Robotic traffic flagger device with autonomous operation

Abstract

A robotic traffic flagger device with autonomous operation, comprising: at least one motorized mobile platform; at least one sensor that collects environment data; and a navigation module that controls the at least one motorized mobile platform to autonomously navigate the robotic traffic flagger device based on the environment data. The robotic traffic flagger device may further autonomously navigate the robotic traffic flagger device based on a workflow within a traffic control zone. The robotic traffic flagger device may further include an environment monitoring module that generates instructions for the automated flagger assistance device to control vehicle traffic within a traffic control zone based on the environment data; and at least one signaling device to control the vehicle traffic within the traffic control zone.

Description

[0001] ROBOTIC TRAFFIC FLAGGER DEVICE WITH AUTONOMOUS OPERATION

[0002] CROSS-REFERENCE TO RELATED APPLICATION

[0003] This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application Serial No. 63 / 708,228 filed October 16, 2024, which is incorporated herein in its entirety by reference.

[0004] FIELD

[0005] The present disclosure is generally directed to traffic management systems, in particular, toward methods and devices for autonomous traffic management in control zones, such as, but not limited to, construction and work sites.

[0006] BACKGROUND

[0007] The background description includes information that may be useful in understanding the present inventive subject matter. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.

[0008] Construction and roadwork may cause disruptions to normal vehicle traffic due to lane closures or other temporary impediments. Currently, the majority of such construction sites utilize human workers to coordinate traffic flow. For example, two workers — commonly referred to as flaggers — are positioned at either end of the traffic control zone. The flaggers, equipped with signs to instruct vehicles, communicate with each other visibly or using radios to synchronize their actions, stopping one lane of traffic while letting the other lane of traffic to proceed. The flaggers are constantly exposed to potential collisions from personal motor vehicles and construction equipment. Fatigue, distraction, low visibility, and perception can also aggregate the potential risks to human flaggers.

[0009] There also exist remote control flagger assistance devices, where workers can operate traffic lights or electronic traffic signs using a wireless remote control. While these products are an improvement over human flaggers alone, they require a dedicated human operator to continuously monitor traffic and control the signs.

[0010] SUMMARY

[0011] Various embodiments according to the present disclosure are directed to an autonomous robotic flagger device that can be used to control traffic. In one or more embodiments, the robotic flagger device is operable to obtain environment data to classify objects within a workflow and / or work zone, including without limitation, vehicles, pedestrians, and construction equipment. In embodiments, the robotic flagger device is operable to interpret and / or use ascertained data relating to a traffic workflow or zone to make informed traffic control decisions, including decisions that ensure safety. The robotic flagger device generally includes hardware and software that is able to process and combine raw data from multiple sensors to create a unified environmental model. Furthermore, in some embodiments, the robotic flagger device is structured to dynamically and / or automatically adjust the focus areas of sensors based on real-time traffic conditions and / or operator input.

[0012] In various embodiments, the robotic flagger device comprises a sensor(s), e.g., cameras, ultrasound sensors, infrared sensors, radar sensors, LiDAR sensors, pressure sensors, proximity / di stance sensors or other types of sensors. In embodiments, the sensor(s) is structured to collect data to determine information about the surroundings of the robotic flagger device, for example, recognize and / or classify traffic patterns, including to trigger pre-defined flagging behaviors and / or signal timings.

[0013] In embodiments, the robotic flagger includes a module for generating movement trajectories of the robotic flagger device based on the predicted paths of vehicles and pedestrians in the work zone. In some embodiments, movement trajectories are precalculated. In at least one embodiment, the robotic flagger comprises a control strategy for improving traffic flow by dynamically adjusting the traffic signals and / or flagger positions based on real-time traffic density and / or speed data obtained from the sensors. In embodiments, the robotic flagger device is operable to dynamically adjust signal timings or flagger positions considering factors such as queue lengths, vehicle speeds, and time-of- day.

[0014] In various embodiments, the robotic flagger is structured to make traffic determinations within a control zone, e.g., how many vehicles, speed of vehicles, number of vehicles travelling in a first and / or a second direction within the control zone. In at least one embodiment, the robotic flagger is structured to ascertain the speeds of approaching vehicles and compare them against pre-defined thresholds to activate warning signals or adjust flagger behaviors to manage workflows and / or ensure safety.

[0015] Accordingly, in various embodiments, a robotic traffic flagger device with autonomous operation comprises: at least one motorized mobile platform, at least one sensor structured to collect environment data, and a navigation module operable to control the at least one motorized mobile platform to autonomously navigate the robotic traffic flagger device based on the environment data. In one or more embodiments, the navigation module is operable to autonomously navigate the robotic traffic flagger device based on a workflow within a traffic control zone.

[0016] In at least one embodiment, the navigation module the navigation module is operable to autonomously recognize and classify traffic patterns and / or trigger predefined traffic behaviors and / or signal timings based on the recognized traffic patterns.

[0017] In one or more embodiments, the robotic traffic flagger device comprises an environment monitoring module structured to generate instructions for the robotic traffic flagger device to control vehicle traffic within a control zone based on the environment data.

[0018] In at least one embodiment, the robotic traffic flagger device comprises at least one signaling device operable to control vehicle traffic within the traffic control zone.

[0019] In at least one embodiment, the robotic traffic flagger device comprises an environment monitoring module structured to dynamically adjust signal timings or robotic traffic flagger device positions to at least improve or optimize traffic flow through the traffic control zone.

[0020] In one or more embodiments, the robotic traffic flagger device comprises an environment monitoring module that is operable to dynamically adjust signal timings or robotic traffic flagger positions to optimize traffic flow based on one or more of the following factors: queue length, vehicle speed and time-of-day.

[0021] In embodiments of the robotic traffic flagger device, the environment monitoring module is operable to dynamically adjust signal timings or robotic traffic flagger positions by comparing ascertained speeds of approaching vehicles to predefined thresholds.

[0022] In one or more embodiments of the robotic traffic flagger device, the environment monitoring module is operable to dynamically activate warning signals or adjust signal timing or robotic flagger positions to enforce predetermined vehicle speeds.

[0023] In various embodiments of the robotic traffic flagger device, the at least one sensor is operable to ascertain real-time positions of workers within the traffic control zone.

[0024] In one or more embodiments of the robotic traffic flagger device, the environment monitoring module is operable to dynamically adjust robotic traffic flagger positions based on ascertained real-time positions of workers within a traffic control zone such that the motorized mobile platform stays within a threshold distance of workers or the workflow.

[0025] In various embodiments of the robotic traffic flagger device, the environment module is operable to dynamically adjust signal timings or flagger positions based on environmental conditions detected by the at least one sensor to maintain an effective traffic control in the traffic control zone. In one or more embodiment of the traffic flagger device, the environment monitoring module is operable to dynamically adjust signal timings or flagger positions based on environmental conditions comprising weather changes, lighting conditions, and road surface quality, to maintain effective traffic control in the traffic control zone.

[0026] In some embodiments of the traffic flagger device, the environment monitoring module is operable to detect an unsafe condition and / or activate an alarm in response to detecting the unsafe condition.

[0027] In various embodiments of the traffic flagger device, the at least one sensor includes at least one of: a camera, a radar, a Light Detection and Ranging (LiDAR) sensor, RADAR sensors, and ultrasonic sensor.

[0028] In one or more embodiments of the traffic flagger device, the at least one sensor comprises at least two rear-view cameras operable to process acquired rear-view images to generate a stereo view comprising depth data for targets visible by all of the at least two rear-view cameras.

[0029] In one or more embodiments of the robotic traffic flagger device, the at least one motorized mobile platform includes an electric drivetrain.

[0030] In one or more embodiments, the robotic traffic flagger device further comprises a rechargeable battery system operable to at least partially power the robotic traffic flagger device.

[0031] In some embodiments, the robotic traffic flagger device comprises a rechargeable battery system is at least partially rechargeable using one or more solar panels.

[0032] In embodiments, the robotic traffic flagger device comprises a cellular transceiver operable to control the robotic traffic flagger device from any distance.

[0033] In one or more embodiments, the robotic traffic flagger device comprises telematic components operable to control or monitor the robotic traffic flagger device from a remote location.

[0034] In at least one embodiment, the robotic traffic flagger device comprises telematic components operable to control or monitor the robotic traffic flagger device from a remote location that comprises less than 100 ms of latency.

[0035] In one or more embodiments, the robotic traffic flagger device comprises a communication interface operable to allow the robotic traffic flagger device to wirelessly exchange information with connected equipment.

[0036] Other embodiments according to the present disclosure are directed to a method of operating a robotic traffic flagger device. In one or more embodiments, the method comprises a robotic flagger device as described herein. That is, in embodiments the method comprises providing a robotic traffic flagger device with autonomous operation, the robotic traffic flagger device comprising: at least one motorized mobile platform; at least one sensor structured to collect environment data; and a navigation module operable to control the at least one motorized mobile platform to autonomously navigate the robotic traffic flagger device based on the environment data.

[0037] In one or more embodiments, the method comprises using the at least one sensor to collect environment data.

[0038] In embodiments, the method comprises processing collected environment data to autonomously navigate the robotic flagger device based on a workflow within a control zone.

[0039] In one or more embodiments, the method comprises processing the collected environment data to control traffic within the control zone comprising dynamically adjusting signal timings or flagger positions to optimize traffic flow through the control zone considering at least one of the following factors: queue lengths, vehicle speeds, and time-of-day.

[0040] A first aspect of the present disclosure is to provide a robotic traffic flagger device with autonomous operation, comprising at least one motorized mobile platform; at least one sensor structured to collect environment data; and a navigation module operable to control the at least one motorized mobile platform to autonomously navigate the robotic traffic flagger device based on the environment data.

[0041] The device of the first aspect may include, optionally, that the navigation module is operable to autonomously navigate the robotic traffic flagger device based on a workflow within a traffic control zone.

[0042] The device of the first aspect may include one or more of the previous embodiments and, optionally, that the navigation module is operable to autonomously recognize and classify traffic patterns and trigger predefined traffic behaviors and signal timings based on the recognized traffic patterns.

[0043] The device of the first aspect may include one or more of the previous embodiments and, optionally, an environment monitoring module operable to generate instructions for the robotic traffic flagger device to control vehicle traffic within a traffic control zone based on the environment data; and at least one signaling device operable to control the vehicle traffic within the traffic control zone. The device of the first aspect may include one or more of the previous embodiments and, optionally, that the environment monitoring module is operable to dynamically adjust signal timings or robotic traffic flagger positions to control traffic flow through the traffic control zone.

[0044] The device of the first aspect may include one or more of the previous embodiments and, optionally, that the environment monitoring module is operable to dynamically adjust signal timings or robotic traffic flagger positions to optimize traffic flow based on one or more of the following factors: queue lengths, vehicle speeds, and time-of-day.

[0045] The device of the first aspect may include one or more of the previous embodiments and, optionally, that the environment monitoring module is operable to dynamically adjust signal timings or robotic traffic flagger positions by comparing ascertained speeds of approaching vehicles to pre-defined thresholds; and wherein the environment monitoring module is operable to dynamically activate warning signals or adjust signal timing or robotic traffic flagger positions to enforce predetermined vehicle speeds.

[0046] The device of the first aspect may include one or more of the previous embodiments and, optionally, that the navigation module is operable to autonomously navigate the robotic traffic flagger device based on a workflow within the traffic control zone that is a construction zone, and wherein the at least one sensor is operable to ascertain real-time positions of workers within the traffic control zone.

[0047] The device of the first aspect may include one or more of the previous embodiments and, optionally, that the environment monitoring module is operable to dynamically adjust robotic traffic flagger positions based on ascertained real-time positions of workers within the traffic control zone such that the at least one motorized mobile platform stays within a threshold distance of the workers or the workflow.

[0048] The device of the first aspect may include one or more of the previous embodiments and, optionally, that the environment monitoring module is operable to dynamically adjust signal timings or flagger positions based on environmental conditions detected by the at least one sensor to maintain an effective traffic control in the traffic control zone, wherein the environmental conditions detected by the at least one sensor comprise weather changes, lighting conditions, and road surface quality.

[0049] The device of the first aspect may include one or more of the previous embodiments and, optionally, that the environment monitoring module is operable to detect an unsafe condition; and wherein the robotic traffic flagger device is operable to activate an alarm in response to detecting the unsafe condition. The device of the first aspect may include one or more of the previous embodiments and, optionally, that the at least one sensor includes at least one of: a camera, a radar, a Light Detection and Ranging (LiDAR) sensor, RADAR sensors, and ultrasonic sensor.

[0050] The device of the first aspect may include one or more of the previous embodiments and, optionally, that the at least one sensor comprises at least two rear-view cameras operable to process acquired rear-view images to generate a stereo view comprising depth data for targets visible by all of the at least two rear-view cameras.

[0051] The device of the first aspect may include one or more of the previous embodiments and, optionally, a cellular transceiver operable to control the robotic traffic flagger device from any distance.

[0052] A second aspect of the present disclosure is to provide a flagger device, comprising a signaling device configured to produce a signal to control traffic; a mobile platform configured to move the flagger device; a plurality of sensors configured to detect environmental data; a control system configured to construct an environmental model based on the environmental data, wherein the environmental model comprises at least one of a workflow, a worker location, a traffic pattern, and an environment state; wherein the control system is configured to cause the signaling device to change the signal from a first state to a second state based on the environmental model; and wherein the control system is configured to cause the mobile platform to move the flagger device from a first position to a second position based on the environmental model.

[0053] The device of the second aspect may include, optionally, that the traffic pattern comprises one of a queue length, an average speed of a plurality of vehicles, and a time-of- day.

[0054] The device of the second aspect may include one or more of the previous embodiments and, optionally, that the control system is configured to cause the mobile platform to move the flagger device within a predetermined distance of the worker location or the workflow.

[0055] The device of the second aspect may include one or more of the previous embodiments and, optionally, that a control zone extends from the flagger device in a lateral direction, and the plurality of sensors combine to detect the environmental data in the control zone.

[0056] The device of the second aspect may include one or more of the previous embodiments and, optionally, that a detection zone of one sensor of the plurality of sensors and a detection zone of another sensor of the plurality of sensors overlap. A third aspect of the present disclosure is to provide a method of operating a robotic traffic flagger device, the method comprising providing a robotic traffic flagger device with autonomous operation, the robotic traffic flagger device comprising at least one motorized mobile platform; at least one sensor structured to collect environment data; and a navigation module operable to control the at least one motorized mobile platform to autonomously navigate the robotic traffic flagger device based on the environment data; using the at least one sensor to collect environment data; processing the collected environment data to autonomously navigate the robotic traffic flagger device based on a workflow within a control zone; and processing the collected environment data to control traffic within the control zone comprising dynamically adjusting signal timings or flagger positions to optimize traffic flow through the control zone considering at least one of the following factors: queue lengths, vehicle speeds, and time-of-day.

[0057] The phrases “at least one”, “one or more”, and “and / or”, as used herein, are open- ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “one or more of A, B, or C,” and “A, B, and / or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.

[0058] Unless otherwise indicated, all numbers expressing quantities, dimensions, conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term “about” or “approximately”. As used herein, unless otherwise specified, the terms “about,” “approximately,” etc., when used in relation to numerical limitations or ranges, mean that the recited limitation or range may vary by up to 10%. By way of non-limiting example, “about 750” can mean as little as 675 or as much as 825, or any value therebetween. When used in relation to ratios or relationships between two or more numerical limitations or ranges, the terms “about,” “approximately,” etc. mean that each of the limitations or ranges may vary by up to 10%; by way of non-limiting example, a statement that two quantities are “approximately equal” can mean that a ratio between the two quantities is as little as 0.9: 1.1 or as much as 1.1 :0.9 (or any value therebetween), and a statement that a four-way ratio is “about 5:3: 1 : 1” can mean that the first number in the ratio can be any value of at least 4.5 and no more than 5.5, the second number in the ratio can be any value of at least 2.7 and no more than 3.3, and so on.

[0059] The use of “substantially” in the present disclosure, when referring to a measurable quantity (e.g., a diameter or other distance) and used for purposes of comparison, is intended to mean within 5% of the comparative quantity. The terms “substantially similar to,” “substantially the same as,” and “substantially equal to,” as used herein, should be interpreted as if explicitly reciting and encompassing the special case in which the items of comparison are “similar to,” “the same as” and “equal to,” respectively.

[0060] The term “a” or “an” entity, as used herein, refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more” and “at least one” can be used interchangeably herein.

[0061] The use of “including,” “comprising,” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. Accordingly, the terms “including,” “comprising,” or “having” and variations thereof can be used interchangeably herein. The use of “engaged with” and variations thereof herein is meant to encompass any direct or indirect connections between components.

[0062] It shall be understood that the term “means” as used herein shall be given its broadest possible interpretation in accordance with 35 U.S.C. § 112(f). Accordingly, a claim incorporating the term “means” shall cover all structures, materials, or acts set forth herein, and all of the equivalents thereof. Further, the structures, materials, or acts and the equivalents thereof shall include all those described in the Summary, Brief Description of the Drawings, Detailed Description, Abstract, and claims themselves. Moreover, any Appendixes enclosed herein are incorporated herein in their entireties.

[0063] These and other advantages will be apparent from the disclosure of the invention(s) contained herein. The above-described embodiments, objectives, and configurations are neither complete nor exhaustive. The Summary is neither intended nor should it be construed as being representative of the full extent and scope of the present disclosure. Moreover, references made herein to “the present disclosure” or aspects thereof should be understood to mean certain embodiments of the present disclosure and should not necessarily be construed as limiting all embodiments to a particular description. The present disclosure is set forth in various levels of detail in the Summary as well as in the attached drawings and the Detailed Description and no limitation as to the scope of the present disclosure is intended by either the inclusion or non-inclusion of elements, components, etc. in this Summary. Additional aspects of the present disclosure will become more readily apparent from the Detailed Description, particularly when taken together with the drawings.

[0064] It is to be appreciated that any feature or aspect described herein can be claimed in combination with any other feature(s) or aspect(s) as described herein, regardless of whether the features or aspects come from the same described embodiment. Any one or more aspects described herein can be combined with any other one or more aspects described herein. Any one or more features described herein can be combined with any other one or more features described herein. Any one or more embodiments described herein can be combined with any other one or more embodiments described herein.

[0065] BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Those of skill in the art will recognize that the following description is merely illustrative of the principles of the disclosure, which may be applied in various ways to provide many different alternative embodiments. This description is made for illustrating the general principles of the teachings of this disclosure and is not meant to limit the inventive concepts disclosed herein.

[0067] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and together with the general description of the disclosure given above and the detailed description of the drawings given below, serve to explain the principles of the disclosure.

[0068] Fig. 1 shows an example flagger device;

[0069] Fig. 2A shows an example robotic flagger device in accordance with embodiments of the present disclosure;

[0070] Fig. 2B shows a plan view of the robotic flagger device in accordance with at least some embodiments of the present disclosure;

[0071] Fig. 3A is a block diagram of an embodiment of a communication environment of the robotic flagger device in accordance with embodiments of the present disclosure;

[0072] Fig. 3B is a block diagram of an embodiment of a navigation system of the robotic flagger device in accordance with embodiments of the present disclosure;

[0073] Fig. 4 is a block diagram of a computing environment associated with the embodiments presented herein;

[0074] Fig. 5 is a block diagram of a computing device associated with one or more components described herein;

[0075] Fig. 6 is a flow diagram illustrating example operations of the system(s) in Figs. 1- 5; and

[0076] Fig. 7 is a block diagram illustrating power management of the system(s) in Figs. 1- 5.

[0077] It should be understood that the drawings are not necessarily to scale, and various dimensions may be altered. In certain instances, details that are not necessary for an understanding of the disclosure or that render other details difficult to perceive may have been omitted. It should be understood, of course, that the disclosure is not necessarily limited to the particular embodiments illustrated herein. It is noted that any line in the drawings may be illustrated as solid or broken lines, including any section or length of each individual line, without departing from the scope of the present disclosure. It will be appreciated that recitation of, for example, reference character 116, 116A, 116B, etc. may apply to any combination of reference characters 116, 116A, 116B, etc.

[0078] 101 Automated Flagger Assistance Device

[0079] 102a Traffic Signaling Device

[0080] 102b Traffic Signaling Device

[0081] 104 Base

[0082] 106 Camera

[0083] 110 Front

[0084] 116 Sensor

[0085] 116A Sensor

[0086] 116B Sensor

[0087] 116C Sensor

[0088] 116D Sensor

[0089] 116E Sensor

[0090] 116F Sensor

[0091] 116G Sensor

[0092] 116H Sensor

[0093] 120 Rear

[0094] 200 Control Zone

[0095] 201 Automated Flagger Assistance Device

[0096] 202a Traffic Signaling Device

[0097] 202b Traffic Signaling Device

[0098] 202c Traffic Signaling Device

[0099] 202d Traffic Signaling Device

[0100] 203 Mobile Platform / Base

[0101] 204 Effective Detection Limit

[0102] 208 View Zone

[0103] 212 Undetected Zone

[0104] 216A Detection Zone 216B Detection Zone

[0105] 216C Detection Zone

[0106] 216D Detection Zone

[0107] 220 Overlap Zone

[0108] 224 Virtual Intersection Point

[0109] 300 Communication System

[0110] 302 Navigation System

[0111] 304 Sensor

[0112] 308 Navigation Sensor

[0113] 312 Orientation Sensor

[0114] 316 Odom etry Sensor

[0115] 320 LIDAR Sensor

[0116] 324 RADAR Sensor

[0117] 328 Ultrasonic Sensor

[0118] 331 GPS Antenna / Receiver

[0119] 332 Camera Sensor

[0120] 333 Location Module

[0121] 335 Maps Database

[0122] 336 Infrared Sensor

[0123] 338 Other Sensor

[0124] 340 Sensor Processor

[0125] 344 Sensor Data Memory

[0126] 348 Control System

[0127] 350 Communications Subsystem

[0128] 352 Communication Network

[0129] 356A Navigation Source

[0130] 356B Control Source

[0131] 356N Other S ource

[0132] 360 Bus

[0133] 364 Control Data Memory

[0134] 368 Computing Device

[0135] 372 Display Device

[0136] 374 Other Component

[0137] 378 Power Source 380 Alarm

[0138] 400 Computing Environment

[0139] 404 Robotic Flagger Device

[0140] 408 Communication Device

[0141] 412 Additional Device

[0142] 414 Web Server

[0143] 416 Server

[0144] 418 Database

[0145] 500 Computer System

[0146] 504 Bus

[0147] 508 Processor

[0148] 512 Input Device

[0149] 516 Output Device

[0150] 520 Storage Device

[0151] 524 Computer-Readable Storage Media Reader

[0152] 528 Communication System

[0153] 532 Processing Unit

[0154] 536 Working Memory

[0155] 540 Operating System

[0156] 544 Other Code

[0157] 600 Method

[0158] 605 Operation

[0159] 610 Operation

[0160] 615 Operation

[0161] 620 Operation

[0162] 625 Operation

[0163] DETAILED DESCRIPTION

[0164] Although the following text sets forth a detailed description of numerous different embodiments, it should be understood that the legal scope of the description is defined by the words of the claims set forth at the end of this disclosure. The Detailed Description is to be construed as exemplary only and does not describe every possible embodiment of the flagger since describing every possible embodiment would be impractical, if not impossible. Numerous alternative embodiments could be implemented, using either current technology or technology developed after the filing date of this patent, which would still fall within the scope of the claims. Additionally, any combination of features shown in the various figures can be used to create additional embodiments of the present disclosure. Thus, dimensions, aspects, and features of one embodiment of the flagger can be combined with dimensions, aspects, and features of another embodiment of the flagger to create the claimed embodiment.

[0165] The robotic flagger of the present disclosure can autonomously drive itself along a route without human intervention. Autonomous driving technologies, also known as selfdriving or driverless technologies, use a combination of technologies to navigate a vehicle from one destination to another without a human driver.

[0166] These technologies include artificial intelligence (Al), machine-learning, and deep learning algorithms that interpret sensory data from onboard sensors to help the vehicle understand its environment and make decisions; sensors (e.g., Light Detection and Ranging (LiDAR), radar, and GPS); mapping and navigation systems; and wireless infrastructure that allows the vehicle to share information with other vehicles.

[0167] To ensure safety and proper operation, the robotic flagger includes a mechanism for a remote operator to immediately override autonomous functions and initiate a controlled stop of the flagger in the event of system errors, safety concerns, or unusual situations. Additionally, the robotic flagger may implement redundancy and fault tolerance in the autonomous flagger's systems, ensuring that, in case of a sensor or component failure, the system can continue to operate safely and effectively. The autonomous functionality may also be inhibited based on conditions at the worksite (e.g., weather such as heavy rain), changes to the vehicle’s ability to receive accurate navigation information (e.g., a GPS dead zone), low battery, detected latency exceeding a threshold, etc.

[0168] Additionally, the robotic flagger includes telematics components that allow the flagger to be controlled / monitored for any location in the country with low latency (e.g., with < 100 ms of latency and more preferably with < 50ms of latency). In embodiments, the telematics control allows an operator to control the robotic flagger from a safe distance and have a 360o view at any point in time. Telematics control may also allow for two-way communication between an operator and a worker working on-site. Additionally, the robotic flagger may have cellular capabilities that allow data to be sent to / from the robotic flagger.

[0169] In embodiments, the robotic flagger is capable of identifying and classifying objects, such as vehicles, pedestrians, and construction equipment, and using this information to make informed traffic control decisions and ensure safety. The robotic flagger includes hardware and software that is able to process and combine raw data from multiple sensor types to create a unified environmental model, wherein said model prioritizes object detection, classification, and tracking accuracy based on sensor confidence levels. Additionally, the robotic flagger may adaptively and dynamically adjust the focus areas of sensors based on real-time traffic conditions and operator input, wherein said focus area selection optimizes computational resources and prioritizes safety-critical zones.

[0170] The robotic flagger may include cameras and other sensors (e.g., ultrasound sensors, infrared sensors, radar sensors, LiDAR sensors, pressure sensors, proximity / di stance sensors, etc.) that collect data used to determine information about the surroundings of the robotic flagger. In embodiments, the robotic flagger is able to autonomously recognize and classify traffic patterns (e.g., stop-and-go, intermittent flow, free flow) and trigger predefined flagging behaviors and signal timings based on the recognized traffic patterns.

[0171] In embodiments, the robotic flagger includes a module for generating safe and efficient movement trajectories for the flagger based on the predicted paths of vehicles and pedestrians in the work zone. These movement trajectories may be pre-calculated (e.g., prior to starting a movement) to ensure the trajectory is able to be travelled and the robotic flagger is able to complete the trajectory in the case of a sensor failure. Additionally, the robotic flagger may include a control strategy for improving traffic flow by dynamically adjusting the traffic signals and flagger positions based on real-time traffic density and speed data obtained from the sensors, thereby minimizing congestion and delays. In embodiments, the robotic flagger dynamically adjusts signal timings or flagger positions to optimize traffic flow through the work zone, considering factors such as queue lengths, vehicle speeds, and time-of-day.

[0172] For example, the robotic flagger may make determinations about traffic within the control zone (e.g., how many vehicles, speed of vehicles, number of vehicles travelling in a first direction compared to the number of vehicles travelling in a second direction opposite to the first direction). The robotic flagger may also make determinations about a construction zone within the control zone. For example, the flagger may monitor the progress of workers in the construction zone in order to autonomously stay within a threshold distance of the workers or workflow. Continuing the example, if the construction workers are paving a portion of the road, the robotic flagger may be able to move with the workers and paving equipment as the paving is performed. In another example, the flagger may monitor the traffic and the workers to ensure worker safety. The robotic flagger may detect a vehicle traveling above the speed limit and notify workers in the area. In embodiments, the robotic flagger may continuously measure / monitor the speeds of approaching vehicles and compare them against pre-defined thresholds, and activate warning signals or adjust flagger behaviors to enforce safe speeds. Additionally, the robotic flagger may use a wireless / cellular network for broadcasting alerts and warnings to crew members or connected devices in the work zone when safety-critical events are detected (e.g., unauthorized entry, excessive speeds, equipment malfunctions).

[0173] The robotic flagger may use an electric power train (e.g., 48-volt DC electrical system) or any other type of power train (e.g., an internal combustion engine). The electric power train may include a rechargeable battery that may be recharged using solar panels attached to the robotic flagger. The robotic flagger may have a maximum speed < 25 miles / hour. Preferably, the robotic flagger travels at speeds up to 15 miles / hour. In embodiments, the robotic flagger is able to manage its electric power train, including battery usage and recharging to optimize the use of a solar power unit to extend operational time and reduce downtime. Additionally, the robotic flagger’s operations may be adjusted based on environmental conditions detected by the sensors, such as weather changes, lighting conditions, and road surface quality, to maintain effective traffic control. The robotic flagger may provide an alert or indication when the battery is low or when the flagger needs attention. For example, the robotic flagger may initiate a text message or email to an operator regarding any issues.

[0174] Fig. 1 shows an example Automated Flagger Assistance Device (AFAD) 101. The AFAD 101 includes traffic signaling devices 102a-b, a base 104, and a camera 106. The AFAD 101 is provided for illustrative purposes, and it is understood that the AFAD 101 may include other components not shown for clarity.

[0175] Fig. 2A illustrates another example Automated Flagger Assistance Device (AFAD) 201. The AFAD 201 includes traffic signaling devices 202a-d, and a mobile platform / base 203. AFAD 201 may include other components not shown. The AFAD 101 is provided for illustrative purposes, and it is understood that the AFAD 101 may include other components not shown for clarity. The base 203 of the AFAD 201 includes wheels that allows the AFAD 201.

[0176] In some embodiments, the AFAD 201 may include a number of sensors, devices, and / or systems that are capable of assisting in autonomous or semi-autonomous control. Examples of the various sensors and systems may include, but are in no way limited to, one or more of cameras (e.g., independent, stereo, combined image, etc.), infrared (IR) sensors, radio frequency (RF) sensors, ultrasonic sensors (e.g., transducers, transceivers, etc.), RADAR sensors (e.g., object-detection sensors and / or systems), LiDAR (Light Imaging, Detection, And Ranging) systems, odometry sensors and / or devices (e.g., encoders, etc.), orientation sensors (e.g., accelerometers, gyroscopes, magnetometer, etc.), navigation sensors and systems (e.g., GPS, etc.), and other ranging, imaging, and / or object-detecting sensors. The sensors may be disposed on any portion of the AFAD 201.

[0177] The sensors and systems may be selected and / or configured to suit a level of operation associated with the AFAD 201. Among other things, the number of sensors used in a system may be altered to increase or decrease information available to a control system (e.g., affecting control capabilities of the AFAD 201). Additionally or alternatively, the sensors and systems may be part of one or more advanced driver assistance systems (ADAS) associated with an AFAD 201. In any event, the sensors and systems may be used to provide driving assistance at any level of operation (e.g., from fully-manual to fully-autonomous operations, etc.) as described herein.

[0178] Referring now to Fig. 2B, a plan view of an AFAD 201 will be described in accordance with embodiments of the present disclosure. In particular, Fig. 2B shows a control zone 200 at least partially defined by the sensors and systems 116A-H, disposed in, on, and / or about the AFAD 201. Each sensor 116A-H may include an operational detection range R and operational detection angle a. The operational detection range R may define the effective detection limit, or distance, of the sensor 116A-H. In some cases, this effective detection limit may be defined as a distance from a portion of the sensor 116A-H (e.g., a lens, sensing surface, etc.) to a point in space offset from the sensor 116A-H. The effective detection limit may define a distance, beyond which, the sensing capabilities of the sensor 116A-H deteriorate, fail to work, or are unreliable. In some embodiments, the effective detection limit may define a distance, within which, the sensing capabilities of the sensor 116A-H are able to provide accurate and / or reliable detection information. The operational detection angle a may define at least one angle of a span, or between horizontal and / or vertical limits, of a sensor 116A-H. As can be appreciated, the operational detection limit and the operational detection angle a of a sensor 116A-H together may define the effective detection zone 216A-D (e.g., the effective detection area, and / or volume, etc.) of a sensor 116A-H.

[0179] In some embodiments, the AFAD 201 may include a ranging and imaging system such as LiDAR, or the like. The ranging and imaging system may be configured to detect visual information in an environment surrounding the AFAD 201. The visual information detected in the environment surrounding the ranging and imaging system may be processed (e.g., via one or more sensor and / or system processors, etc.) to generate a complete 360- degree view of control zone 200 around the AFAD 201. The ranging and imaging system may be configured to generate changing 360-degree views of the control zone 200 in realtime, for instance, as the AFAD 201 maneuvers. In some cases, the ranging and imaging system may have an effective detection limit 204 that is some distance from the center of the AFAD 201 outward over 360 degrees. The effective detection limit 204 of the ranging and imaging system defines a view zone 208 (e.g., an area and / or volume, etc.) surrounding the AFAD 201. Any object falling outside of the view zone 208 is in the undetected zone 212 and would not be detected by the ranging and imaging system of the AFAD 201.

[0180] Sensor data and information may be collected by one or more sensors or systems 116A-H, ranging and imaging system of the AFAD 201 monitoring the control zone 200. This information may be processed (e.g., via a processor, computer-vision system, etc.) to determine targets (e.g., objects, signs, people, markings, roadways, conditions, etc.) inside one or more detection zones 208, 216A-D associated with the control zone 200. In some cases, information from multiple sensors 116A-H may be processed to form composite sensor detection information. For example, data from a first sensor 116A and a second sensor 116B may be combined to form stereo image information. This composite information may increase the capabilities of a single sensor in the one or more sensors 116A- H by, for example, adding the ability to determine depth associated with targets in the one or more detection zones 208, 216A-D. Similar image data may be collected by rear view cameras (e.g., sensors 116D, 116C) aimed in a rearward 120 traveling direction of AFAD 201.

[0181] In some embodiments, multiple sensors 116A-H may be effectively joined to increase a sensing zone and provide increased sensing coverage. For instance, multiple RADAR sensors 116B disposed on the front 110 of the AFAD 201 may be joined to provide a zone 216B of coverage that spans across an entirety of the front 110 of the AFAD 201. In some cases, the multiple RADAR sensors 116B may cover a detection zone 216B that includes one or more other sensor detection zones 216A. These overlapping detection zones may provide redundant sensing, enhanced sensing, and / or provide greater detail in sensing within a particular portion (e.g., zone 216A) of a larger zone (e.g., zone 216B). Additionally or alternatively, the sensors 116A-H of the AFAD 201 may be arranged to create a complete coverage, via one or more sensing zones 208, 216A-D around the AFAD 201. In some areas, the sensing zones 216C of two or more sensors 116D, 116E may intersect at an overlap zone 220. In some areas, the angle and / or detection limit of two or more sensing zones 216C, 216D (e.g., of two or more sensors 116A-116H) may meet at a virtual intersection point 224.

[0182] The AFAD 201 may include a number of sensors 116 disposed proximal to the rear 120 of the AFAD 201. These sensors can include, but are in no way limited to, an imaging sensor, camera, IR, a radio object-detection and ranging sensors, RADAR, RF, ultrasonic sensors, and / or other object-detection sensors. Among other things, these sensors 116 may detect targets near or approaching the rear 120 of the AFAD 201. For example, a vehicle approaching the rear 120 of the AFAD 201 may be detected by one or more of the ranging and imaging system (e.g., LiDAR) 116, rear-view cameras 116, and / or rear facing RADAR sensors 116. As described above, the images from the rear-view cameras 116 may be processed to generate a stereo view (e.g., providing depth associated with an object or environment, etc.) for targets visible to both cameras 116. As another example, the AFAD 201 may be driving and one or more of the ranging and imaging system, front-facing cameras 116, front-facing RADAR sensors 116, and / or ultrasonic sensors 116 may detect targets in front of the AFAD 201. This approach may provide critical sensor information to a vehicle control system

[0183] As yet another example, the AFAD 201 may be operating and one or more of the ranging and imaging system, and / or the side-facing sensors 116 (e.g., RADAR, ultrasonic, camera, combinations thereof, and / or other type of sensor), may detect targets at a side of the AFAD 201. It should be appreciated that the sensors 116A-H may detect a target that is both at a side 160 and a front 110 of the AFAD 201 (e.g., disposed at a diagonal angle to a centerline of the AFAD 201 running from the front 110 of the AFAD 201 to the rear 120 of the AFAD 201). Additionally or alternatively, the sensors 116A-H may detect a target that is both, or simultaneously, at a side 160 and a rear 120 of the AFAD 201 (e.g., disposed at a diagonal angle to the centerline of the AFAD 201).

[0184] Figs. 3 A-3B are block diagrams of an embodiment of a communication environment or system 300 of the AFAD 201 in accordance with embodiments of the present disclosure. The communication system 300 may include one or more sensors 304, sensor processors 340, sensor data memory 344, vehicle control system 348, communications subsystem 350, control data memory 364, computing devices 368, display devices 372, power sources 378, alarm(s) 380, and other components 374 that may be associated with an AFAD 201. These associated components may be electrically and / or communicatively coupled to one another via at least one bus 360. In some embodiments, the one or more associated components may send and / or receive signals across a communication network 352 to at least one of a navigation source 356A, a control source 356B, or some other entity 356N.

[0185] In accordance with at least some embodiments of the present disclosure, the communication network 352 may comprise any type of known communication medium or collection of communication media and may use any type of protocols, such as SIP, TCP / IP, SNA, IPX, AppleTalk, and the like, to transport messages between endpoints. The communication network 352 may include wired and / or wireless communication technologies. The Internet is an example of the communication network 352 that constitutes an Internet Protocol (IP) network consisting of many computers, computing networks, and other communication devices located all over the world, which are connected through many telephone systems and other means. Other examples of the communication network 352 include, without limitation, a standard Plain Old Telephone System (POTS), an Integrated Services Digital Network (ISDN), the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), such as an Ethernet network, a Token-Ring network and / or the like, a Wide Area Network (WAN), a virtual network, including without limitation a virtual private network ("VPN"); the Internet, an intranet, an extranet, a cellular network, an infrared network; a wireless network (e.g., a network operating under any of the IEEE 802.9 suite of protocols, the Bluetooth® protocol known in the art, and / or any other wireless protocol), and any other type of packet- switched or circuit-switched network known in the art and / or any combination of these and / or other networks. In addition, it can be appreciated that the communication network 352 need not be limited to any one network type, and instead may be comprised of a number of different networks and / or network types. The communication network 352 may comprise a number of different communication media such as coaxial cable, copper cable / wire, fiber-optic cable, antennas for transmitting / receiving wireless messages, and combinations thereof.

[0186] The sensors 304 may include at least one navigation system 302 (e.g., global positioning system (GPS), etc.), orientation sensor 312, odometry sensor 316, LiDAR sensor 320, RADAR sensor 324, ultrasonic sensor 328, camera sensor 332, infrared (IR) sensor 336, and / or other sensor 338 or system. These sensors 304 may be similar, if not identical, to the sensors and systems 116A-H described in conjunction with Figs. 1 and 2.

[0187] The navigation sensor 308 may include one or more sensors having receivers and antennas that are configured to utilize a satellite-based navigation system including a network of navigation satellites capable of providing geolocation and time information to at least one component of the AFAD 201. Examples of the navigation sensor 308 as described herein may include, but are not limited to, at least one of Garmin® GLO™ family of GPS and GLONASS combination sensors, Garmin® GPS 15x™ family of sensors, Garmin® GPS 16x™ family of sensors with high-sensitivity receiver and antenna, Garmin® GPS 18x OEM family of high-sensitivity GPS sensors, Dewetron DEWE-VGPS series of GPS sensors, GlobalSat 1-Hz series of GPS sensors, other industry-equivalent navigation sensors and / or systems, and may perform navigational and / or geolocation functions using any known or future-developed standard and / or architecture.

[0188] The orientation sensor 312 may include one or more sensors configured to determine an orientation of the AFAD 201 relative to at least one reference point. In some embodiments, the orientation sensor 312 may include at least one pressure transducer, stress / strain gauge, accelerometer, gyroscope, and / or geomagnetic sensor. Examples of the orientation sensor 312 as described herein may include, but are not limited to, at least one of Bosch Sensortec BMX 160 series low-power absolute orientation sensors, Bosch Sensortec BMX055 9-axis sensors, Bosch Sensortec BMI055 6-axis inertial sensors, Bosch Sensortec BMI160 6-axis inertial sensors, Bosch Sensortec BMF055 9-axis inertial sensors (accelerometer, gyroscope, and magnetometer) with integrated Cortex M0+ microcontroller, Bosch Sensortec BMP280 absolute barometric pressure sensors, Infineon TLV493D-A1B6 3D magnetic sensors, Infineon TLI493D-W1B6 3D magnetic sensors, Infineon TL family of 3D magnetic sensors, Murata Electronics SCC2000 series combined gyro sensor and accelerometer, Murata Electronics SCC1300 series combined gyro sensor and accelerometer, other industry-equivalent orientation sensors and / or systems, which may perform orientation detection and / or determination functions using any known or future- developed standard and / or architecture.

[0189] The odometry sensor and / or system 316 may include one or more components that is configured to determine a change in position of the AFAD 201 over time. In some embodiments, the odometry sensor 316 may utilize data from one or more other sensors 304 in determining a position (e.g., distance, location, etc.) of the AFAD 201 relative to a previously measured position for the AF AD 201. Additionally or alternatively, the odometry sensors 316 may include one or more encoders, Hall speed sensors, and / or other measurement sensors / devices configured to measure a wheel speed, rotation, and / or number of revolutions made over time. Examples of the odometry sensor / system 316 as described herein may include, but are not limited to, at least one of Infineon TLE4924 / 26 / 27 / 28C high- performance speed sensors, Infineon TL4941plusC(B) single chip differential Hall wheelspeed sensors, Infineon TL5041plusC Giant Mangnetoresistance (GMR) effect sensors, Infineon TL family of magnetic sensors, EPC Model 25 SP Accu-CoderPro™ incremental shaft encoders, EPC Model 30M compact incremental encoders with advanced magnetic sensing and signal processing technology, EPC Model 925 absolute shaft encoders, EPC Model 958 absolute shaft encoders, EPC Model MA36S / MA63S / SA36S absolute shaft encoders, Dynapar™ Fl 8 commutating optical encoder, Dynapar™ HS35R family of phased array encoder sensors, other industry-equivalent odometry sensors and / or systems, and may perform change in position detection and / or determination functions using any known or future-developed standard and / or architecture.

[0190] The LiDAR sensor / system 320 may include one or more components configured to measure distances to targets using laser illumination. In some embodiments, the LiDAR sensor / system 320 may provide 3D imaging data of an environment or control zone 200 around the AFAD 201. The imaging data may be processed to generate a full 360-degree view of the environment around the AFAD 201. The LiDAR sensor / system 320 may include a laser light generator configured to generate a plurality of target illumination laser beams (e.g., laser light channels). In some embodiments, this plurality of laser beams may be aimed at, or directed to, a rotating reflective surface (e.g., a mirror) and guided outwardly from the LiDAR sensor / system 320 into a measurement environment. The rotating reflective surface may be configured to continually rotate 360 degrees about an axis, such that the plurality of laser beams is directed in a full 360-degree range around the AFAD 201. A photodiode receiver of the LiDAR sensor / system 320 may detect when light from the plurality of laser beams emitted into the measurement environment returns (e.g., reflected echo) to the LiDAR sensor / system 320. The LiDAR sensor / system 320 may calculate, based on a time associated with the emission of light to the detected return of light, a distance from the AFAD 201 to the illuminated target. In some embodiments, the LiDAR sensor / system 320 may generate over 2.0 million points per second and have an effective operational range of at least 100 meters. Examples of the LiDAR sensor / system 320 as described herein may include, but are not limited to, at least one of Velodyne® LiDAR™ HDL-64E 64-channel LiDAR sensors, Velodyne® LiDAR™ HDL-32E 32-channel LiDAR sensors, Velodyne® LiDAR™ PUCK™ VLP-16 16-channel LiDAR sensors, Leica Geosystems Pegasus: Two mobile sensor platform, Garmin® LiDAR-Lite v3 measurement sensor, Quanergy M8 LiDAR sensors, Quanergy S3 solid state LiDAR sensor, LeddarTech® LeddarVU compact solid state fixed-beam LiDAR sensors, other industry-equivalent LiDAR sensors and / or systems, and may perform illuminated target and / or obstacle detection in an environment around the AFAD 201 using any known or future-developed standard and / or architecture. The RADAR sensors 324 may include one or more radio components that are configured to detect objects / targets in an environment 200 of the AFAD 201. In some embodiments, the RADAR sensors 324 may determine a distance, position, and / or movement vector (e.g., angle, speed, etc.) associated with a target over time. The RADAR sensors 324 may include a transmitter configured to generate and emit electromagnetic waves (e.g., radio, microwaves, etc.) and a receiver configured to detect returned electromagnetic waves. In some embodiments, the RADAR sensors 324 may include at least one processor configured to interpret the returned electromagnetic waves and determine locational properties of targets. Examples of the RADAR sensors 324 as described herein may include, but are not limited to, at least one of Infineon RASIC™ RTN7735PL transmitter and RRN7745PL / 46PL receiver sensors, Autoliv ASP Vehicle RADAR sensors, Delphi L2C0051TR 77GHz ESR Electronically Scanning Radar sensors, Fujitsu Ten Ltd. Automotive Compact 77GHz 3D Electronic Scan Millimeter Wave Radar sensors, other industry-equivalent RADAR sensors and / or systems, and may perform radio target and / or obstacle detection in an environment around the AFAD 201 using any known or future- developed standard and / or architecture.

[0191] The ultrasonic sensors 328 may include one or more components that are configured to detect objects / targets in an environment 200 of the AFAD 201. In some embodiments, the ultrasonic sensors 328 may determine a distance, position, and / or movement vector (e.g., angle, speed, etc.) associated with a target over time. The ultrasonic sensors 328 may include an ultrasonic transmitter and receiver, or transceiver, configured to generate and emit ultrasound waves and interpret returned echoes of those waves. In some embodiments, the ultrasonic sensors 328 may include at least one processor configured to interpret the returned ultrasonic waves and determine locational properties of targets. Examples of the ultrasonic sensors 328 as described herein may include, but are not limited to, at least one of Texas Instruments TIDA-00151 automotive ultrasonic sensor interface IC sensors, MaxBotix® MB8450 ultrasonic proximity sensor, MaxBotix® ParkSonar™-EZ ultrasonic proximity sensors, Murata Electronics MA40H1S-R open-structure ultrasonic sensors, Murata Electronics MA40S4R / S open-structure ultrasonic sensors, Murata Electronics MA58MF14-7N waterproof ultrasonic sensors, other industry-equivalent ultrasonic sensors and / or systems, and may perform ultrasonic target and / or obstacle detection in an environment around the AFAD 201 using any known or future-developed standard and / or architecture. The camera sensors 332 may include one or more components configured to detect image information associated with an environment 200 of the AFAD 201. In some embodiments, the camera sensors 332 may include a lens, filter, image sensor, and / or a digital image processer. It is an aspect of the present disclosure that multiple camera sensors 332 may be used together to generate stereo images providing depth measurements. Examples of the camera sensors 332 as described herein may include, but are not limited to, at least one of ON Semiconductor® MT9V024 Global Shutter VGA GS CMOS image sensors, Teledyne DALSA Falcon2 camera sensors, CMOSIS CMV50000 high-speed CMOS image sensors, other industry-equivalent camera sensors and / or systems, and may perform visual target and / or obstacle detection in an environment around the AFAD 201 using any known or future-developed standard and / or architecture.

[0192] The infrared (IR) sensors 336 may include one or more components configured to detect image information associated with an environment 200 of the AFAD 201. The IR sensors 336 may be configured to detect targets in low-light, dark, or poorly-lit environments. The IR sensors 336 may include an IR light emitting element (e.g., IR light emitting diode (LED), etc.) and an IR photodiode. In some embodiments, the IR photodiode may be configured to detect returned IR light at or about the same wavelength to that emitted by the IR light emitting element. In some embodiments, the IR sensors 336 may include at least one processor configured to interpret the returned IR light and determine locational properties of targets. The IR sensors 336 may be configured to detect and / or measure a temperature associated with a target (e.g., an object, pedestrian, other vehicle, etc.). Examples of IR sensors 336 as described herein may include, but are not limited to, at least one of Opto Diode lead-salt IR array sensors, Opto Diode OD-850 Near-IR LED sensors, Opto Diode SA / SHA727 steady state IR emitters and IR detectors, FLIR® LS microbolometer sensors, FLIR® TacFLIR 380-HD InSb MWIR FPA and HD MWIR thermal sensors, FLIR® VOx 640x480 pixel detector sensors, Delphi IR sensors, other industry-equivalent IR sensors and / or systems, and may perform IR visual target and / or obstacle detection in an environment around the AFAD 201 using any known or future- developed standard and / or architecture.

[0193] In some embodiments, the AFAD 201 may include one or more power sources 378. These one or more power sources 378 may be configured to provide drive or traction power, system and / or subsystem power, accessory power, etc. While described herein as a single power source 378 for sake of clarity, embodiments of the present disclosure are not so limited. For example, it should be appreciated that independent, different, or separate power sources 378 may provide power to various systems of the AFAD 201. For instance, a drive power source may be configured to provide the power for the one or more electric drive motors of the AFAD 201, while a system power source may be configured to provide the power for one or more other systems and / or subsystems of the AFAD 201. Other power sources may include an accessory power source, a backup power source, a critical system power source, and / or other separate power sources. Separating the power sources 378 in this manner may provide a number of benefits over conventional systems. For example, separating the power sources 378 may allow one power source 378 to be removed and / or replaced independently without requiring that power be removed from all systems and / or subsystems of the AFAD 201 during a power source 378 removal / replacement. For instance, one or more of the accessories, communications, safety equipment, and / or backup power systems, etc., may be maintained even when a particular power source 378 is depleted, removed, or becomes otherwise inoperable. In embodiments, the power sources 378 may be rechargeable. Additionally, the power sources 378 may be recharged using solar energy obtained from solar panels.

[0194] The power source 378 may include a charge controller that may be configured to determine charge levels of the power source 378, control a rate at which charge is drawn from the power source 378, control a rate at which charge is added to the power source 378, and / or monitor a health of the power source 378 (e.g., one or more modules, cells, portions, etc.). In some embodiments, the charge controller or the power source 378 may include a communication interface. The communication interface can allow the charge controller to report a state of the power source 378 to one or more other controllers of the AFAD 201 or even communicate with a communication device separate and / or apart from the AFAD 201. Additionally or alternatively, the communication interface may be configured to receive instructions (e.g., control instructions, charge instructions, communication instructions, etc.) from one or more other controllers or computers of the AFAD 201 or a communication device that is separate and / or apart from the AFAD 201.

[0195] A navigation system 302 can include any hardware and / or software used to navigate the vehicle either manually or autonomously. The navigation system 302 may be as described in conjunction with Fig. 3B.

[0196] In some embodiments, the sensors 304 may include other sensors 338 and / or combinations of the sensors 306-337 described above. Additionally or alternatively, one or more of the sensors 306-338 described above may include one or more processors configured to process and / or interpret signals detected by the one or more sensors 306-338. In some embodiments, the processing of at least some sensor information provided by the sensors 304 and systems may be processed by at least one sensor processor 340. Raw and / or processed sensor data may be stored in a sensor data memory 344 storage medium. In some embodiments, the sensor data memory 344 may store instructions used by the sensor processor 340 for processing sensor information provided by the sensors 304 and systems. In any event, the sensor data memory 344 may be a disk drive, optical storage device, solid- state storage device such as a random access memory ("RAM") and / or a read-only memory ("ROM"), which can be programmable, flash-updateable, and / or the like.

[0197] The control system 348 may receive processed sensor information from the sensor processor 340 and determine to control an aspect of the AFAD 201. Controlling an aspect of the AFAD 201 may include presenting information via one or more display devices 372 associated with the AFAD 201, sending commands to one or more computing devices 368 associated with the AFAD 201, and / or controlling a driving operation of the AFAD 201. In one embodiment, the control system 348 may operate a speed of the AFAD 201 by controlling an output signal to the accelerator and / or braking system of the AFAD 201. In this example, the control system 348 may receive sensor data describing an environment surrounding the AFAD 201 and, based on the sensor data received, determine to adjust the acceleration, power output, and / or braking of the AFAD 201. The control system 348 may additionally control steering and / or other driving functions of the AFAD 201.

[0198] The control system 348 may communicate, in real-time, with the sensors 304 forming a feedback loop. In particular, upon receiving sensor information describing a condition of targets in the environment 200 surrounding the AFAD 201, the control system 348 may autonomously make changes to the operation of the AFAD 201. The control system 348 may then receive subsequent sensor information describing any change to the condition of the targets detected in the environment 200. This continual cycle of observation (e.g., via the sensors, etc.) and action (e.g., selected control or non-control of operations, etc.) allows the AFAD 201 to operate autonomously in the environment 200.

[0199] In some embodiments, the one or more components of the AFAD 201 (e.g., the sensors 304, control system 348, display devices 372, etc.) may communicate across the communication network 352 to one or more entities 356A-N via a communications subsystem 350 of the AFAD 201. For instance, the navigation sensors 308 may receive global positioning, location, and / or navigational information from a navigation source 356A. In some embodiments, the navigation source 356A may be a global navigation satellite system (GNSS) similar, if not identical, to NAVSTAR GPS, GLONASS, EU Galileo, and / or the BeiDou Navigation Satellite System (BDS) to name a few.

[0200] In some embodiments, the control system 348 may receive control information from one or more control sources 356B. The control source 356B may provide control information including autonomous driving control commands, operation override control commands, and the like. The control source 356B may correspond to an autonomous control system, a traffic control system, an administrative control entity, and / or some other controlling server. It is an aspect of the present disclosure that the control system 348 and / or other components of the AFAD 201 may exchange communications with the control source 356B across the communication network 352 and via the communications subsystem 350.

[0201] Information associated with controlling operations of the AFAD 201 may be stored in a control data memory 364 storage medium. For example, the controlling operations may include pre-configured traffic controls. The control data memory 364 may store instructions used by the control system 348 for controlling operations of the AFAD 201, historical control information, autonomous driving control rules, and the like. In some embodiments, the control data memory 364 may be a disk drive, optical storage device, solid-state storage device such as a random access memory ("RAM") and / or a read-only memory ("ROM"), which can be programmable, flash-updateable, and / or the like.

[0202] In addition to the mechanical components described herein, the AFAD 201 may include a number of user interface devices. The user interface devices receive and translate human input into a mechanical movement or electrical signal or stimulus. The human input may be one or more of motion (e.g., body movement, body part movement, in two- dimensional or three-dimensional space, etc.), voice, touch, and / or physical interaction with the components of the AFAD 201. In some embodiments, the human input may be configured to control one or more functions of the AFAD 201 and / or systems of the AFAD 201 described herein. User interfaces may include, but are in no way limited to, at least one graphical user interface of a display device, steering wheel or mechanism, transmission lever or button (e.g., including park, neutral, reverse, and / or drive positions, etc.), throttle control pedal or mechanism, brake control pedal or mechanism, power control switch, communications equipment, etc.

[0203] Fig. 3B illustrates a GPS / Navigation subsystem(s) 302. The navigation subsystem(s) 302 can be any present or future-built navigation system that may use location data, for example, from the Global Positioning System (GPS), to provide navigation information or control the AFAD 201. The navigation subsystem(s) 302 can include several components, such as, one or more of, but not limited to: a GPS Antenna / receiver 331, a location module 333, a maps database 335, etc. Generally, the several components or modules 331-335 may be hardware, software, firmware, computer readable media, or combinations thereof.

[0204] A GPS Antenna / receiver 331 can be any antenna, GPS puck, and / or receiver capable of receiving signals from a GPS satellite or other navigation system. The signals may be demodulated, converted, interpreted, etc. by the GPS Antenna / receiver 331 and provided to the location module 333. Thus, the GPS Antenna / receiver 331 may convert the time signals from the GPS system and provide a location (e.g., coordinates on a map) to the location module 333. Alternatively, the location module 333 can interpret the time signals into coordinates or other location information.

[0205] The location module 333 can be the controller of the satellite navigation system designed for use in the AFAD 201. The location module 333 can acquire position data, as from the GPS Antenna / receiver 331, to locate the AFAD 201 on a road in the unit's maps database 335. Using the maps database 335, the location module 333 can give directions to other locations along roads also in the maps database 335. When a GPS signal is not available, the location module 333 may apply dead reckoning to estimate distance data from sensors 304 including one or more of, but not limited to, a speed sensor attached to the drive train of the AFAD 201, a gyroscope, an accelerometer, etc. Additionally or alternatively, the location module 333 may use known locations of Wi-Fi hotspots, cell tower data, etc. to determine the position of the AFAD 201, such as by using time difference of arrival (TDOA) and / or frequency difference of arrival (FDOA) techniques.

[0206] The maps database 335 can include any hardware and / or software to store information about maps, geographical information system (GIS) information, location information, etc. The maps database 335 can include any data definition or other structure to store the information. Generally, the maps database 335 can include a road database that may include one or more vector maps of areas of interest. Street names, street numbers, house numbers, and other information can be encoded as geographic coordinates so that the user can find some desired destination by street address. Points of interest (waypoints) can also be stored with their geographic coordinates. The maps database 335 may also include road or street characteristics, for example, speed limits, location of stop lights / stop signs, lane divisions, school locations, etc. The map database contents can be produced or updated by a server connected through a wireless system in communication with the Internet, even as the AFAD 201 is driven along existing streets, yielding an up-to-date map. The control system 348, can control the AFAD 201 based on sensor information from the sensors 304., in response to the sensed object information, and navigation information from the maps database 335.

[0207] The sensed object information refers to sensed information regarding objects external to the AFAD 201. Examples include animate objects such as pedestrians and attributes thereof (e.g., identity, age, sex, current spatial location, current activity, etc.), and the like and inanimate objects and attributes thereof such as other vehicles (e.g., current vehicle state or activity (parked or in motion or level of automation currently employed), occupant or operator identity, vehicle type (truck, car, etc.), vehicle spatial location, etc.), curbs (topography and spatial location), potholes (size and spatial location), lane division markers (type or color and spatial locations), signage (type or color and spatial locations such as speed limit signs, yield signs, stop signs, and other restrictive or warning signs), traffic signals (e.g., red, yellow, blue, green, etc.), buildings (spatial locations), walls (height and spatial locations), barricades (height and spatial location), and the like.

[0208] In a typical implementation, the automated control system 348, based on feedback from certain sensors, specifically the LiDAR and radar sensors positioned around the circumference of the AFAD 201, constructs a three-dimensional map in spatial proximity to the AFAD 201 that enables the automated control system 348 to identify and spatially locate animate and inanimate objects. Other sensors, such as inertial measurement units, gyroscopes, wheel encoders, sonar sensors, motion sensors to perform odometry calculations with respect to nearby moving exterior objects, and exterior facing cameras (e.g., to perform computer vision processing) can provide further contextual information for generation of a more accurate three-dimensional map. The navigation information is combined with the three-dimensional map to provide short, intermediate and long range course tracking and route selection. The control system 348 processes real-world information as well as GPS data, and vehicle speed to determine accurately the precise position of each vehicle, down to a few centimeters all while making corrections for nearby animate and inanimate objects.

[0209] The control system 348 can process in substantially real-time the aggregate mapping information and models (or predicts) behavior of other nearby animate or inanimate objects and, based on the aggregate mapping information and modeled behavior, issues appropriate commands regarding operation. While some commands are hard-coded into the AFAD 201, other responses are learned and recorded by profile updates based on previous situations. Fig. 4 illustrates a block diagram of a computing environment 400 that may function as the servers, user computers, or other systems provided and described herein. The computing environment 400 includes one or more user computers, or computing devices, such as a robotic flagger device 404, a communication device 408, and / or additional devices 412. The computing devices 404, 408, 412 may include general purpose personal computers (including, merely by way of example, personal computers, and / or laptop computers running various versions of Microsoft Corp.'s Windows® and / or Apple Corp.'s Macintosh® operating systems) and / or workstation computers running any of a variety of commercially- available UNIX® or UNIX-like operating systems. These computing devices 404, 408, 412 may also have any of a variety of applications, including for example, database client and / or server applications, and web browser applications. Alternatively, the computing devices 404, 408, 412 may be any other electronic device, such as a thin-client computer, Internet- enabled mobile telephone, and / or personal digital assistant, capable of communicating via a communication network 352 and / or displaying and navigating web pages or other types of electronic documents or information. Although the exemplary computing environment 400 is shown with two computing devices, any number of user computers or computing devices may be supported.

[0210] The computing environment 400 may also include one or more servers 414, 416. In this example, server 414 is shown as a web server and server 416 is shown as an application server. The web server 414, which may be used to process requests for web pages or other electronic documents from computing devices 404, 408, 412. The web server 414 can be running an operating system including any of those discussed above, as well as any commercially available server operating systems. The web server 414 can also run a variety of server applications, including SIP (Session Initiation Protocol) servers, HTTP(s) servers, FTP servers, CGI servers, database servers, Java® servers, and the like. In some instances, the web server 414 may publish operations available operations as one or more web services.

[0211] The computing environment 400 may also include one or more file and or / application servers 416, which can, in addition to an operating system, include one or more applications accessible by a client running on one or more of the computing devices 404, 408, 412. The server(s) 416 and / or 414 may be one or more general purpose computers capable of executing programs or scripts in response to the computing devices 404, 408, 412. As one example, the server 416, 414 may execute one or more web applications. The web application may be implemented as one or more scripts or programs written in any programming language, such as Java®, C, C#®, or C++, and / or any scripting language, such as Perl, Python, or TCL, as well as combinations of any programming / scripting languages. The application server(s) 416 may also include database servers, including without limitation those commercially available from Oracle®, Microsoft®, Sybase®, IBM® and the like, which can process requests from database clients running on a computing device 404, 408, 412.

[0212] The computing environment 400 may also include a database 418. The database 418 may reside in a variety of locations. By way of example, database 418 may reside on a storage medium local to (and / or resident in) one or more of the computers 404, 408, 412, 414, 416. Alternatively, it may be remote from any or all of the computers 404, 408, 412, 414, 416, and in communication (e.g., via the network 352) with one or more of these. The database 418 may reside in a storage-area network ("SAN") familiar to those skilled in the art. Similarly, any necessary files for performing the functions attributed to the computers 404, 408, 412, 414, 416 may be stored locally on the respective computer and / or remotely, as appropriate. The database 418 may be a relational database, such as Oracle 20i®, that is adapted to store, update, and retrieve data in response to SQL-formatted commands.

[0213] Fig. 5 illustrates one embodiment of a computer system 500 upon which the servers, user computers, computing devices, or other systems or components described above may be deployed or executed. The computer system 500 is shown comprising hardware elements that may be electrically coupled via a bus 504. The hardware elements may include one or more central processing units (CPUs) 508; one or more input devices 512 (e.g., a mouse, a keyboard, etc.); and one or more output devices 516 (e.g., a display device, a printer, etc.). The computer system 500 may also include one or more storage devices 520. By way of example, storage device(s) 520 may be disk drives, optical storage devices, solid-state storage devices such as a random access memory ("RAM") and / or a read-only memory ("ROM"), which can be programmable, flash-updateable and / or the like.

[0214] The computer system 500 may additionally include a computer-readable storage media reader 524; a communications system 528 (e.g., a modem, a network card (wireless or wired), an infra-red communication device, etc.); and working memory 536, which may include RAM and ROM devices as described above. The computer system 500 may also include a processing unit 532, which can include a DSP, a special-purpose processor, and / or the like.

[0215] The computer-readable storage media reader 524 can further be connected to a computer-readable storage medium, together (and, optionally, in combination with storage device(s) 520) comprehensively representing remote, local, fixed, and / or removable storage devices plus storage media for temporarily and / or more permanently containing computer- readable information. The communications system 528 may permit data to be exchanged with a network and / or any other computer described above with respect to the computer environments described herein. Moreover, as disclosed herein, the term "storage medium" may represent one or more devices for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and / or other machine readable mediums for storing information.

[0216] The computer system 500 may also comprise software elements, shown as being currently located within a working memory 536, including an operating system 540 and / or other code 544. It should be appreciated that alternate embodiments of a computer system 500 may have numerous variations from that described above. For example, customized hardware might also be used and / or particular elements might be implemented in hardware, software (including portable software, such as applets), or both. Further, connection to other computing devices such as network input / output devices may be employed.

[0217] Examples of the processors (e.g., processor 508) as described herein may include, but are not limited to, at least one of Qualcomm® Snapdragon® 800 and 801, Qualcomm® Snapdragon® 620 and 615 with 4G LTE Integration and 64-bit computing, Apple® A7 processor with 64-bit architecture, Apple® M7 motion coprocessors, Samsung® Exynos® series, the Intel® Core™ family of processors, the Intel® Xeon® family of processors, the Intel® Atom™ family of processors, the Intel Itanium® family of processors, Intel® Core® i5-4670K and i7-4770K 22nm Haswell, Intel® Core® i5-3570K 22nm Ivy Bridge, the AMD® FX™ family of processors, AMD® FX-4300, FX-6300, and FX-8350 32nm Vishera, AMD® Kaveri processors, Texas Instruments® Jacinto C6000™ automotive infotainment processors, Texas Instruments® OMAP™ automotive-grade mobile processors, ARM® Cortex™-M processors, ARM® Cortex-A and ARM926EJ-S™ processors, other industry-equivalent processors, and may perform computational functions using any known or future-developed standard, instruction set, libraries, and / or architecture.

[0218] Fig. 6 is a flow diagram illustrating example operations of the system(s) in Figs. 1- 5. In more detail, the operations of Fig. 6 are carried out for autonomous operation of a robotic flagger (e.g., robotic flagger 201) for traffic control and management in a control zone.

[0219] While a general order for the steps of the method 600 is shown in Fig. 6, the method 600 can include more or fewer steps or can arrange the order of the steps differently than those shown in Fig. 6. Generally, the method 600 starts at operation 605 and ends at operation 625. The method 600 can be executed as a set of computer-executable instructions executed by the control system 348 (that includes one or more processors 508) and encoded or stored on a computer readable medium (e.g., control data storage 364, storage device 520, etc.). Alternatively, the operations discussed with respect to Fig. 6 may be implemented by the various elements of the system(s) Figs. 1-5. Hereinafter, the method 600 shall be explained with reference to the systems, components, assemblies, devices, user interfaces, environments, software, etc. described in conjunction with Figs. 1-5.

[0220] In operation 610, the method 600 includes collecting environment data. For example, the sensors 304 may collect data about the control zone / environment 200. In operation 615, the method 600 includes processing the environment data to autonomously control a robotic flagger (e.g., the robotic flagger 201). For example, the control system 348 autonomously navigates the robotic traffic flagger device based on a workflow within the control zone 200. For example, the robotic flagger 201 is controlled to move with the workers as they pave a portion of a road.

[0221] In operation 620, the method includes processing the environment data to control traffic. For example, the control system 348 controls the signaling device(s) 202 to control the vehicle traffic within the control zone 200. The control system 348 may dynamically adjust signal timings or flagger positions to optimize traffic flow through the control zone 200, considering factors such as queue lengths, vehicle speeds, and time-of-day. The control system 348 may dynamically adjust signal timings or flagger positions based on environmental conditions detected by the at least one sensor, such as weather changes, lighting conditions, and road surface quality, to maintain effective traffic control in the traffic control zone.

[0222] Fig. 7 is a block diagram illustrating power management of the system(s) in Figs. 1- 5.

[0223] In view of the foregoing description, it should be appreciated that one or more example embodiments provide methods and devices for automated traffic control.

[0224] Embodiments of the present disclosure include a robotic traffic flagger device with autonomous operation, including at least one motorized mobile platform; at least one sensor that collects environment data; and a navigation module that controls the at least one motorized mobile platform to autonomously navigate the robotic traffic flagger device based on the environment data. Aspects of the present disclosure include wherein the navigation module autonomously navigates the robotic traffic flagger device based on a workflow within a traffic control zone.

[0225] Aspects of the present disclosure include an environment monitoring module that generates instructions for the robotic traffic flagger device to control vehicle traffic within a traffic control zone based on the environment data; and at least one signaling device to control the vehicle traffic within the traffic control zone.

[0226] Aspects of the present disclosure include the environment monitoring module dynamically adjusts signal timings or flagger positions to optimize traffic flow through the traffic control zone, considering factors such as queue lengths, vehicle speeds, and time-of- day.

[0227] Aspects of the present disclosure include the environment monitoring module dynamically adjusts signal timings or flagger positions based on environmental conditions detected by the at least one sensor, such as weather changes, lighting conditions, and road surface quality, to maintain effective traffic control in the traffic control zone.

[0228] Aspects of the present disclosure include the environment monitoring module detects an unsafe condition; and in response to detecting the unsafe condition, the robotic flagger traffic device activates an alarm.

[0229] Aspects of the present disclosure include wherein the at least one sensor includes at least one of: a camera, a radar, a Light Detection and Ranging (LiDAR) sensor, and ultrasonic sensor.

[0230] Aspects of the present disclosure include wherein the at least one motorized mobile platform includes an electric drivetrain.

[0231] Aspects of the present disclosure include a rechargeable battery system that at least partially powers the robotic traffic flagger device.

[0232] Aspects of the present disclosure include wherein the rechargeable battery system is at least partially rechargeable using one or more solar panels.

[0233] Aspects of the present disclosure include a cellular transceiver that allows the robotic traffic flagger device to be controlled from any distance.

[0234] Aspects of the present disclosure include a communication interface that allows the robotic traffic flagger device to wirelessly exchange information with connected equipment.

[0235] Any one or more of the aspects / embodiments as substantially disclosed herein. Any one or more of the aspects / embodiments as substantially disclosed herein optionally in combination with any one or more other aspects / embodiments as substantially disclosed herein.

[0236] One or more means adapted to perform any one or more of the above aspects / embodiments as substantially disclosed herein.

[0237] The term “automatic” and variations thereof, as used herein, refers to any process or operation, which is typically continuous or semi-continuous, done without material human input when the process or operation is performed. However, a process or operation can be automatic, even though performance of the process or operation uses material or immaterial human input, if the input is received before performance of the process or operation. Human input is deemed to be material if such input influences how the process or operation will be performed. Human input that consents to the performance of the process or operation is not deemed to be “material.”

[0238] Aspects of the present disclosure may take the form of an embodiment that is entirely hardware, an embodiment that is entirely software (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module,” or “system.” Any combination of one or more computer-readable medium(s) may be utilized. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.

[0239] A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0240] A computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer- readable signal medium may be any computer-readable medium that is not a computer- readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0241] The terms “determine,” “calculate,” “compute,” and variations thereof, as used herein, are used interchangeably and include any type of methodology, process, mathematical operation or technique.

[0242] While various embodiments of the present disclosure have been described in detail, it is apparent that modifications and alterations of those embodiments will occur to those skilled in the art. However, it is to be understood that such modifications and alterations are within the scope and spirit of the present disclosure, as set forth in the following claims. Further, the invention(s) described herein is capable of other embodiments and of being practiced or of being carried out in various ways. It is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

Claims

CLAIMSWhat is claimed is:

1. A robotic traffic flagger device with autonomous operation, comprising: at least one motorized mobile platform; at least one sensor structured to collect environment data; and a navigation module operable to control the at least one motorized mobile platform to autonomously navigate the robotic traffic flagger device based on the environment data.

2. The robotic traffic flagger device according to claim 1, wherein the navigation module is operable to autonomously navigate the robotic traffic flagger device based on a workflow within a traffic control zone.

3. The robotic traffic flagger device according to claim 1, wherein the navigation module is operable to autonomously recognize and classify traffic patterns and trigger predefined traffic behaviors and signal timings based on the recognized traffic patterns.

4. The robotic traffic flagger device according to claim 1, further comprising: an environment monitoring module operable to generate instructions for the robotic traffic flagger device to control vehicle traffic within a traffic control zone based on the environment data; and at least one signaling device operable to control the vehicle traffic within the traffic control zone.

5. The robotic traffic flagger device according to claim 4, wherein the environment monitoring module is operable to dynamically adjust signal timings or robotic traffic flagger positions to control traffic flow through the traffic control zone.

6. The robotic traffic flagger device according to claim 5, wherein the environment monitoring module is operable to dynamically adjust signal timings or robotic traffic flagger positions to optimize traffic flow based on one or more of the following factors: queue lengths, vehicle speeds, and time-of-day.

7. The robotic traffic flagger device according to claim 5, wherein the environment monitoring module is operable to dynamically adjust signal timings or robotic traffic flagger positions by comparing ascertained speeds of approaching vehicles to pre-defined thresholds; and wherein the environment monitoring module is operable to dynamically activate warning signals or adjust signal timing or robotic traffic flagger positions to enforce predetermined vehicle speeds.

8. The robotic traffic flagger device according to claim 5, wherein the navigation module is operable to autonomously navigate the robotic traffic flagger device based on a workflow within the traffic control zone that is a construction zone, and wherein the at least one sensor is operable to ascertain real-time positions of workers within the traffic control zone.

9. The robotic traffic flagger device according to claim 8, wherein the environment monitoring module is operable to dynamically adjust robotic traffic flagger positions based on ascertained real-time positions of workers within the traffic control zone such that the at least one motorized mobile platform stays within a threshold distance of the workers or the workflow.

10. The robotic traffic flagger device according to claim 4, wherein the environment monitoring module is operable to dynamically adjust signal timings or flagger positions based on environmental conditions detected by the at least one sensor to maintain an effective traffic control in the traffic control zone, wherein the environmental conditions detected by the at least one sensor comprise weather changes, lighting conditions, and road surface quality.

11. The robotic traffic flagger device according to claim 4, wherein the environment monitoring module is operable to detect an unsafe condition; and wherein the robotic traffic flagger device is operable to activate an alarm in response to detecting the unsafe condition.

12. The robotic traffic flagger device according to claim 1, wherein the at least one sensor includes at least one of: a camera, a radar, a Light Detection and Ranging (LiDAR) sensor, RADAR sensors, and ultrasonic sensor.

13. The robotic traffic flagger device according to claim 12, wherein the at least one sensor comprises at least two rear-view cameras operable to process acquired rear-view images to generate a stereo view comprising depth data for targets visible by all of the at least two rear-view cameras.

14. The robotic traffic flagger device according to claim 1, further comprising: a cellular transceiver operable to control the robotic traffic flagger device from any distance.

15. A flagger device, comprising: a signaling device configured to produce a signal to control traffic; a mobile platform configured to move the flagger device; a plurality of sensors configured to detect environmental data;a control system configured to construct an environmental model based on the environmental data, wherein the environmental model comprises at least one of a workflow, a worker location, a traffic pattern, and an environment state; wherein the control system is configured to cause the signaling device to change the signal from a first state to a second state based on the environmental model; and wherein the control system is configured to cause the mobile platform to move the flagger device from a first position to a second position based on the environmental model.

16. The flagger device of claim 15, wherein the traffic pattern comprises one of a queue length, an average speed of a plurality of vehicles, and a time-of-day.

17. The flagger device of claim 15, wherein the control system is configured to cause the mobile platform to move the flagger device within a predetermined distance of the worker location or the workflow.

18. The flagger device of claim 15, wherein a control zone extends from the flagger device in a lateral direction, and the plurality of sensors combine to detect the environmental data in the control zone.

19. The flagger device of claim 15, wherein a detection zone of one sensor of the plurality of sensors and a detection zone of another sensor of the plurality of sensors overlap.

20. A method of operating a robotic traffic flagger device, the method comprising: providing a robotic traffic flagger device with autonomous operation, the robotic traffic flagger device comprising: at least one motorized mobile platform; at least one sensor structured to collect environment data; and a navigation module operable to control the at least one motorized mobile platform to autonomously navigate the robotic traffic flagger device based on the environment data; using the at least one sensor to collect environment data; processing the collected environment data to autonomously navigate the robotic traffic flagger device based on a workflow within a control zone; and processing the collected environment data to control traffic within the control zone comprising dynamically adjusting signal timings or flagger positions to optimize traffic flow through the control zone considering at least one of the following factors: queue lengths, vehicle speeds, and time-of-day.

Images