted safety system for managing traffic is disclosed. The system is configured to be mounted on a host vehicle, such as a service vehicle that stops frequently. The system's Al processing unit is configured to autonomously determine the operational state of the host vehicle by fusing data from sensors like GPS, an IMU, and the vehicle's CAN bus. In response to determining an operational state, such as a service stop, the AI processing unit automatically commands a projection unit to project a specific dynamic visual notification, such as a directional arrow, onto the road surface to guide approaching traffic safely around the host vehicle.

[0032]

[0031] The vehicle-mounted system may further comprise a worker protection subsystem. This subsystem includes a wearable device for a worker, which contains an alert mechanism such as a haptic vibrator. Upon the AI processing unit detecting a hazardous condition for the worker, such as an erratically approaching vehicle, it commands a wireless transmitter to send an alert signal to the wearable device, providing the worker with an immediate, tactile warning of the potential threat.

[0033] BRIEF DESCRIPTION OF THE DRAWINGS

[0034]

[0032] Figure 1 is a system block diagram illustrating the primary components of the intelligent projection system and their interconnections, in accordance with an embodiment of the present disclosure.

[0035]

[0033] Figure 2 is a flowchart illustrating a method for deploying the intelligent projection system for roadway safety, in accordance with an embodiment of the present disclosure.

[0034] Figure 3 is a flowchart illustrating a method for enhancing residential safety using the intelligent projection system, in accordance with an embodiment of the present disclosure.

[0036]

[0035] Figure 4 is a flowchart illustrating a method for managing safety on an industrial or construction site, in accordance with an embodiment of the present disclosure.

[0037]

[0036] Figure 5 is a flowchart illustrating a method for using the projection system for emergency management, in accordance with an embodiment of the present disclosure.

[0038]

[0037] Figure 6 is a flowchart illustrating a method for facility navigation and safety, in accordance with an embodiment of the present disclosure.

[0039]

[0038] Figure 7 is a flowchart illustrating a method for advanced traffic management, in accordance with an embodiment of the present disclosure.

[0040]

[0039] Figure 8 is a diagram illustrating a pedestrian detection scenario as analyzed by the vehiclemounted safety system, in accordance with an embodiment of the present disclosure.

[0041]

[0040] Figure 9 is a diagram illustrating a blocked overtaking path scenario as analyzed by the vehiclemounted safety system, in accordance with an embodiment of the present disclosure.

[0042]

[0041] Figure 10 is a diagram illustrating an oncoming vehicle (car / truck) scenario as analyzed by the vehicle-mounted safety system, in accordance with an embodiment of the present disclosure.

[0043]

[0042] Figure 11 is a diagram illustrating an oncoming two- wheel er scenario as analyzed by the vehiclemounted safety system, in accordance with an embodiment of the present disclosure.

[0044] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS DEFINITIONS

[0045]

[0043] Adaptive Learning: A process by which the system continually refines its understanding of patterns in traffic or household activities, adjusting projections to enhance safety in frequently changing or high-risk environments.

[0046]

[0044] Al Processing Unit: A component of the system employing machine learning algorithms to analyze real-time sensor data, facilitating intelligent, dynamic decision-making based on environmental conditions.

[0047]

[0045] Anomaly Detection: The capability of the AI processing unit to identify deviations from expected environmental patterns, signaling potential hazards or risks.

[0046] Dynamic Projection: The projection of laser or LED patterns that adapt in real-time to changing environmental conditions, used as navigational aids or danger alerts.

[0048]

[0047] Environmental Object Mapping: A method involving the use of simultaneous localization and mapping (SLAM) techniques to create a dynamic model of the environment, ensuring accurate projection alignment in varying conditions.

[0049]

[0048] Emergency Protocols: Procedures activated during critical situations, allowing the system to project urgent alerts and facilitate safe actions or evacuations.

[0050]

[0049] Energy Efficiency: The reduced energy consumption of the projection system achieved through laser technology and predictive management modules.

[0051]

[0050] Gestures: Movements or actions detected by sensors that serve as input mechanisms allowing the user to interact with the system seamlessly.

[0052]

[0051] LED Projection: A projection technology providing static or updated displays, employed where LED attributes are optimal for specific applications.

[0053]

[0052] LIDAR: A sensor technology that uses laser pulses to measure distances, aiding in environmental mapping and detection in the projection system.

[0054]

[0053] Lasers: Devices emitting beams of coherent light used in the projection system for precise mapping and pattern projection, contributing to detection and navigation functionalities.

[0055]

[0054] Predictive Modeling: The use of machine learning to anticipate future risks based on patterns in traffic flow or household activity, allowing the system to prioritize safety alerts accordingly.

[0056]

[0055] Real-Time Data Analysis: The continuous processing and evaluation of sensor data by the Al unit, forming situational models for accurate and immediate projection adjustments.

[0057]

[0056] Real-Time (Nano, Milli, and Second Scales): The capability of the system to process and respond to information within nanoseconds, milliseconds, or seconds, allowing for timely adjustments to environmental changes.

[0058]

[0057] Sensors: Devices including LIDAR, cameras, and smoke detectors used to monitor environment parameters like movement, temperature, and smoke presence to inform the projection system.

[0059]

[0058] User Interface: A mobile or web application component enabling interaction with the system for real-time updates, manual control, and customization of projection settings.

[0059] User Customization and Interaction: The ability for system users to adjust projection settings and provide feedback through the interface, aiding in personalizing system behavior and responses.

[0060]

[0060] " About": A measure being up to 25% greater than or lower than the defined value.

[0061]

[0061] SYSTEM OVERVIEW AND Al FUNCTIONALITY

[0062]

[0062] The systems and methods disclosed herein provide for an intelligent projection system comprising a synergistic integration of several key subsystems: at least one projection unit (e.g., laser or LED), a sensor suite for environmental perception, and an Al processing unit that serves as the control center. The Al processing unit is configured to receive and analyze real-time data from the sensor suite and, in response, dynamically command the projection unit to project or adjust visual notifications. This creates a closed-loop system that actively and intelligently responds to its environment, rather than relying on pre-programmed or static displays.

[0063]

[0063] The Al processing unit is configured to integrate machine learning models with the projection technologies. This enables the system to process real-time environmental data collected from the sensor suite, which may include LIDAR, cameras, and other sensors. The Al processing unit analyzes this data to generate context-aware visual cues and alerts. For example, in a traffic application, the AI processing unit can command the projection of modified traffic signals or lane markings dynamically based on current traffic loads. Similarly, in a residential context, the system's ability to project alerts related to household safety, such as for unattended appliances, results from this Al-driven sensor data fusion.

[0064]

[0064] In some embodiments, the AI processing unit is configured to facilitate anticipatory actions rather than merely reactive ones. The system may leverage machine learning algorithms to discern patterns and predict future conditions. For instance, by analyzing historical and real-time traffic data, the Al can predict the formation of congestion and preemptively project modified lane guides to smooth traffic flow. This predictive modeling allows the system to prioritize safety alerts and guidance based on anticipated risks.

[0065]

[0065] To support accurate projection alignment and environmental awareness, the system may be configured to perform environmental object mapping. In some embodiments, the AI processing unit uses techniques such as simultaneous localization and mapping (SLAM) to create andmaintain a dynamic model of the surrounding environment. This ensures that projections are accurately aligned with surfaces and objects, even as conditions change or the system itself moves, as in the case of a vehicle-mounted embodiment.

[0066]

[0066] The system may further be configured for adaptive learning and self-optimization. The Al processing unit can incorporate feedback to refine its operational models over time. This feedback may be derived from user input via an interface or from analyzing the outcomes of its own projections (e.g., measuring whether a projected traffic cue resulted in a change in vehicle behavior). In some embodiments, the system may be configured to recognize user gestures, detected by the sensor suite, as a form of real-time input to control or modify the projections.

[0067]

[0067] ADDITIONAL APPLICATIONS AND ALTERNATIVE EMBODIMENTS

[0068]

[0068] The disclosed system's versatility enables its application across numerous domains. The following descriptions are illustrative of various alternative embodiments and are not intended to be limiting.

[0069]

[0069] In a healthcare or emergency response embodiment, the system may be configured to assist patients and visitors in navigating large hospitals by projecting pathways to specific departments. In an emergency, the system may analyze environmental data from sensors (e.g., smoke detectors) to mark the safest escape routes in real-time.

[0070]

[0070] In an education or training embodiment, the system may be used in interactive classrooms to project visual cues for students, or in professional training settings to visually highlight specific tools, instruments, or hazards during hands-on exercises.

[0071]

[0071] In an animal training embodiment, the system may be configured to project light signals onto a surface. The light signals may be used in conjunction with a sensor worn by an animal, such as a vibrating sensor, that activates when the projected light is incident upon it, thereby providing a tactile cue to direct the animal during training sessions, such as for search and rescue or agricultural herding tasks.

[0072]

[0072] In a retail or logistics embodiment, the system may be deployed in a warehouse to guide workers to specific inventory items by projecting a path on the floor, thereby optimizing retrieval time. In a retail setting, it may project paths to guide shoppers to desired items or departments.

[0073] In a public safety or law enforcement embodiment, the system may be configured to manage crowds in public events or transportation hubs by projecting dynamic visual indicators to direct foot traffic. The system may also integrate with surveillance systems to visually highlight suspicious activities or individuals, thereby aiding security personnel.

[0073]

[0074] In an urban planning or infrastructure development embodiment, the system may be used on construction sites to dynamically mark safe zones and danger areas. As part of a smart city infrastructure, the system can be integrated to adaptively manage pedestrian and vehicle flows during peak hours or special events.

[0074]

[0075] In an agriculture or environmental monitoring embodiment, the system may be used for precision farming by projecting markers onto specific areas of a field that require irrigation, fertilization, or pest control based on real-time data. For wildlife conservation, it may be used to visually identify and track animals or mark areas of interest.

[0075]

[0076] In a recreational or entertainment embodiment, the system may be deployed in theme parks or sports arenas to guide visitors by projecting real-time directions to attractions, seats, or concessions, thereby managing crowd flow and enhancing the visitor experience.

[0076]

[0077] In a military or defense embodiment, the system may be used for battlefield navigation by projecting secure paths and potential threats in real-time. For training exercises, it may project adaptive visual markers to simulate real-world scenarios.

[0077]

[0078] In a further embodiment, the system is specifically adapted for enhancing operational safety on offshore oil and natural gas platforms, where harsh weather, complex machinery, and hazardous materials create a uniquely challenging environment. In this configuration, ruggedized and corrosion-resistant projection units are mounted on high structures, such as the drilling derrick, crane booms, and along catwalks. The sensor suite is augmented with specialized sensors, including anemometers for high-wind detection, hydrocarbon gas detectors, and load sensors integrated with the platform's cranes. The Al processing unit is trained to recognize the kinematic signatures of heavy equipment in operation, such as the movement of the top drive or the slewing of a crane.

[0078]

[0079] Based on this real-time analysis, the system projects dynamic safety zones directly onto the platform's deck. For example, when a crane is lifting a heavy container, the AI processing unittracks the load's position and projects a high-visibility, pulsating "red zone" on the deck directly beneath it, with the projected zone moving in real-time with the suspended load. This provides an unambiguous and intuitive warning to ground personnel, far surpassing the limitations of static painted markings. Furthermore, upon integration with the platform's central alarm system, a gas leak or fire detection automatically triggers an emergency protocol, commanding all projection units to display illuminated, directional evacuation pathways leading personnel to muster stations or lifeboat locations, with the Al dynamically rerouting these paths to avoid the detected hazard. The system can also project alignment guides and standoff warnings onto the sea surface to assist supply vessels during difficult mooring operations in rough seas. As a further application within this embodiment, the system can be configured as a ground-based visual support system for helicopter landing operations on the platform's helideck. In this configuration, it provides adaptive visual projections for dynamically marking the touchdown and lift-off area (TLOF), indicating rotor blade clearance zones based on the helicopter's size, and displaying real-time deck status indications, such as warnings for foreign object debris (FOD) detected by the sensor suite. This functionality serves as a supplemental safety enhancement that complements, but does not replace, existing certified aviation lighting and helideck guidance systems.

[0079]

[0080] As an additional offshore embodiment, the system may be configured as a ground-based visual support system for helicopter landing operations on offshore platforms, providing adaptive visual projections for marking clearance zones, safety boundaries, and deck status indications, while complementing existing certified aviation and helideck systems.

[0080]

[0081] In an assistive technology embodiment, the system may be configured to guide visually impaired individuals. This may be achieved by projecting focused light signals that activate vibrating sensors on a cane or wearable device to help the user navigate complex environments like crosswalks.

[0081]

[0082] In a personalized device illumination embodiment, the system's processor may be configured to control lighting on a mobile device, adjusting brightness, color, and patterns dynamically based on user settings and ambient environmental conditions to enhance usability.

[0082]

[0083] In some embodiments, the system is configured to enhance user interaction and customization.

[0083] The system may include a user interface with options for voice command input. The Al processing unit may be further configured to refine its models based on user feedback, enabling a bi-directional interaction that enhances the adaptability and personalization of the system based on specific user behaviors and preferences.

[0084]

[0084] In some embodiments, the system is configured for energy efficiency and sustainability. The processing unit may include a predictive energy management module that optimizes power consumption. The system may optionally be powered by or supplemented with a renewable energy source, such as a solar power unit, to support sustainable operation.

[0085]

[0085] SYSTEM COMPONENTS AND ALTERNATIVE CONFIGURATIONS

[0086]

[0086] The projection system may be configured with various components and functionalities, as described in the following illustrative embodiments.

[0087]

[0087] AI Processing Unit and Control Logic

[0088]

[0088] In some embodiments, the AI processing unit is configured to prioritize data inputs based on predefined criteria to enhance processing efficiency and system responsiveness. For example, data indicating a high-speed collision risk may be prioritized over data related to ambient temperature.

[0089]

[0089] The Al processing unit may be configured to operate with minimal human intervention. It may also be configured to integrate with third-party software platforms for extended capabilities. For system maintenance and health, the AI processing unit may incorporate a real-time monitoring module and be maintainable through periodic firmware updates.

[0090]

[0090] To support continuous improvement, the AI processing unit may integrate machine learning algorithms that are continuously trained with historical data. To protect data, the system may support encrypted communication channels.

[0091]

[0091] In some embodiments, the AI processing unit may include a predictive analysis tool for forecasting system demand, for example, predicting traffic patterns at certain times of day. The processing architecture may be configured to be energy-efficient to minimize power consumption.

[0092]

[0092] Projection Subsystem

[0093]

[0093] The at least one projection unit may comprise a laser projector and an LED projection unit configured to operate in a synchronized manner to provide a comprehensive visual display.

[0094] In embodiments utilizing a laser projector, the projector may comprise a plurality of laser diodes capable of emitting light in different wavelengths (e.g., for multi-color projections). The projection subsystem may further comprise an adaptive optics system to refine the focus and shape of the projection.

[0094]

[0095] The projection subsystem may be configured to operate in different modes based on ambient light conditions, for example, by increasing intensity during the day and decreasing it at night. It may further be configured for self-calibration to maintain optimal performance over time.

[0095]

[0096] The projection subsystem may incorporate a thermal management system to prevent overheating of the projectors and include energy-efficient designs for prolonged operational periods. The control unit for the subsystem may be capable of receiving remote updates to modify operational parameters.

[0096]

[0097] Sensor Suite Subsystem

[0097]

[0098] The sensor suite may further comprise acoustic sensors for capturing sound data, enabling the AI processing unit to adjust projections in response to auditory environmental changes, such as the sound of a siren or a collision.

[0098]

[0099] The sensor suite may include infrared sensors for enhanced night-time operation. To anticipate changing conditions, the suite may further comprise a weather prediction module for anticipating atmospheric changes that could affect visibility or road conditions.

[0099]

[0100] To ensure data reliability and redundancy, the sensors may be configured to communicate via a mesh network. The system may also allow for sensor calibration to account for performance drifts over time.

[0100]

[0101] User Interface Subsystem

[0101]

[0102] The user interface may include a touch-sensitive display for facilitating user interaction and modification of system settings. The interface may be customizable based on user preferences.

[0102]

[0103] The user interface may further comprise audio output for providing user feedback and may be configured to log user interactions for future analysis and system refinement.

[0104] In some embodiments, the user interface includes a mobile application for remote control. The input devices may support multi-touch gestures for enhanced control. For security, the interface may be equipped with authentication modules to ensure secure access.

[0103]

[0105] The user interface may be designed to display system status indicators and may be integrated with a task scheduler for automatic operation at predetermined times. For hands-free operation, the interface may incorporate a voice command recognition system.

[0104]

[0106] METHOD EMBODIMENTS

[0105]

[0107] In another aspect, methods of using the projection system are disclosed. In one embodiment, a method for dynamically projecting safety indicators on roadways is provided. The method includes deploying the intelligent projection system along a roadway, integrating LIDAR and camera sensors to gather real-time traffic data, analyzing the data with the AI processing unit to identify traffic patterns, and generating adaptive laser and LED projections to display safety indicators. The method may further include monitoring vehicular motion to adjust projections, projecting hazard warnings for accidents, and communicating detected hazards to traffic management centers.

[0106]

[0108] The roadway method may be enhanced by integrating auditory warnings synchronized with visual projections. The system may further include a self-cleaning mechanism for sensor lenses to maintain data accuracy and may be integrated into a collaborative network with adjacent roadway systems for regional traffic management.

[0107]

[0109] In another embodiment, a method for enhancing residential safety is provided. The method includes installing the projection system in a residential environment, equipping it with sensors such as heat and carbon monoxide sensors and cameras, analyzing sensor data to detect risks like unattended cooking or security breaches, projecting warning alerts onto visible surfaces, and notifying residents via a mobile application. The method may further include integrating with smart home networks and HVAC systems for a comprehensive response to detected hazards like smoke or gas leaks.

[0108]

[0110] In another embodiment, a method for managing industrial safety is provided. The method includes deploying laser projectors to define hazardous or restricted zones on a construction or industrial site, integrating sensors to track site activity, using the AI processing unit to continuously analyze sensor data, and adjusting the laser projections in response to changing conditions. The methodmay include projecting visual markings for machinery alignment and integrating with central site management software and construction design plans for real-time coordination.

[0109]

[0111] In another embodiment, a method for adaptive emergency alerting is provided. The method includes employing the intelligent projection system to display emergency alerts, using integrated sensors to collect live environmental data, analyzing the data to adapt projected evacuation routes as a situation evolves, and facilitating communication with emergency responders by projecting areas of concern or instructions.

[0110]

[0112] In an embodiment, the intelligent laser and LED projection system is deployed along roadways to enhance pedestrian safety by projecting visual alerts such as crossing markers and directional arrows that adapt to real-time traffic conditions. The system utilizes embedded sensors and an Al processing unit to continuously analyze vehicular motion, thereby optimizing the projection of warnings and guidance patterns on the road surface to inform both drivers and pedestrians of safe crossing intervals and potential hazards.

[0111]

[0113] In another embodiment, the system is integrated within a residential environment where it monitors kitchen activities to improve safety. Equipped with sensors such as heat detectors and cameras, the AI processing unit analyzes activity patterns, identifying potential risks like unattended cooking appliances. In the event that a threshold temperature is detected without subsequent movement indicating supervision, the system projects a warning alert directly onto a visible kitchen surface, prompting immediate resident response.

[0112]

[0114] In yet another embodiment, the intelligent projection system is tailored for industrial applications, wherein laser projectors and a suite of sensors are employed to demarcate hazardous areas within a construction site. Through real-time analysis of environmental data, the system dynamically adjusts laser projections to align with changing site conditions. This ensures that machinery operators and workers receive accurate visual guides, such as boundary markers and alignment lines, thereby reducing the risk of accidents and improving operational efficiency.

[0113]

[0115] In an embodiment, the intelligent laser and LED projection system incorporates a humidity sensor to adjust the intensity and focus of laser projections in response to varying atmospheric moisture levels. This capability enhances projection clarity and reliability in diverse weather conditions, particularly in foggy or misty environments. The system may employ Al algorithms to correlate humidity data with optimal projection settings, ensuring consistent visibility regardless of external factors.

[0116] In another embodiment, the system integrates a voice recognition module within the user interface, enabling hands-free operation and customization of projection patterns. Users may issue voice commands to adjust the focus, size, and orientation of projected images, providing greater flexibility and accessibility, particularly in situations where manual control could be cumbersome or unsafe.

[0114]

[0117] In yet another embodiment, the system's Al processing unit incorporates a noise detection feature that identifies sudden, anomalous sounds, such as a car crash or a window breaking. Upon detection, the system may automatically adjust projection patterns to highlight the source of the noise and notify users through the mobile application or web interface. This feature enhances situational awareness and assists in prompt response to unexpected events.

[0115]

[0118] In an alternative embodiment, the system's environmental object mapping capability employs advanced edge detection algorithms to create more precise boundaries of projected patterns in complex environments. By leveraging such algorithms, the system ensures that projections are accurately aligned with real- world objects and surfaces, enhancing both safety and aesthetic appeal.

[0116]

[0119] In still another embodiment, the system is equipped with an advanced energy management module that analyzes usage patterns and environmental conditions to forecast energy consumption. The Al processing unit adjusts the projection system's power settings to optimize energy efficiency, contributing to reduced operational costs and environmental impact. This embodiment may incorporate solar power integration as an alternative energy source, further enhancing sustainability.

[0117]

[0120] In an embodiment, the intelligent laser and LED projection system is equipped to perform dynamic traffic management by projecting adaptive signals on road surfaces, which guide vehicular movement across intersections and pedestrian crossings. These projections may change in realtime and / or in a time resolved manner based on traffic congestion data obtained from integrated LIDAR and camera sensors, facilitating efficient traffic flow and reducing congestion.

[0121] In another embodiment, the system includes advanced environmental hazard detection capabilities, allowing it to identify potential fire hazards in residential settings by utilizing heat sensors and smoke detectors. Upon detection of a hazard, the system projects visible alerts on walls or floors in the vicinity while simultaneously sending notifications to a user's mobile device via the user interface for immediate attention and action.

[0122] In yet another embodiment, the AI processing unit of the system leverages predictive modeling to anticipate peak traffic times in industrial settings, thereby enabling preemptive marking of hazardous zones and machinery paths to enhance worker safety. This predictive capability allows for the timely adaptation of laser projections to display visual cues that help in the coordination and seamless operation of industrial processes.

[0118]

[0123] In a further embodiment, the user interface is designed to provide customization options such that users can adjust the system's sensitivity to specific movements or environmental changes. For example, homeowners can set thresholds for sensor detection levels, which in turn modify the system’s response to detect deviations in expected household activity patterns, influencing the types of alerts and projections displayed.

[0119]

[0124] In yet another embodiment, the system's adaptive learning feature incorporates user feedback obtained via the mobile application. This feedback, coupled with continuous environmental data, allows the AI processing unit to refine its understanding of habitual patterns in residential or commercial areas. Consequently, the system enhances the accuracy and relevance of its projections and alerts, thereby supporting safer navigation and more effective communication across demarcated spaces.

[0120]

[0125] The intelligent laser and LED projection system offers broad applicability across multiple sectors, each leveraging the system's adaptive projection capabilities and comprehensive environmental awareness. The following descriptions highlight various potential uses across diverse environments and settings:

[0121]

[0126] Roadway and Traffic Safety: The system can be installed alongside roadways to project dynamic visual cues such as pedestrian crosswalk indicators and real-time traffic signals. Using data from LIDAR and camera sensors, it adjusts projections to guide vehicles and pedestrians safely, especially during peak traffic hours or inclement weather conditions. Additionally, the system projects hazard warnings in response to detected accidents or roadwork.

[0122]

[0127] Residential Safety Enhancements: Within a home, the system monitors kitchen activities, projecting alerts to mitigate risks such as unattended appliances or fire hazards. It employs sensors such as heat detectors and cameras to analyze activity and environmental conditions, prompting an alert when detecting high temperatures without movement suggestive of supervision. The system also identifies security-related anomalies like window breaches, projecting alerts on walls or floors and notifying users via mobile applications for quick response.

[0128] Industrial and Construction Site Safety: The system is configured to delineate hazardous zones on construction sites using laser projections. By integrating sensors that track environmental conditions, it dynamically adjusts these zones in response to shifting on-site activities, providing clear visual boundaries to enhance worker safety and prevent accidents. In manufacturing or warehousing contexts, the system aids machinery alignment by projecting precise guidelines and patterns, ensuring accuracy and reducing waste from misalignment or errors.

[0123]

[0129] Public Safety and Emergency Management: The system projects alerts during emergency scenarios such as fire or severe weather events. It offers pathways for evacuation by marking and illuminating safe routes using real-time sensor data to adapt to evolving conditions. Additionally, it aids police or emergency responders by highlighting areas of concern, such as ongoing criminal activities or crowd control zones, to facilitate efficient management.

[0124]

[0130] Medical and Health Applications: In medical settings, the system supports minimally invasive procedures through precise laser-guided projections that assist practitioners in targeting specific treatment areas, contributing to enhanced surgical precision and safety. The projection system can also be adapted to guide patients or staff along safe navigational routes within hospital complexes, adjusting in response to changes such as temporary or emergency ward layouts.

[0125]

[0131] Traffic Management and Control: Beyond conventional traffic signals, the system projects in a time resolved manner and / or a real-time visual indicators on road surfaces to manage vehicular movement effectively, particularly at complex intersections and during events causing sudden traffic surges. This enhances visibility and compliance, reducing congestion.

[0126]

[0132] Assistive Technologies for Mobility and Cognitive Support: For individuals with mobility challenges, the system augments communication through visual cues and patterns, facilitating easier navigation within residential or public environments. Similarly, the system offers cognitive support for users with disabilities by providing contextual prompts and adaptive assistance where needed.

[0127]

[0133] Military and Tactical Operations: The system offers undetectable projection solutions for targeting and troop coordination, utilizing invisible infrared lasers where stealth is required. It aids reconnaissance operations by projecting terrain markers or guiding paths without overt visual detection, supporting strategic initiatives.

[0134] Disaster Response and Rescue Operations: During disaster events such as earthquakes or floods, the system enhances rescue operations by projecting guides that direct responders and evacuees along safe evacuation routes. It dynamically adapts to unfolding scenarios, ensuring responsive updates to projected pathways and safe zones.

[0128]

[0135] Environmental Monitoring and Conservation: The system is used in ecological sites for conservation efforts by projecting barriers or informational guides without physical infrastructures that might disturb the habitat. This usefulness extends to visitor management by directing flows and highlighting areas of interest while respecting ecological sensitivity.

[0129]

[0136] Dynamic parking management and enforcement: In a dynamic parking management and enforcement embodiment, the system is configured as a "smart sign" for dynamic parking management. Projection units and integrated high-resolution cameras are mounted on infrastructure overlooking parking spaces, such as lampposts or building facades. The Al processing unit is trained with image recognition models to continuously analyze the video feed to: (a) identify the boundaries of designated parking spaces; (b) detect the presence or absence of a vehicle within a space; and (c) correlate this data with predefined rules, such as time-of-day restrictions, permit-only zones, or handicapped-only designations. Based on this real-time analysis, the projection unit provides an immediate visual cue directly onto the pavement or vehicle. For example, it may project a green, welcoming box over an available space, a neutral blue outline around a legally parked car, or a highly visible red " NO PARKING" symbol or crosshatch pattern over an illegally parked vehicle. This provides instant feedback to drivers and can be further configured to log violations, capture license plate data, and transmit an alert to parking enforcement authorities, thereby automating and streamlining the enforcement process.

[0130]

[0137] Expanding on the smart signage concept, another embodiment configures the system to manage dynamic, transient events in public spaces beyond parking. The Al processing unit is trained not only to detect vehicle occupancy but also to identify temporary obstructions, such as a delivery vehicle double-parked, a sudden build-up of pedestrian congestion near a building entrance or crosswalk, or debris in a traffic lane. Upon real-time analysis of such an event, the system projects adaptive visual indicators directly onto the roadway or pavement. For example, it may project animated arrows to guide traffic smoothly around a temporary blockage, project a temporary, illuminated safety buffer zone around a crowd of people, or highlight an unexpected obstacle witha pulsating warning pattern, thereby enhancing situational awareness for all road users and improving urban flow and safety without requiring physical intervention.

[0131]

[0138] In a smart signage embodiment, the system is configured to detect vehicle entry and exit from parking spaces and to identify dynamic events occurring in public spaces, including congestion, temporary obstructions, pedestrian movement, or access to building entrances. Based on real-time analysis, the system projects adaptive visual indicators directly onto the roadway or pavement to guide drivers and pedestrians and enhance situational awareness.

[0132]

[0139] In an embodiment, the intelligent laser and LED projection system includes gesture recognition capabilities that allow users to interact with the system via hand gestures. For example, a user can draw specific symbols or patterns in the air, which the system recognizes and uses to initiate, modify, or terminate certain projection settings. The sensors detect hand movements, and the AI processing unit translates these gestures into actionable commands, enabling dynamic interaction and control over the system's projections.

[0133]

[0140] In another embodiment, the system incorporates a feature where gestures made by crossing a specific zone or line marked by one or more indicia trigger certain projections. For instance, in an industrial setting, walking patterns within designated areas automatically adjust laser guidelines for moving machinery safely around the identified pathway. The system utilizes movement patterns over time to refine the boundaries and optimize workflow efficiency while maintaining safety.

[0134]

[0141] In yet another embodiment, the system is configured to respond to time-based gesture sequences that initiate complex projection mechanisms. Users perform a predefined series of gestures, which the system records and processes through the Al unit. This feature could be used in residential environments, for example, to switch automatically between different modes — such as safety monitoring and ambient lighting — at particular times of day, based on repeated gesture patterns logged over time.

[0135]

[0142] In a further embodiment, the system includes gesture-based interactions that convey changes over time. In traffic management contexts, specific gestures executed by traffic personnel, such as a sweeping arm motion, activate or change specific road projections like stop or go indicators. The system monitors gesture trends and adjusts signal timings or projection patterns accordingly, improving efficiency and responsiveness to real-time conditions.

[0143] In yet another embodiment, the system enables multi-user gesture interactions, where combined gestures from multiple users influence the system's operation. For example, in a disaster response scenario, collaborative gestures performed by a team over a designated period create a composite projection indicating a safe path or marking a hazardous area. This gesture integration supports coordinated efforts and real-time adaptability to changing rescue conditions.

[0136]

[0144] In an embodiment, the intelligent laser and LED projection system comprises an Al module with personal-specific training capabilities, allowing for individual user modeling based on recurrent interaction data. The system records user behaviors and preferences through integrated sensors and refines its operation by training neural networks over repeated sessions. This facilitates tailored projections and alerts adapted to the specific habits and needs of different users, enhancing personalized safety and communication.

[0137]

[0145] In another embodiment, the system includes a module for event-specific training sessions whereby the Al processes and learns from recurring events such as traffic surges during rush hours or regular household activities. Machine learning algorithms are employed to capture patterns associated with these events, enabling the system to anticipate needs and adjust projections accordingly. This results in optimized visual cues for traffic management or household safety that accurately reflect familiar scenarios, improving overall situational responsiveness.

[0138]

[0146] In yet another embodiment, the system features hazard-specific training capabilities integrated within the AI processing unit. Through neural network algorithms, the system learns to identify and predict potential hazards by analyzing historical data collected during training sessions. This includes recognizing distinctive environmental patterns indicative of risks such as fires, gas leaks, or unauthorized access. The system thereby enhances its predictive capacity, providing more timely and contextually relevant alerts and projections in response to known and anticipated hazards.

[0139]

[0147] In a further embodiment, the projection system incorporates a training module for machine learning focused on dynamic environmental changes. The Al processing unit is equipped to undergo continual training based on real-time changes in traffic patterns, weather conditions, or human activities within a monitored space. This ongoing adaptation enables the system to refine its projection strategies, ensuring accurate and effective guidance or warnings in diverse and fluctuating circumstances.

[0148] In yet another embodiment, the system's machine learning module includes training sessions that incorporate feedback from various user groups or stakeholders. This collaborative approach involves gathering input from distinct populations such as commuters, residents, or industry workers who interact with the projection system. The Al applies this aggregated data during its training to improve understanding and accuracy in reflecting collective needs and preferences, consequently optimizing projection outputs for enhanced usability and effectiveness across different contexts.

[0140]

[0149] In an embodiment, the intelligent laser and LED projection system operates in real-time to manage pedestrian and vehicular flow at intersections. The system utilizes continuous LIDAR and camera data to project adaptive lane markings and pedestrian signals, automatically adjusting to current traffic densities to optimize traffic throughput and pedestrian safety.

[0141]

[0150] In another embodiment, the projection system engages in online monitoring of residential environments, allowing remote users to receive continuous updates and control projections directly via a web interface. The system analyzes streaming data from integrated sensors, displaying projections that notify users of detected household hazards such as smoke or unauthorized entry via a live dashboard accessible online.

[0142]

[0151] In yet another embodiment, the system's Al processing unit executes predictive traffic management using online data feeds from city-wide sensor networks. During peak traffic hours, real-time traffic data is used to alter the timing and positioning of projected traffic signals at intersections, ensuring smoother vehicular flows. This capability is further enhanced by analyzing social media feeds and public transportation updates to anticipate changes in traffic patterns.

[0143]

[0152] In a further embodiment, the intelligent projection system incorporates dynamic environmental mapping features to facilitate construction site safety through online connectivity. Real-time environmental conditions, monitored online, direct the system to adjust projections marking safety boundaries and machinery pathways, minimizing risks as conditions evolve.

[0144]

[0153] In yet another embodiment, the system offers online user customization and interaction capabilities, enabling individuals to modify projection settings in real-time via a mobile application. This includes adjusting the system's sensitivity to sensor inputs and the appearance of projected alerts, providing tailored responses that meet unique user needs and preferences from any location.

[0154] In an embodiment, the system integrates an Al-driven online platform for predictive anomaly detection and alert dissemination. The projection system processes real-time online sensor feeds to identify deviations in expected household or roadway patterns, automatically projecting situational alerts and notifications that can be accessed remotely via user accounts on the platform.

[0145]

[0155] In another embodiment, the intelligent laser and LED projection system functions as an online disaster response tool. It utilizes real-time environmental data streams, accessible through an online portal, to guide search and rescue operations by projecting paths to safe zones or areas needing urgent attention. Notifications and projections can be dynamically modified based on live input from emergency response teams accessing the system remotely.

[0146]

[0156] In yet another embodiment, the system's Al processing unit adapts to real-time user feedback collected from online interactions to modify and improve projection strategies. Frequent adjustments are made to projection patterns and alert settings through iterative feedback loops facilitated by online community platforms, enabling proactive safety interventions shaped by user experiences and suggestions.

[0147]

[0157] In a further embodiment, the system facilitates online integration with smart city infrastructures, continuously processing real-time data from municipal surveillance cameras. The projection system overlays virtual markers for traffic management and public safety adaptations, responding to online data indicating fluctuating urban dynamics such as special events or emergency scenarios.

[0148]

[0158] In yet another embodiment, the intelligent projection system utilizes online data sources to enhance environmental monitoring for conservation purposes. Streaming data on weather conditions and visitor movements is used to project dynamic informational guides and alerts without physical interference, allowing for real-time environmental management and sustainable visitor experiences.

[0149]

[0159] In an embodiment, the intelligent laser and LED projection system incorporates real-time Al processing to optimize traffic management by projecting adaptive lane demarcations on busy roadways. Utilizing LIDAR and camera inputs, the system continually analyzes vehicular flow, altering laser and LED projections such as lane guides and directional arrows to reflect current traffic conditions, thereby facilitating seamless vehicular navigation and enhancing road safety.

[0150]

[0160] In another embodiment, the system is deployed in residential areas where real-time Al-driven decision-making allows for the detection of household hazards. Equipped with an array of sensors,the system monitors activities such as cooking, projecting immediate visual warnings using LED and laser technologies when it identifies anomalies like unattended stove operation or the presence of smoke, thus promoting home safety.

[0151]

[0161] In an embodiment tailored for industrial applications, the system employs Al to project real-time laser patterns, marking off hazardous zones and aligning construction elements. As site conditions change, sensor data input is continuously processed by the Al unit to adapt projections, ensuring clear demarcation of safety boundaries and optimized machinery paths to prevent worksite accidents.

[0152]

[0162] In a further embodiment, the intelligent projection system is integrated within a disaster response framework, where it utilizes real-time environmental data to project routes for safe evacuation. By leveraging Al processing, the system dynamically updates these pathways using LED and laser projections, guiding individuals in emergency scenarios such as earthquakes or floods based on live sensor data collected from the affected area.

[0153]

[0163] In yet another embodiment, the system features an interactive user interface enabling real-time customization through a mobile application. Users can adjust projection settings and sensor sensitivity directly from their devices, allowing for tailored alerts and patterns that meet specific personal or professional needs. This integration ensures responsive and personalized projection solutions across diverse contexts.

[0154]

[0164] In a further embodiment, the intelligent laser and LED projection system is configured as a diagnostic and safety tool for visualizing airflow in industrial, commercial, or laboratory environments. This embodiment is particularly applicable to Heating, Ventilation, and Air Conditioning (HVAC) systems, clean rooms, chemical processing plants, and any setting where the direction, velocity, and quality of airflow are critical for safety and operational efficiency. The system architecture for this embodiment comprises:

[0155] a. Projection unit: At least one laser or high-intensity LED projection unit is mounted on a wall, ceiling, or tripod adjacent to an airflow outlet, such as a ventilation duct, fume hood, or air handler exhaust,

[0156] b. Sensor suite: The sensor suite is specifically adapted for atmospheric analysis and includes one or more of the following: an anemometer to measure airflow velocity, a thermal or infrared camera to detect temperature differentials in the air stream, a humidity sensor, and one or more air quality sensors (e.g., particulate matter sensors for dust, or photoionization detectors forvolatile organic compounds (VOCs)).

[0157] c. Al processing unit and logic: The Al processing unit is trained to establish a baseline model of normal airflow characteristics for the specific outlet, including expected velocity, temperature range, and direction. It continuously processes real-time data from the sensor suite, employing anomaly detection algorithms to identify deviations from this baseline. A hazardous condition is triggered if, for example, the anemometer detects a velocity drop below a predefined safety threshold (indicating a blockage), the thermal camera detects a temperature spike (indicating an overheating component), or an air quality sensor detects contaminants exceeding a safe parts- per-million (PPM) value.

[0158]

[0165] Upon detecting such an anomaly, the AI processing unit commands the projection unit to display a real-time, intuitive visualization of the airflow directly onto an adjacent surface, such as a floor or wall. The projected cues may include: a vector field of arrows dynamically changing in size and speed to represent airflow velocity; a color-coded thermal map (e.g., blue for cool, red for hot) representing the temperature of the air stream; or a flashing, high-contrast warning icon (e.g., a biohazard symbol or " STOP") upon the detection of a critical failure like airflow reversal or the presence of toxic gases. This embodiment provides an immediate, easily understandable visual representation of an otherwise invisible environmental condition, enabling maintenance personnel to diagnose system failures without specialized equipment and allowing workers to instantly assess if an area is safe to enter.

[0159]

[0166] In another embodiment, the system is specifically configured to create a dynamic "traffic-regulation envelope" around a slow-moving vehicle. For the purposes of this embodiment, a slow-moving vehicle is defined as any vehicle operating at a velocity significantly below the posted speed limit or the prevai ling speed of traffic, or a vehi cle that makes frequent, unscheduled stops as part of its operational duty. Examples include, but are not limited to, municipal sanitation trucks, agricultural tractors on public roads, large-scale gardening and landscaping machines, utility service carts, and oversized logistics vehicles. The system is mounted on the vehicle and functions as an autonomous, mobile traffic-moderation authority.

[0160]

[0167] The system architecture for this embodiment leverages the vehicle-mounted configuration previously described, wherein the AI processing unit integrates data from a sensor suite comprising LIDAR, cameras, GPS, and an IMU. Optionally, it interfaces directly with the vehicle's CAN bus to ascertain operator intent, such as brake application, turn signal activation,or the engagement of auxiliary equipment. The Al processing unit executes a predictive risk-assessment algorithm that continuously analyzes the surrounding environment. It identifies and tracks approaching vehicles, calculates their Time-to-Collision (TTC), monitors adjacent lanes for safe passing gaps, and detects vulnerable road users such as pedestrians or cyclists in the vehicle's vicinity.

[0161]

[0168] Based on this continuous, millisecond- level analy sis, the AI processing unit commands the rear- and side-facing projection units to project a dynamic, context-aware envelope of visual information onto the road surface. This projected envelope is not static but adapts in real-time to the specific traffic scenario. Exemplary projections include:

[0162] a. Overtaking guidance: When the Al determines that the adjacent lane is clear and a sufficient gap exists, it projects a green, animated chevron or arrow, actively guiding the following driver into the passing lane. Conversely, if an oncoming vehicle is detected or the lane is obstructed, it projects a solid red " Do Not Pass" symbol or a virtual barrier line.

[0163] b. Safe distance indicators: The system projects calibrated markings, such as chevrons or lines, on the road surface behind the vehicle, visually indicating a safe following distance for the vehicle behind, with the distance dynamically adjusted based on the host vehicle's speed and road conditions.

[0164] c. Worker hazard zones: If the system detects a worker on foot operating near the vehicle, it projects a brightly colored, pulsating "halo" or marked boundary on the road surface around the worker's position, warning approaching drivers of the human presence.

[0165] This embodiment transforms the slow-moving vehicle from a passive obstacle into an active and intelligent participant in traffic management, significantly reducing the risk of rear-end collisions and accidents caused by impatient or misinformed overtaking maneuvers.

[0166]

[0169] In yet another embodiment, the system is configured to provide adaptive, event-based roadway illumination to mitigate hazards from unexpected and transient events, particularly in conditions of low visibility, at night, or in complex urban environments. In this configuration, the system may be vehicle-mounted or installed as part, of a fixed infrastructure at high-risk locations such as intersections, crosswalks, or highway merging zones. The core of this embodiment is the system's ability to react with extremely low latency, preferably under 150 milliseconds, from hazard detection to active illumination.

[0170] The Al processing unit and sensor suite are specifically tuned for high-speed event detection.

[0167] The Al algorithms are trained to recognize the kinematic signatures of imminent hazards, such as a pedestrian's trajectory indicating an imminent entry into the roadway, an object falling from another vehicle, a lead vehicle braking with a deceleration rate exceeding a critical threshold, or the high-speed approach of a two-wheeled vehicle that may be difficult for a driver to see.

[0168]

[0171] Upon the positive identification of such an event with a confidence score exceeding a predefined threshold (e.g., 95%), the AI processing unit instantly commands the projection unit to project a highly conspicuous, focused light pattern or symbolic hazard zone directly onto the road surface in the relevant area. The projected illumination is designed to draw the immediate attention of drivers to the precise location and nature of the hazard. Examples of such event-based projections include:

[0169] a. Dynamic crosswalk: For a pedestrian detected stepping onto the road outside of a marked crosswalk, the system projects a temporary, illuminated crosswalk directly in their path, making them highly visible to approaching traffic.

[0170] b. Virtual hazard cone: For a detected object on the road, such as debris, the system projects a three-dimensional-appearing cone of light around the object, simulating a physical warning cone. c. Imminent braking alert: Upon detecting a sudden stop by a vehicle ahead, the system projects a large, flashing " STOP" symbol or a red bar on the road surface between the driver and the stopping vehicle, preempting the driver's own reaction to the vehicle's brake lights.

[0171] This rapid, adaptive illumination serves as a "visual reflex," dramatically reducing driver perception-reaction time and preventing accidents caused by sudden, unforeseen roadway events.

[0172]

[0172] As a variant of the previous embodiment, the system is configured for large-scale, smart urban design by mounting the projection units directly onto the external facades of buildings that face roadways. This configuration eliminates the need for dedicated poles, gantries, or other ground¬ mounted infrastructure, which is particularly advantageous in dense urban centers, historic districts with aesthetic preservation rules, or narrow streets where sidewalk space is limited. A network of these building-mounted systems can communicate wirelessly (e.g., via a 5G or Wi-Fi mesh network) to create a fully coordinated, infrastructure-free traffic management grid. The sensor suites integrated into these building-mounted units can also be leveraged for broader smart city applications, such as monitoring pedestrian density on sidewalks, detecting unauthorized gatherings, or identifying available curbside loading zones in real-time.

[0173] In a primary application of this embodiment, the system manages bi-directional traffic on a street that is only wide enough for a single lane of vehicles. Sensors and projection units are mounted on buildings at both ends of the narrow street segment. The networked Al processing units function as a singular, distributed intelligence. They detect vehicles approaching from both directions and execute a dynamic traffic-flow optimization algorithm to assign right-of-way, preventing gridlock and head-on conflicts. The dynamic traffic-flow optimization algorithm may employ a virtual token-passing or reservation-based system, wherein a vehicle or platoon of vehicles is granted an exclusive, timed ’token' to traverse the narrow segment, ensuring a collision-free and efficient flow.

[0173]

[0174] In an embodiment, the projection system is externally mounted on building facades facing a roadway, eliminating the need for poles or ground-mounted infrastructure. The system provides adaptive, directed illumination and dynamic road markings to regulate bi-directional traffic on single-lane streets, enabling architectural flexibility and advanced urban traffic planning.

[0174]

[0175] The system may project:

[0175] a. Right-of-Way Indicators: A large, green, forward-pointing arrow is projected onto the road surface for the direction of traffic that has been granted right-of-way. Simultaneously, a bright red, horizontal bar or " HOLD" text is projected for the opposing traffic.

[0176] b. Timed passing windows: The system can project a countdown timer, allowing waiting drivers to see when the right-of-way will switch, managing expectations and improving flow efficiency. c. Dynamic no-blocking zones: The system can project " KEEP CLEAR" markings in front of building exits, garage doors, or intersecting alleys, dynamically activating them only when its sensors detect a vehicle or pedestrian needing to emerge.

[0177] d. Emergency Vehicle Preemption: Upon receiving a signal from a V2X network or detecting the acoustic signature of an approaching emergency vehicle, the system can override the standard traffic flow logic, projecting 'PULL OVER' or 'YIELD' commands to all vehicles in the segment to clear a path.

[0178] This building-mounted embodiment provides unprecedented architectural and planning flexibility, enabling advanced, adaptive traffic control to be seamlessly integrated into the urban fabric without the physical or aesthetic intrusion of traditional infrastructure.

[0179]

[0176] Detailed Description of the Roadway Projection System: System Architecture: The system encompasses a projection unit strategically installed on traffic management infrastructures, suchas traffic poles. This unit integrates laser and LED technologies to project visual notifications on road surfaces, including traffic signals, pedestrian crossing indications, and lane markings. The design ensures enhanced road visibility, adapting to variable weather conditions and lighting environments. The unit may utilize commercial products like VarioLED Flex, which allows modifications in projection color and intensity, as well as Phantom Dynamics Laser Projectors, calibrated for high-precision image rendering.

[0180]

[0177] Sensor Sub-System: The subsystem includes a range of sensors such as Light Detection and Ranging (LIDAR) devices, high-resolution cameras, and infrared sensors, working together to continuously monitor vehicular and pedestrian movements. The gathered data include traffic density, speed, and directional vectors. Devices like Velodyne HDL-32E can perform three- dimensional environmental mapping, Axis Communications cameras capture broad-angle video, and FLIR Systems' infrared sensors enable low-light and nocturnal monitoring.

[0181]

[0178] Al Processing Module: This module incorporates machine learning algorithms for real-time data analysis, dynamically adapting projection schemes based on traffic conditions. The processing capabilities include identifying traffic patterns, predicting congestion, or collision prospects. Platforms like TensorFlow and PyTorch facilitate the deployment of neural network-based recognition models.

[0182]

[0179] Communication Interface: Designed for seamless integration with municipal traffic management systems, this interface enables synchronized data flow and updates, ensuring coordinated traffic flow management. It supports various communication protocols, potentially utilizing high-performance network devices like Cisco Industrial Ethernet 1000 Series switches for durable, high-speed connectivity.

[0183]

[0180] Detailed Description of the Residential Safety System: Core Configuration: Optimal projection units are placed in residential environments such as kitchens and living rooms, emitting visual alerts for potential hazards like fire or carbon monoxide risks. Systems may feature components similar to Philips Hue Play Gradient Lightstrip, which offers customizable color and intensity.

[0184]

[0181] Sensor Sub-System: Comprising devices such as temperature sensors, smoke detectors, motion sensors, and surveillance cameras, this subsystem collaboratively identifies and responds to hazardous conditions. Nest Protect units can deliver notifications about smoke or carbon monoxide levels, while Arlo Ultra cameras provide high-def security surveillance.

[0182] Al Processing Module: Processing environmental sensor data, this module establishes activity patterns to identify atypical events signaling potential dangers. Al frameworks like IBM Watson can develop predictive models, enhancing the system’s safety alerts.

[0185]

[0183] User Interface Module: A flexible mobile or web-based application allows users to receive alerts, customize projection settings, and manage notifications. Integration with platforms like Apple HomeKit may offer a cohesive user experience and comprehensive control across devices.

[0186]

[0184] Detailed Description of the Industrial and Construction Site Safety System: System Framework: Laser projectors demarcate hazardous zones and aid machinery alignment on construction sites. Tools like LaserTools 5000 are configured for site deployments demanding durability and precision.

[0187]

[0185] Sensor Sub-System: Including LIDAR, optical cameras, and environmental sensors, this subsystem records real-time conditions. Instruments like Topcon RL-H5A laser level support precise measurements, essential for safety and precision.

[0188]

[0186] Al Processing Module: Evaluating sensor inputs continuously, this module optimizes projection patterns based on site changes. High-performance platforms like NVIDIA Jetson are used for realtime data processing.

[0189]

[0187] Interface Module: Facilitating user interaction, this module allows customization of projection parameters and boundaries. Devices such as Panasonic Toughbook, known for their rugged design, enhance usability in tough environments.

[0190]

[0188] Detailed Description of the Disaster Response System: System Composition: Portable projection units for emergencies project dynamic evacuation routes and messages. Lightweight units akin to Epson EF-100 Mini-Laser Projector adapt to various situations.

[0191]

[0189] Sensor Sub-System: Remote sensors and real-time feeds maintain situational awareness during disasters. Instruments like Davis Instruments Vantage Pro2 collect crucial atmospheric data for evaluation.

[0192]

[0190] Al Processing Module: Processing dynamic data, this module adjusts evacuation route projections based on real-time conditions. Platforms like Microsoft Azure Al enable scalable data processing.

[0193]

[0191] Communication Interface: Ensures connectivity with emergency entities, facilitating proactive management through feedback. Systems like Motorola Solutions APX radios provide reliable communication in adverse conditions.

[0192] Detailed Description of the Interactive User Interface System: System Structure: Integrating a mobile app with control interfaces, the system allows for real-time interaction, enhancing user engagement. Platforms like Android OS offer development flexibility and accessibility.

[0193] Customization Module: Offers options for altering projection characteristics such as intensity, patterns, and focal areas, adapting to user preferences or environmental needs. Xamarin is employed for seamless cross-platform interface development.

[0194]

[0194] Feedback Module: Designed to analyze user interactions, improving system responsiveness.

[0195] Systems like AppDynamics Performance Monitoring provide insights into user engagement.

[0196]

[0195] Data Synchronization Interface: Coordinates settings across devices, linking to a central unit for consistent application. Technologies like AWS loT Core enable cohesive synchronization.

[0197]

[0196] Detailed Description of the Environmental Monitoring and Conservation System:

[0198] Architectural Overview: With laser and LED projectors, the system provides guidance and informational displays in conservation settings, minimizing habitat impact. NEC Display Solutions' projectors offer non- intrusive, informative projections.

[0199]

[0197] Sensor Sub-System: Includes devices monitoring environmental variables like climate and human activity. Equipment analogous to Kestrel 5500 Weather Meter captures detailed data.

[0200]

[0198] Al Processing Module: Analyzing sensor inputs, this module presents dynamic informational guides relevant to environmental contexts. TensorFlow Lite ensures efficient processing compatible with resource-limited systems.

[0201]

[0199] Communication Interface: Interfaces with conservation networks, delivering data and receiving instructions. Solutions like loT Connectivity from Sierra Wireless provide reliable data transmission for efficient operations.

[0202]

[0200] Detailed Embodiment for Assisting Older Individuals and Patients: In an embodiment, the intelligent laser and LED projection system is implemented in environments requiring assistance for older individuals and patients, such as homes for the elderly or hospitals. The system features projection solutions designed to enhance safety and mobility support for patients confined to beds or chairs.

[0203]

[0201] System Implementation: The projection units are strategically positioned across the living or medical spaces, equipped with advanced laser and LED technology to display visual cues and alerts tailored to the specific needs of older individuals and patients. These cues include pathwaymarkers, proximity alerts, and guidance patterns, aiding in navigation and safety within these environments.

[0204]

[0202] Sensor Sub-System: A suite of sensors, including motion detectors, proximity sensors, and environmental sensors, is deployed to assess room layouts and monitor patient activity. Devices such as motion sensors detect patient movement, and proximity sensors ensure safe navigation, especially for those with limited mobility. These sensors gather detailed data to inform the AI processing unit about patient activities, positioning, and potential obstacles.

[0205]

[0203] Al Processing Module: The embedded Al processing unit employs machine learning algorithms to analyze sensor data, facilitating real-time adaptive projections based on patient needs. The module can identify routines and adapt the projection patterns for safe navigation, enhancing alert systems in response to detected risks like bed falls or wandering outside designated areas.

[0206]

[0204] User Interface: The system interfaces through a mobile or web application that enables caregivers and healthcare professionals to monitor real-time activity and modify projection settings remotely. This functionality supports customized patient care, providing immediate alerts for movements deviating from expected patterns, thus ensuring timely interventions.

[0207]

[0205] Functional Features:

[0208]

[0206] Adaptive Pathway Guidance: The system projects dynamic visual aids onto floors, indicating routes for safe movement within facilities. These projections adjust in real-time, considering factors like proximity to obstacles or changes in room layout.

[0209]

[0207] Proximity and Movement Alerts: Visual and auditory alarms are projected when patients move towards unsafe zones or attempt to exit the bed or chair without assistance. This feature aids caregivers in preventing falls or accidents.

[0210]

[0208] Routine Support and Reminders: The system highlights daily routines and schedules through visual prompts, supporting patients with memory challenges. For hospital settings, it can remind staff of medication timings or treatment schedules projected onto walls or medical charts.

[0211]

[0209] Emergency Response Integration: In emergencies, the system switches to an alert mode, projecting evacuation routes and safety instructions onto floors and walls. The integration with hospital emergency systems ensures cohesion in response scenarios, facilitating patient and staff safety through clear, visible guidance.

[0210] Interaction and Customization: The user interface allows for interactive customization, enabling modifications of projection brightness, pattern styles, and alert sensitivities. Caregivers can adjust these settings to match patient preferences or address specific care objectives, enhancing patient comfort and safety.

[0212]

[0211] Integration with Assistive Technologies: The system complements existing assistive technologies, integrating with devices such as mobility aids or monitoring systems. Projections can interact with wheelchair sensors, automatically adjusting navigation aids and alerts as patients move throughout the facility.

[0213]

[0212] Through such adaptable features and configurations, the intelligent projection system provides a comprehensive solution for protecting and aiding older individuals and patients, promoting independence and safety in both home and hospital settings.

[0214]

[0213] Detailed description of a vehicle-mounted autonomous traffic management embodiment

[0214] In a preferred embodiment, the general principles of the intelligent laser and LED projection system with Al-driven safety enhancements, as disclosed herein, are specifically configured and applied to create a vehicle-mounted, autonomous system for dynamic traffic management and worker protection. This embodiment is particularly adapted for service vehicles that move slowly or stop frequently on roadways, such as sanitation, maintenance, or infrastructure trucks, creating a novel solution for a well-known and critical safety problem in urban and highway environments. This embodiment functions as a mobile, self-contained traffic management platform, transforming the service vehicle from a passive hazard into an active traffic-regulating agent.

[0215]

[0215] Principle of operation: The core principle of this embodiment is to leverage the system's capabilities of environmental sensing and Al-driven adaptive projection to manage traffic flow around the host vehicle. By autonomously identifying the vehicle's operational state (e.g., an operational stop for collection versus a stop in traffic) and analyzing the surrounding environment in real-time, the system projects active, intelligible visual cues to guide other vehicles safely. This creates a dynamic protective envelope for on-foot workers without dependence on fixed roadway infrastructure or manual intervention. The innovation lies in the full autonomy of the decision¬ making process at the vehicle level, providing an active, intelligent response to the specific conditions of service vehicles, a capability not present in conventional Advanced Driver- Assistance Systems (ADAS) or static infrastructure solutions.

[0216] System architecture and components:

[0216]

[0217] Consistent with the overall system architecture described, this vehicle-mounted embodiment comprises a synergistic integration of hardware and software modules:

[0217] a. Al processing unit (control unit): This is the central processing hub of the system, comprising a smart control unit with independent logic. It is preferably implemented on a robust, industrial-grade edge- computing platform (e.g., NVIDIA Jetson Orin series, Raspberry Pi, or an equivalent. ARM Cortex-based board) optimized for real-time performance, low- power consumption, and resilience to environmental factors such as heat, moisture, and vibration. This unit is responsible for executing the sensor fusion, decision-making, and communication algorithms.

[0218] b. Projection and indication units: At least two rear-facing projection units are utilized to project dynamic visual notifications. These may be high-intensity LED displays or laser projectors capable of rendering clear symbols on the road surface. They are configured to display a vocabulary of visual cues, including but not limited to: dynamic directional arrows (e.g., "pass left," "pass right”), "stop" signals, "slow down" warnings, and patterns indicating a safe overtaking lane. The choice of projection technology and intensity is dynamically- adjusted based on ambient light conditions to ensure high visibility day or night and in inclement weather. c. Environmental sensor suite: The system interfaces with a multi-modal sensor suite specifically adapted for vehicular use:

[0219]

[0218] i. Vehicle state sensors: To determine the host vehicle’s precise operational state, the system integrates data from a sensor fusion module. This module processes inputs from a Global Positioning System (GPS) receiver for location and coarse velocity, an Inertial Measurement Unit (IMU) for fine-grained acceleration and orientation data, and a direct connection to the vehicle's Controller Area Network (CAN) bus. The CAN bus interface allows the system to read critical parameters such as vehicle speed, brake application status, gear selection, and the state of auxiliaryequipment (e.g., the refuse collection arm on a sanitation truck), providing rich context for the AI processing unit. ii. External environment sensors: To build a real-time model of the surrounding environment, the suite includes external-facing sensors such as LIDAR for precise distance and velocity measurements of other objects, and one or more high-resolution cameras for object detection andclassification (e.g., identifying cars, cyclists, pedestrians). These inputs are processed by algorithms, potentially including open-source foundations like YOLO (You Only Look Once) for object recognition, to detect and track passing vehicles, their speed, trajectory, and proximity to workers.

[0220]

[0219] Worker protection subsystem (optional): To extend the system's safety capabilities beyond visual communication to drivers, an optional subsystem may be included to provide direct, immediate alerts to workers. This subsystem is a critical feature for mitigating the highest-risk scenarios. It comprises:

[0221] a. Wearable alert devices: Each worker on the team is equipped with a personal, wearable device. This device may be integrated into standard personal protective equipment (PPE) such as a safety vest, a helmet, or a wristband. The device includes one or more alert mechanisms, such as a high-power haptic vibrator, a loud audible alarm (e.g., a high-frequency beep), and / or a high-intensity, flashing LED light. b. Wireless communication link: The Al processing unit on the vehicle is equipped with a low- latency wireless communication module (e.g., Bluetooth LE, LoRa, or a proprietary RF protocol) that maintains a constant link with the wearable devices. c. Threat-triggered alert logic: The Al processing unit continuously analyzes sensor data to identify imminent threats to workers. A threat may be defined by parameters such as an approaching vehicle exceeding a speed threshold, a vehicle on a collision trajectory with a worker's last known position, or a vehicle making an unexpected maneuver. Upon detection of such a threat, the Al unit instantly transmits an alert signal to the specific worker's (or all workers') wearable device, activating the alert mechanism. This provides an immediate, unambiguous warning, allowing the worker critical seconds to take evasive action.

[0222]

[0220] Additional safety and communication features:

[0223] a. Follow-me illumination: The system may be further configured to control an articulated spotlight or an array of lights on the host vehicle. In low-tight conditions or upon detecting a specific event, the system can direct a beam of light towards the area of activity, a detected hazard, or a worker's location, thereby enhancing visibility for the worker and for other drivers. b. V2X (vehicle-to-everything) integration: The system architecture is designed to be forward¬ compatible with emerging V2X communication protocols. This allows the system not only to project visual cues but also to electronically broadcast its status, intended actions, and warningsto other V2X-equipped vehicles and smart infrastructure (V2V, V2I), creating a more integrated and predictive safety ecosystem.

[0224]

[0221] System logic and decision-making algorithms: The Al processing unit executes a sophisticated, layered software stack to achieve its objectives:

[0225] a. Sensor fusion algorithm for state recognition: A key technological challenge is distinguishing between different vehicle states. The system employs an algorithm that fuses the noisy, high-frequency data from the GPS and IMU with the deterministic data from the CAN bus. This algorithm is trained to recognize specific patterns, such as the characteristic stop-start cadence of residential garbage collection ("operational stop") versus a stop due to a red light or traffic congestion ("non-operational stop"). The algorithm must reliably identify the correct vehicle state with an accuracy of greater than approximately 92%. b. Dynamic indication logic engine: Based on the determined vehicle state, the system activates a decision-making engine. This may be a rule-based engine or a trained machine learning model that maps specific scenarios to specific projection outputs. For example:

[0226] * Scenario: Vehicle in "operational stop" state on a two-lane road. Action: Project a solid arrow pointing to the adjacent lane, indicating "overtake here." * Scenario: Vehicle slowing down rapidly on a highway. Action: Project a flashing "slow down" warning pattern. * Scenario: Threat detected by worker protection subsystem. Action: Project a high- intensity, flashing hazard pattern in all directions while simultaneously sending the wearable alert.

[0227] c. Real-time performance: The entire process, from sensor data acquisition to the activation of the visual indication, is optimized to occur within a maximum latency of approximately 2 seconds, ensuring that the guidance provided is timely and relevant to real-time traffic dynamics.

[0228]

[0222] To execute its safety functions, the AI processing unit is configured to define, monitor, and react to a plurality of predefined hazardous event scenarios. The novelty of the system's logic lies in its ability to not only detect objects, but to understand the context of their presence and motion relative to the host vehicle and its workers, and to trigger a specific, appropriate response. Each event scenario is characterized by a set of parameters that enable the AI processing unit to identify a potential accident root cause and act preemptively.

[0223] For each defined scenario, the AI processing unit utilizes a set of coverage parameters and quality parameters. The coverage parameters establish geometric zones of interest or "sectors" relative to the host vehicle, defining the spatial boundaries within which sensor data is critically analyzed. The quality parameters define the required performance metrics for the sensor suite and the AI processing unit's algorithms for that specific scenario, including but not limited to detection latency, true positive (TP) and false positive (FP) rates, and measurement accuracy for position and velocity. As the system gathers more data over time, these parameters may be tuned to optimize performance, and new scenarios can be added if analysis of accident data reveals previously unhandled root causes.

[0229]

[0224] To implement this event-driven logic, the AI processing unit may employ a multi-modal processing architecture that provides algorithmic redundancy, thereby enhancing system reliability and robustness, which can contribute to achieving a higher Automotive Safety Integrity Level (ASIL). This architecture may comprise at least two parallel processing paths:

[0230] i. An object detection path: This path utilizes a Deep Neural Network (DNN) or a similar machine learning model to process sensor data (e.g., from cameras or LIDAR) to detect, classify (e.g., as a car, truck, pedestrian, two- wheeler), and predict the trajectory and velocity of discrete objects in the environment. ii. An occupancy path: This path operates in parallel to identify the existence of general objects or obstacles without necessarily classifying them. It may generate a dynamic occupancy grid or map of the surrounding area, which is crucial for scenarios where an object is unclassifiable but still poses a hazard, such as a fallen object on the road.

[0231]

[0225] The system further relies on a mapping module for contextual awareness. An offline map of a service route may be pre-generated. During operation, an online mapping function provides realtime ego-positioning of the host vehicle on the map, allowing for accurate lane assignment for both the host vehicle (ego) and detected target objects. This mapping is also used to enhance occupancy detection by differentiating between expected static objects (from the map) and new, dynamic obstacles.

[0232]

[0226] Exemplary use-case scenarios:

[0233] a. Residential sanitation collection: The system detects the frequent stops and the operation of the collection arm via the CAN bus. It projects an arrow guiding following cars to pass onthe left. If a car approaches too quickly while a worker is behind the truck, the system simultaneously flashes a warning projection and sends a vibration alert to the worker's vest. b. Highway maintenance vehicle: While performing slow-moving work on a highway shoulder, the system projects a large, highly visible chevron pattern to merge traffic away from the lane. It continuously monitors for vehicles encroaching into the work zone and can issue direct alerts to the crew. c. Emergency scene management: On an emergency vehicle at an accident scene, the system can project dynamic instructions to guide approaching traffic into specific lanes, helping to manage the scene safely and effectively before additional personnel arrive.

[0234]

[0227] TABLE 1

[0235] Label Name Label Description

[0236] Step 1 Employing Al to project emergency alerts and evacuation pathways during crises

[0237] Step 2 Using live sensor data to adapt projected routes to safe zones as situations evolve

[0238] Step 3 Facilitating communication with responders by projecting areas of concern

[0239] Step 4 Incorporating feedback from responders for refined projection strategies

[0240] Step 5 End

[0241] Step 6 Start

[0242] Step 7 Integrate system in facility

[0243] Step 8 Use sensors and Al for real-time layout monitoring

[0244] Step 9 Project safe navigational routes and guidelines

[0245] Step 10 Interaction through mobile devices?

[0246] Step 11 Customize projections on mobile devices

[0247] Step 12 Receive and analyze feedback for optimization

[0248] Step 13 End

[0249]

[0250] Label Name Label Description

[0251] Step 14 Deploying the system at critical intersections and roadways.

[0252] Step 15 Analyzing traffic data from municipal sensors using Al processing.

[0253] Step 16 Is traffic flow normal?

[0254] Step 17 Projecting adaptive lane guides and traffic signals based on vehicular flow.

[0255] Step 18 Is it peak hour or event?

[0256] Step 19 Adjusting projections during peak hours or events to improve traffic efficiency.

[0257] Step 20 Integrating feedback from traffic management officials for projection refinement.

[0258] Step 21 End of traffic management procedure

[0259]

[0260]

[0272] This comprehensive embodiment of the intelligent laser and LED projection system not only enhances safety and communication across multiple applications but also adapts dynamically to the needs of various environments. Through real-time data processing, Al integration, and user- friendly interfaces, the system dramatically improves situational awareness and operational efficiency in contexts ranging from urban traffic management to healthcare and emergency response.

[0261]

[0273] In summary, the intelligent projection system exemplifies a significant advancement in technology, combining predictive analytics, environmental monitoring, and customizable user interactions to create a safer and more responsive environment for users in diverse settings.

[0262]

[0274] Turning first to Figure 1, a system block diagram 100 illustrates the primary components of the intelligent projection system. The system is controlled by a central Al Processing Unit 102. The Al Processing Unit 102 receives data from a multi-modal Sensor Suite 104, which may include sensors such as LIDAR 106, Cameras 108, and other environmental sensors 110 (e.g., acoustic, thermal). The Al Processing Unit 102 executes software logic, including machine learning models, to analyze the sensor data. Based on its analysis, it sends commands to at least one Projection Unit 112, which may be a Laser Projector 114 or an LED Projector 116. The system can optionallyinteract with a User 120 via a User Interface 118, which allows for control and feedback. The system may also communicate with external systems via a V2X or Network Interface 122.

[0263]

[0275] Turning to Figure 2, a flowchart illustrates a method 200 for roadway safety. The process begins at step 201, where the intelligent projection system is deployed along a roadway. At step 202, sensors such as LIDAR and cameras gather real-time traffic data. This data is continuously analyzed by the AI processing unit at step 203. Based on the analysis, the system generates adaptive projections, such as dynamic crosswalks or lane guides, at step 204. At decision step 205, the system determines if a hazardous condition, such as an accident or excessive speed, is detected. If no hazard is detected, the process loops back to continue monitoring traffic data at step 202. If a hazard is detected, the system projects explicit hazard warnings at step 206 and simultaneously sends an alert to a traffic management center at step 207. The process concludes at step 208.

[0264]

[0276] Figure 3 illustrates a method 300 for enhancing residential safety. The process starts at step 301 by installing the intelligent projection system within a residence. At step 302, the system is equipped with sensors such as heat detectors and cameras. The Al processing unit continuously monitors and analyzes sensor data at step 303. At decision step 304, the system determines if a potential risk, such as an unattended cooking appliance, has been detected. If no risk is detected, the system continues to monitor at step 303. If a risk is detected, the system projects a warning alert onto a visible surface at step 305 and simultaneously notifies residents via a mobile application at step 306. At step 307, the system may receive user feedback to adapt and refine alert protocols before the process ends at step 308.

[0265]

[0277] Figure 4 depicts a method 400 for industrial site safety. The process begins at step 401 by deploying the projection system on site. At step 402, sensors are integrated to track environmental and site activity. The Al processing unit analyzes this real-time data at step 403. Based on the analysis, the system generates and updates laser projections at step 404, defining hazardous zones or machinery alignment guides. The system continuously loops between analyzing data (403) and updating projections (404). Concurrently, the system is configured to receive user feedback at step 405, which is used to optimize projection parameters at step 406 before the process concludes at step 407.

[0266]

[0278] Figure 5 shows a method 500 for emergency management. The process begins at step 501 by implementing the system in a public space or emergency center. At step 502, environmental sensors gather data. In response to a crisis trigger, the Al projects emergency alerts and evacuationpathways at step 503. The system then uses live sensor data to continuously adapt the projected routes to safe zones as the situation evolves at step 504. At step 505, the system facilitates communication with responders by projecting areas of concern. At decision step 506, the system determines if feedback from responders is available. If yes, the projection strategies are refined at step 507. The process then concludes at step 508.

[0267]

[0279] Figure 6 details a method 600 for facility navigation. The process begins at step 601 by integrating the system within a facility. At step 602, sensors and Al are used for real-time monitoring of the facility layout. At step 603, the system projects safe navigational routes and guidelines. At decision step 604, the system checks for interaction from a user's mobile device. If there is no interaction, the system continues to project the standard routes (603). If interaction occurs, projections are customized via the mobile device at step 605, and the system receives and analyzes feedback for optimization at step 606. The process concludes at step 607.

[0268] Turning to Figure 7, a method 700 for traffic management is described. The process begins at step 701 by deploying the system at critical intersections. At step 702, traffic data is analyzed using the AI processing unit. At decision step 703, the system assesses if traffic flow is normal. If it is normal, the system projects standard adaptive lane guides at step 704. The process then checks at decision step 705 if it is a peak hour or special event. If not, the system continues to project standard guides (704). If it is a peak hour or event, projections are dynamically adjusted to improve efficiency at step 706. Following either standard (704) or adjusted (706) projections, feedback from traffic officials may be integrated to refine the system at step 707, before the process ends at step 708.

[0269]

[0280] Turning now to Figure 8, a diagram illustrating a pedestrian scenario (" Peds scenario") is depicted. The figure shows a host service vehicle, represented as " OTK agent," and an " Ego" vehicle, which represents a vehicle that might be following the host vehicle. Several pedestrians, labeled " Ped," are shown in the vicinity. A coordinate system (X, Y) relative to the host vehicle is established. To manage this scenario, the AI processing unit defines specific coverage sectors: a front sector defined by coordinates 0 > X > x' and y 1 > Y > y2, and a side sector defined by xl > X > 0 and y3 > Y > 0. The Al processing unit continuously monitors these sectors for the presence of pedestrians using its object detection path. Quality parameters for this scenario include low latency (defined as the time between a pedestrian entering the Field of View (FoV) and being reliably detected), stable detection, and high pedestrian measurement accuracy, with acceptableerror tolerances for position (Err(x), Err(y)) and velocity (Err(Vx), Err(Vy)). Upon detecting a pedestrian within these sectors whose trajectory poses a risk, the AI processing unit identifies a hazardous event. In response, it may command the projection unit to project a " Caution: Pedestrian” symbol and simultaneously transmit a haptic alert to a worker's wearable device.

[0270]

[0281] Figure 9 illustrates a " Blocked over-taking path” scenario. This scenario depicts the host vehicle (" OTK agent"), a following ”Ego" vehicle, and curved lines indicating the boundary of a potential overtaking path that is obstructed. For this scenario, the AI processing unit utilizes its occupancy path to detect stationary or slow-moving obstacles within the defined coverage sectors. The quality parameters emphasize low latency and high accuracy for occupancy measurement, with defined error tolerances for the position of the obstruction (Err(x), Err(y)). If the AI processing unit detects an obstruction of a minimum predefined size that encroaches upon or completely blocks the safe overtaking path, it identifies a "blocked path" event. In response, the system is commanded to project a clear " Do Not Pass" or " Stop" signal onto the road surface to prevent the following vehicle from attempting a dangerous maneuver.

[0271]

[0282] Figure 10 depicts an " Oncoming traffic vehicle scenario” involving a larger vehicle such as a car or truck. The host vehicle (" OTK agent") and following vehicle (" Ego") are shown in one lane, while a " Target" vehicle is detected approaching in an adjacent lane (" Lane: next lane"). The Al processing unit’s coverage parameters for this event include monitoring the adjacent lane and calculating a Time to Collision (TTC) for any detected target. An event is triggered if the calculated TTC is less than a predefined safety threshold value (V). The quality parameters for this scenario are critical and include low latency and high measurement accuracy for the target's position, its velocity components (Err(Vx), Err(Vy)), and the calculated TTC (Err(TTC)). If the system detects an oncoming vehicle in the adjacent lane that meets the TTC < V condition, it immediately commands the projection unit to display a high-alert " Danger: Oncoming Vehicle" or " Do Not Pass" warning.

[0272]

[0283] Figure 11 illustrates an " Oncoming traffic two-wheeler scenario." This scenario is structurally similar to that of Figure 15 but highlights a distinct and critical challenge. Two-wheeler vehicles (e.g., motorcycles, scooters) present unique detection challenges for the object detection path due to their smaller radar cross-section and visual profile, and their capacity for more rapid changes in velocity and direction. The Al processing unit's DNN models are therefore specifically trained to identify this class of objects with high confidence. The coverage and quality parameters, includingTTC calculation and error tolerances, are similar to the car / truck scenario but may be tuned to be more sensitive to account for the higher potential risk associated with two-wheelers. Upon detection of an oncoming two-wheeler that triggers the TTC threshold, the system initiates the same high-alert warning projections to prevent a potential collision.

Claims

CLAIMS1. A projection system comprising:a. at least one projection unit configured to project adaptable visual notifications onto a surface;b. a sensor suite configured to generate sensor data corresponding to a dynamic condition in an environment of the system; andc. a processing unit communicatively coupled to the sensor suite and the at least one projection unit, the processing unit configured to:i. analyze the sensor data to identify the dynamic condition; andii. in response to identifying the dynamic condition, automatically command the at least one projection unit to adjust the adaptable visual notifications to correspond to the identified dynamic condition.

2. The system of claim 1, wherein the processing unit is an artificial intelligence (Al) processing unit configured to perform real-time data analysis and decision-making.

3. The system of claim 2, wherein the AI processing unit is further configured to perform environmental object mapping using simultaneous localization and mapping (SLAM) techniques to create a dynamic model of the environment.

4. The system of claim 2, wherein the AI processing unit is configured to perform anomaly detection by identifying deviations from a baseline model of expected environmental patterns.

5. The system of claim 2, wherein the AI processing unit is further configured to utilize historical data to predictively adjust current projection parameters.

6. The system of claim 1, wherein the at least one projection unit comprises a laser projector or a Light Emitting Diode (LED) projector.

7. The system of claim 1, wherein the sensor suite comprises at least one of a Light Detection and Ranging (LIDAR) sensor or a camera.

8. The system of claim 7, wherein the sensor suite further comprises acoustic sensors, and wherein the processing unit is configured to adjust the visual notifications in response to captured sound data.

9. The system of claim 1, further comprising a user interface for receiving user input, wherein the processing unit is configured to customize the visual notifications based on said user input.

10. The system of claim 1, wherein the sensor suite is further configured to detect user gestures, and wherein the processing unit is configured to adjust the visual notifications in response to a detected user gesture.

11. The system of claim 1, wherein the processing unit is further configured to optimize energy consumption of the at least one projection unit by adjusting projection intensity based on ambient lighting conditions.

12. The system of claim 1, wherein the processing unit is configured to execute an emergency protocol that overrides a current projection state to display a predetermined emergency alert in response to a critical event detection.

13. The system of claim 1, wherein the system is configured for fixed installation, and wherein at least one component of the system is mounted on a fixed infrastructure element selected from the group consisting of: a traffic pole, a building facade, a wall, and a gantry.

14. The system of claim 1, wherein the adaptable visual notifications comprise at least one of: a directional arrow for traffic or pedestrian guidance, a symbol indicating a status of a parking space, or a dynamic warning sign.

15. The system of claim 1, further comprising a sensor configured to be worn by an animal, wherein the adaptable visual notification comprises a focused light signal, and wherein the sensor is configured to provide a tactile cue to the animal in response to the focused light signal.

16. A method for dynamically projecting safety indicators, the method comprising:a. gathering, using a sensor suite, real-time environmental data corresponding to a dynamic condition;b. analyzing, using a processing unit, the environmental data to identify the dynamic condition; andc. generating, by commanding a projection system with the processing unit, an adaptive visual projection corresponding to the identified dynamic condition.

17. The method of claim 16, further comprising creating a dynamic environmental model of an area of the projection using simultaneous localization and mapping (SLAM) techniques.

18. The method of claim 16, wherein the adaptive visual projection is projected onto a roadway to guide vehicular traffic.

19. The method of claim 16, wherein the adaptive visual projection is projected within a residential environment to provide a warning alert for a detected household risk.

20. The method of claim 16, wherein the adaptive visual projection is projected within an industrial environment to delineate a hazardous zone.

21. The method of claim 16, further comprising refining, using a machine learning algorithm, a prediction model for identifying future conditions based on the gathered environmental data over time.

22. The method of claim 16, further comprising communicating the identified dynamic condition to a remote management center.

23. A vehicle-mounted safety system for managing traffic, the system comprising:a. an Al processing unit configured to be mounted on a host vehicle;b. a sensor suite in communication with the AI processing unit, the sensor suite configured to provide data indicative of an operational state of the host vehicle and to detect objects in an external environment of the host vehicle; andc. at least one projection unit controlled by the AI processing unit, configured to project dynamic visual notifications onto a road surface;wherein the AI processing unit is configured to:i. autonomously determine the operational state of the host vehicle based on the data from the sensor suite; andii. in response to said determination, automatically command the projection unit to project a specific dynamic visual notification to guide traffic relative to the host vehicle.

24. The system of claim 23, wherein the host vehicle is a service vehicle selected from the group consisting of: a sanitation truck, a maintenance vehicle, and an infrastructure vehicle.

25. The system of claim 23, wherein the specific dynamic visual notification is selected from a predefined vocabulary of cues comprising at least one of: a directional arrow indicatinga safe overtaking path, a 'stop' signal, a 'slow down' warning, or a pattern indicating a safe following distance.

26. The system of claim 23, wherein the sensor suite comprises a Global Positioning System (GPS) receiver and an Inertial Measurement Unit (IMU), and wherein the AI processing unit is configured to execute a sensor fusion algorithm to analyze data therefrom to distinguish between an operational stop and a non-operational stop of the host vehicle.

27. The system of claim 26, wherein the sensor fusion algorithm is further configured to identify a stop-start cadence characteristic of a service operation to distinguish the operational stop from the non-operational stop.

28. The system of claim 26, wherein the sensor suite is further configured to receive data from the host vehicle's Controller Area Network (CAN) bus, the data indicating a state of auxiliary equipment of the host vehicle.

29. The system of claim 26, wherein the AI processing unit comprises at least two parallel processing paths for analyzing data from the sensor suite: an object detection path utilizing a deep neural network (DNN) and an occupancy path generating a dynamic occupancy grid.

30. The system of claim 23, further comprising an articulated spotlight controlled by the AI processing unit to provide follow-me illumination directed toward an area of worker activity or a detected hazard.

31. The system of claim 23, further comprising a worker protection subsystem, said subsystem comprising:a. a wearable device for a worker, the wearable device comprising an alert mechanism; andb. a wireless transmitter controlled by the AI processing unit;wherein the AI processing unit is further configured to transmit an alert signal via the transmitter to the wearable device upon detecting a hazardous condition for the worker in the external environment.

32. The system of claim 31, wherein the alert mechanism of the wearable device comprises a haptic vibrator.

33. The system of claim 31, wherein the wearable device is integrated into a safety vest or a helmet.

34. The system of claim 23, wherein the AI processing unit is further configured to communicate with a Vehicle-to-Everything (V2X) network.

35. A worker protection system for use with a host vehicle, the system comprising:a. a wearable device configured to be worn by a worker, the wearable device comprising a haptic alert mechanism; andb. a control unit configured to be mounted on the host vehicle, the control unit comprising:i. a sensor interface for receiving data from a sensor suite monitoring an environment external to the host vehicle;ii. a processor configured to analyze the sensor data to detect a hazardous condition for the worker, the hazardous condition comprising an approaching vehicle exceeding a predefined speed threshold or following a collision trajectory with the worker; and iii. a wireless transmitter configured to, upon detection of the hazardous condition, transmit an alert signal to the wearable device to cause the haptic alert mechanism to activate.

36. The system of claim 35, wherein the wearable device is integrated into an article of personal protective equipment (PPE) selected from the group consisting of: a safety vest, a helmet, and a wristband.

37. A method for autonomously managing traffic around a host vehicle, the method comprising:a. determining, using an Al processing unit and a sensor suite mounted on the host vehicle, an operational state of the host vehicle;b. detecting, using the sensor suite, one or more approaching vehicles in a vicinity of the host vehicle;c. autonomously selecting, by the AI processing unit, a dynamic visual cue corresponding to the determined operational state and the detected approaching vehicles; and d. projecting, using at least one projection unit on the host vehicle, the selected dynamic visual cue onto a road surface to guide the one or more approaching vehicles.

38. The method of claim 37, wherein determining the operational state comprises executing a sensor fusion algorithm on data from a GPS receiver, an IMU, and the host vehicle's CAN bus to distinguish between an operational stop and a non-operational stop.

39. The method of claim 37, further comprising:a. identifying, by the AI processing unit, a potential threat to a worker, wherein the potential threat comprises an approaching vehicle exceeding a speed threshold; and b. wirelessly transmitting an alert signal to a wearable device worn by the worker to activate an alert mechanism.

40. The method of claim 39, wherein activating the alert mechanism comprises causing a haptic vibrator on the wearable device to vibrate.

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