Vehicle initiated rhythmic operator interaction

Abstract

A vehicle-initiated cadenced operator interaction system introduces an operational concept to a vehicle operator via machine-initiated interactions. Thereafter, the interactions are initiated by the industrial vehicle at a cadence that provides a gap between interactions so that the operator can demonstrate behaviors associated with the introduced concept. The vehicle controller actively analyzes industrial vehicle data associated with the content of the interaction(s) and evaluates the data against predefined operational criteria to determine whether the operator demonstrated the appropriate skills / behaviors associated with the interaction(s). In response to the operator demonstrated capabilities, the system is able to modify the operation of the vehicle to tune the industrial vehicle to the operator. The system is also able to extend to the operational environment by interacting with electronic devices, vehicles, machines, etc. in the operational environment to tune the environment to the operator.

Description

Technical Field

[0001] Businesses are deploying wireless strategies to improve operational efficiency and accuracy. They can also be deployed to mitigate the potential impact of increasing labor and logistics costs. Background Art

[0002] For example, in a typical warehouse implementation, a forklift is equipped with a communication device that links the corresponding forklift operator to a management system executed on an associated computer system via a wireless transceiver. Essentially, the communication device is used as an interface to the management system to direct the forklift operator's tasks, for example, by instructing the forklift operator where and / or how to pick, pack, store, move, place, handle, or otherwise manipulate items within the facility. Summary of the Invention

[0003] According to an embodiment of the present disclosure, a computer-implemented process for vehicle-initiated rhythmic operator interaction is provided. The process is performed in an environment including an industrial vehicle having a user interface communicatively coupled to a vehicle controller. Specifically, the process includes loading an interaction profile into the vehicle controller. The interaction profile includes a pattern including a start action and an end action, wherein the pattern is associated with an operator action that can be performed when operating the industrial vehicle. The interaction profile also includes rules defining a measure of performance associated with the pattern and a target response to the measure of performance. The process also includes loading a rhythm into the vehicle controller, wherein the rhythm defines the interval between interactions associated with the operator action of the interaction profile. Furthermore, the process includes displaying information related to the operator action of the interaction profile via the user interface of the industrial vehicle and performing continuous monitoring of the use of the industrial vehicle.

[0004] Continuous monitoring includes monitoring information electronically collected by the industrial vehicle. The continuous monitoring also includes executing an action set in response to detecting first data within the monitored information corresponding to a start action and second data within the monitored information corresponding to an end action. In this regard, the action set includes applying selected components of the monitored information against rules to define a performance response, and generating performance feedback based on a comparison of the performance response to a target response. The action set also includes saving the performance feedback in a profile history. Furthermore, the action set includes detecting whether an observation event is due based on a cadence, and modifying at least one operating characteristic of the industrial vehicle based on the profile history at a time established by the cadence.

[0005] Loading the interaction profile into the vehicle controller may include defining operator actions as operations using a remote control feature of the industrial vehicle, defining start and end actions for a pattern based on operator interactions with the remote control feature of the industrial vehicle, and defining rules as at least one of a travel distance limit or operator presence on the industrial vehicle while using the remote control feature.

[0006] Loading the interaction profile into the vehicle controller may further include defining operator actions as operations to drive the industrial vehicle, defining start and end actions of a pattern to drive the industrial vehicle based on an operator interaction with at least one control of the industrial vehicle, and defining rules as a function of at least one of travel speed, travel distance, acceleration, or braking.

[0007] Loading the interaction profile into the vehicle controller may further include defining operator actions as operations using a load handling feature of the industrial vehicle, defining start and end actions for a pattern to operate the load handling feature of the industrial vehicle based on an operator interaction with at least one control of the industrial vehicle, and defining rules as a function of at least one of lift height or lift weight.

[0008] Loading the interaction profile into the vehicle controller may further include defining operator actions as operations using a hybrid feature of the industrial vehicle, defining start and end actions of a pattern to operate the hybrid feature of the industrial vehicle based on an operator interaction with at least one control of the industrial vehicle, and defining rules as a function of the hybrid operation feature.

[0009] Loading the interaction profile into the vehicle controller may also include defining operator actions as operations on the industrial vehicle dictated by the environmental regulations, defining start and end actions of a pattern based on an operator interaction with at least one control of the industrial vehicle to operate the industrial vehicle according to the environmental regulations, and defining rules as a function of the environmental regulations.

[0010] Loading an operator-specific cadence into a vehicle controller, wherein the cadence defines intervals between interactions associated with operator actions of an interaction profile, may include establishing the intervals of the cadence based on time, establishing the intervals of the cadence based on a predetermined number of encounters of instances of an event defined by a pattern, or establishing the intervals of the cadence based on a predetermined number of encounters of instances of an event defined by a pattern and a predetermined amount of time.

[0011] Displaying information related to the operator actions of the interaction profile via the user interface of the industrial vehicle may include displaying training information that teaches the operator how to properly perform the operator actions so as to define correct responses and incorrect responses. Furthermore, the process may also include testing the operator's knowledge of the training information by displaying questions that the operator is required to answer, wherein the operator's answers determine whether the operator knows the correct responses.

[0012] If an observation event is due, the process may further include outputting to the display a training message reinforcing the training information, a positive performance feedback message indicating that the performance response was satisfactory in view of the target response, or a combination thereof.

[0013] The process may also include, regardless of whether the observation event is due, outputting a negative performance feedback message to the display indicating that the performance response is unsatisfactory in view of the target response.

[0014] Furthermore, the process may include increasing the tempo if performance feedback in the profile history indicates a predetermined number of consecutive correct behaviors, and decreasing the tempo if performance feedback in the profile history indicates incorrect behaviors.

[0015] According to another embodiment, a computer-implemented process for vehicle-initiated rhythmic operator interaction is provided. The process is performed in an environment including an industrial vehicle having a user interface communicatively coupled to a vehicle controller. Specifically, the process includes loading an interaction profile into the vehicle controller. The interaction profile is a pattern comprising a start action and an end action, wherein the pattern is associated with an operator action that can be performed while operating the industrial vehicle. The interaction profile also includes rules defining a performance measure associated with the pattern and a target response to the performance measure. The process also includes loading a rhythm into the vehicle controller, wherein the rhythm defines the interval between interactions associated with the operator actions of the interaction profile.

[0016] Furthermore, the process includes performing continuous monitoring by monitoring information transmitted across a vehicle network bus, identifying first data from the monitored information as satisfying a start action, identifying second data from the monitored information as satisfying an end action, and applying the monitored data across the vehicle network bus against a rule between the start action and the end action to define a performance calculation. The continuous monitoring also includes comparing the performance calculation to a target response and generating performance feedback based on the comparison. The continuous monitoring also includes detecting whether an observation event is due based on the cadence, and outputting the performance feedback if the observation event is due, otherwise suppressing the performance feedback.

[0017] The process may also include presenting instructions regarding operator actions requiring operator feedback prior to monitoring.

[0018] The process may also include modifying at least one operating parameter of the industrial vehicle to tune the performance of the industrial vehicle to the skills of the operator.

[0019] Detecting whether an observation event is due based on a cadence may include adjusting the cadence based on a desired spacing including a desired time spacing, a desired event spacing, or a combination thereof.

[0020] The process may also include setting a minimum and maximum value for the spacing, increasing the desired spacing toward the maximum value if the performance feedback results in detection of an occurrence of a correct response, and decreasing the desired spacing toward the minimum value if the performance feedback results in an incorrect response.

[0021] In some embodiments, determining the cadence may include detecting, by a processor on the industrial vehicle, an identity of the operator and determining the cadence of the operator based on the interaction profile.

[0022] According to yet another embodiment, a computer-implemented process for vehicle-initiated rhythmic operator interaction is provided. The process is performed in an environment including an industrial vehicle having a user interface communicatively coupled to a vehicle controller. Specifically, the process includes loading an interaction profile into the vehicle controller. The interaction profile includes a pattern comprising a start action and an end action, wherein the pattern is associated with an operational feature that can be implemented while operating the industrial vehicle. The interaction profile also includes rules defining a measure of performance associated with the pattern and a target response to the measure of performance. The process also includes performing continuous monitoring.

[0023] Continuous monitoring is performed by recording, by the controller, first data in a first memory indicating that information transmitted across the vehicle bus satisfies a start action, and recording, by the controller, second data in a second memory indicating that information transmitted across the vehicle bus satisfies an end action. The continuous monitoring also includes evaluating a rule to define a performance calculation by monitoring data transmitted across the vehicle bus between the start action and the end action. The continuous monitoring also includes outputting performance feedback to an output device on the industrial vehicle when a cadence-based observation event expires, wherein the performance feedback is generated based on a comparison of the performance calculation with a target response. Furthermore, the continuous monitoring includes suppressing the performance feedback when the cadence-based observation event has not expired. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a block diagram of an industrial system according to aspects of the present disclosure;

[0025] Figure 2 is a block diagram of a system of electronic devices on an industrial vehicle such as a forklift, a walkie-talkie, etc. according to aspects of the present disclosure;

[0026] Figure 3 is a block diagram of a system for performing spaced-apart interactions;

[0027] Figure 4 is a flow chart illustrating a general framework for a vehicle-initiated rhythmic operator interaction system according to aspects herein;

[0028] Figure 5 is a diagram illustrating various aspects of the present disclosure for establishing Figure 4 A flowchart of an example approach to a framework of useful rhythms;

[0029] Figure 6 is a diagram illustrating various aspects of the present disclosure for establishing Figure 4 Another example of a useful rhythmic framework is a flowchart of the method;

[0030] Figure 7 is a flow chart illustrating an embodiment of a general framework for a vehicle-initiated rhythmic operator interaction system from a vehicle perspective in accordance with aspects herein;

[0031] Figure 8 is a flow chart illustrating an embodiment of a general framework for a vehicle-initiated rhythmic operator interaction system from a user interface perspective in accordance with aspects herein; and

[0032] Figure 9 is a block diagram of a computer system with a computer-readable storage medium for implementing functionality according to various aspects of the present disclosure as described in more detail herein. DETAILED DESCRIPTION

[0033] Industrial vehicle operators are typically expected to operate industrial vehicles under a variety of conditions and in a variety of environments. Furthermore, industrial vehicles often include many different technical features that operators must be able to understand and navigate expertly. Example technical features of industrial vehicles include, but are not limited to, a traction system that controls the vehicle's travel speed, a steering system that controls the vehicle's direction of travel, a load handling system that controls the vehicle's load handling characteristics, a communication system that controls interaction with a wireless environment, an instrument cluster and / or display that can present operational information, vehicle information, mission information, or a combination thereof, and the like.

[0034] Each technical feature of an industrial vehicle requires significant training and / or skill for the operator to use it correctly and efficiently. Furthermore, in some cases, technical features can be combined to enhance operation by operating two or more technical features together (e.g., simultaneously, in series, sequentially, etc.). This translates into a significant amount of time-consuming work, which can include self-study, trial and error, peer observation and interaction, classroom and other instructional activities, and so on. In this regard, the correct or incorrect use of technical features often has significant benefits or consequences for the use of the industrial vehicle.

[0035] Modern industrial vehicles increasingly have limited operating capabilities, for example, in terms of lift height, maximum load weight, travel speed, etc. While such improvements are generally beneficial, improper operation of industrial vehicles can have both mechanical consequences (e.g., increased mechanical wear, which can lead to more frequent scheduled maintenance, etc.) and electrical consequences (e.g., premature battery wear, inefficient energy consumption, the need for frequent or increased battery charging, poor battery health, etc.).

[0036] However, aspects of the present disclosure provide unique technical features that can result in extended life, increased durability, and improved industrial vehicle efficiency (e.g., improved energy savings, reduced maintenance time, improved ratio of energy consumed to work performed, etc.) for industrial vehicles and / or vehicle components by providing rhythmic operator interaction for vehicle startup.

[0037] In an example discussed more fully herein, vehicle-initiated rhythmic operator interactions are generated by an industrial vehicle to enable personalized and spaced-out operator learning, coaching, teaching, mentoring, observation, feedback, other information exchange, or a combination thereof. Vehicle-initiated rhythmic operator interactions include interactions that occur independently of normal industrial vehicle operations, as well as interactions that occur in real-time with normal vehicle operations. Thus, in an example application, an industrial vehicle can teach an operator how to best use the industrial vehicle.

[0038] The vehicle-initiated rhythmic operator interaction system then evaluates the industrial vehicle data to determine whether the operator has adequately demonstrated the taught skills. The operator interactions generated by the vehicle-initiated rhythmic operator interaction, and the corresponding evaluated industrial vehicle data, result in dynamic modifications to the industrial vehicle itself, dynamically "tuning" the industrial vehicle's capabilities, limitations, features, etc., to the operator. Furthermore, the operator interactions generated by the vehicle-initiated rhythmic operator interaction can dynamically change electronic devices, other vehicles, machinery, etc., near the industrial vehicle to dynamically "tune" the work environment to the operator. Thus, the vehicle-initiated rhythmic operator interaction system can modify the vehicle itself, the operating environment, or both to tune to the operator, modify operator behavior to achieve improved industrial vehicle operation, combinations thereof, and the like.

[0039] Brief Introduction

[0040] The vehicle-initiated rhythmic operator interaction system disclosed herein initially presents skills, teaches features, or otherwise introduces operational concepts to the vehicle operator. In many cases, this interaction occurs via the operator interacting with a graphical user interface on the industrial vehicle. However, the interaction can also be performed on a remote device (e.g., a tablet, smartphone, etc.). The initial interaction can occur at a time such as when the vehicle is stopped so that the user can focus on the interaction.

[0041] Thereafter, the industrial vehicle initiates interactions according to the cadence. The cadence provides intervals (e.g., time gaps) between interactions so that the operator can demonstrate skills, behaviors, etc. associated with the previous interaction(s). The intervals can vary, or they can be fixed. For example, the cadence can be based on time, events, or a combination thereof. The vehicle controller can also provide information to the operator in real time independent of the cadence, for example, providing real-time feedback to the operator regarding the vehicle-initiated rhythmic operator interaction system's training of the operator, such as providing rapid positive reinforcement, negative reinforcement, etc.

[0042] In some embodiments, the spacing of the cadence can be preset. Alternatively, the spacing of the cadence can be dynamically determined by the system to allow interactions to be timed appropriately for the operator. In this regard, the computer system can converge on a cadence unique to each specific vehicle operator to provide not only contextually appropriate interactions, but also appropriately timed / spaced interactions, which can include the introduction of new concepts, relearning, positive reinforcement of learned behaviors, negative reinforcement of incorrect behaviors, and the like.

[0043] The intelligence of industrial vehicle systems allows the vehicle controller to establish the content and cadence of interactions independent of the current environment. For example, the vehicle controller can interact with the operator at rhythmic intervals on topics for which the operator has not yet been trained to reinforce previously trained capabilities, react to electronically derived observations of the operator's responses to events, provide instructions in response to events that are expected to occur in the near term, identify vehicle misuse, non-ideal use, or unintended use that is impacting vehicle performance, etc.

[0044] The vehicle controller then actively monitors and analyzes the machine-generated industrial vehicle data to parse the monitored data into industrial vehicle activities associated with the interaction content(s). The monitored activities are evaluated against predefined operating criteria (e.g., desired or otherwise optimal performance) to determine whether the operator exhibits appropriate skills / behaviors associated with the interaction(s).

[0045] Furthermore, in response to the operator's demonstrated competence in operating the industrial vehicle, the vehicle-initiated rhythmic operator interaction system can perform machine control of the vehicle, modify the vehicle's operation, implement performance tuning, combinations thereof, and the like, examples of which are described in greater detail herein. Thus, the operator's demonstrated competence can drive tuning of the industrial vehicle for better performance, more capability, lower vehicle performance, less vehicle capability, and the like. The vehicle-initiated rhythmic operator interaction system can also extend beyond the operational environment of the industrial vehicle by interacting with electronic devices, vehicles, machines, and the like within the industrial vehicle's operational environment (e.g., near the industrial vehicle's workstation) to send messages, perform controls, modify operations, combinations thereof, and the like, examples of which are described in greater detail herein.

[0046] Thus, the vehicle-initiated rhythmic interaction drives the operator's knowledge, skills, education, and familiarity with the industrial vehicle and / or the industrial vehicle's operating environment, while controlling the industrial vehicle, the environment, or both in a manner consistent with the operator's skill in operating the industrial vehicle. Thus, the vehicle-initiated rhythmic operator interaction system can tune the industrial vehicle and / or the operating environment to the operator, and / or can also tune the operator to the industrial vehicle, the operating environment, or a combination thereof.

[0047] In some embodiments, the system can determine individual needs for certain topics to be taught in a "live" feedback situation, determine individual time and / or event based spacing between questions, training, observation and reinforcement, incorporate learning algorithms on industrial vehicles, combinations thereof, and the like.

[0048] In this way, the operator has the possibility of learning at any time, as the learning platform on the industrial vehicle is always with the operator. This also means that the industrial vehicle guides the operator in a continuous manner, allowing for automatic observation, reinforcement, intervention, and a combination thereof to improve the operation of the industrial vehicle. Accordingly, a trainer can only teach one operator at a time. A secondary result is an improvement in the operator's knowledge, skills, and familiarity with the industrial vehicle and / or the industrial vehicle's operating environment. Moreover, the industrial vehicle, the operating environment, and a combination thereof are controlled commensurate with the skills demonstrated by the operator. Other technical effects, technical problems, and corresponding technical solutions are described in more detail herein.

[0049] In this regard, while operator training, operator behavior modification, and operator compliance with environmental rules and regulations are achievable, these benefits are the inevitable result of technology solutions that result in improved industrial vehicle performance, which can be measured in terms of mechanical / physical benefits (e.g., improved use of remote control, hybrid and / or other features on or provided with the industrial vehicle, reduced vehicle wear and maintenance, etc.) and electrical benefits (e.g., increased energy savings, improved battery health, etc.).

[0050] System Overview

[0051] Referring now to the drawings, in particular Figure 1 , illustrates a general diagram of a system 100 according to various aspects of the present disclosure. The system 100 is a dedicated (specific) computing environment that includes multiple hardware processing devices 102 linked together by one or more networks 104.

[0052] The network 104 provides communication links between the various processing devices 102 and may be supported by networking components 106 that interconnect the processing devices 102, including, for example, routers, hubs, firewalls, network interfaces, wired or wireless communication links and corresponding interconnections, cellular stations and corresponding cellular conversion technologies (e.g., converting between cellular and TCP / IP, etc.). Furthermore, the network(s) 104 may include connections using one or more network configurations, examples of which include an intranet, an extranet, a local area network (LAN), a wide area network (WAN), a wireless network (WiFi), the Internet, including the World Wide Web, cellular, and / or other arrangements that enable communication between the processing devices 102, etc.

[0053] The processing device 102 may be implemented as a server, a personal computer, a laptop computer, a netbook computer, a tablet computer, a dedicated device, a special-purpose computing device, a personal data assistant (PDA) processor, a handheld computer, a cellular device including a cellular mobile phone, a smartphone, a computer processing device on an industrial vehicle, a computer processing device on a machine (fixed or mobile) in an environment, or other device capable of communicating via the network 104 .

[0054] In particular, the processing devices 102 are provided on one or more industrial vehicles 108, such as forklifts, reach trucks, stock pickers, automated guided vehicles, turret trucks, tractors, rider pallet trucks, walk-in stackers, etc. In the example configuration shown, the processing devices 102 on the industrial vehicles 108 communicate wirelessly through one or more access points 110 with corresponding networking components 106, which serve as a connection to the network(s) 104. Alternatively, the industrial vehicles 108 may be equipped with cellular or other suitable wireless technology that allows the processing devices 102 on the industrial vehicles 108 to communicate directly with remote devices (e.g., through the network(s) 104).

[0055] System 100 also includes a processing device implemented as a server 112 (e.g., a web server, a file server, and / or other processing device) that supports a platform 114 and corresponding data sources (collectively identified as data sources 116). Platform 114 can be used to execute components of a personalized, spaced-apart interactive system, as described more fully herein.

[0056] In the illustrative example, the data source 116, which need not be co-located, includes a database that links processes performed for the benefit of the enterprise from multiple different domains. In the example shown, the data source 116 includes an industrial vehicle information database 118 that collects data from the operation of industrial vehicles 108, such as in the industrial vehicle domain. The data source 116 also includes a management system 120, such as a warehouse management system (WMS). The WMS associates information with the movement and tracking of goods within the operating environment in the WMS domain. Furthermore, the data source 116 includes a personalized spaced interaction system (PSI) 122 that supports processes performed in the personalized spaced interaction domain, i.e., spaced vehicle interactions, as described more fully herein. In addition, the data source 116 may include a geographic feature management system 124 (supporting processes that utilize environment-based location tracking data for industrial vehicles in the geographic domain), etc. The above list is not exhaustive and is intended to be merely illustrative.

[0057] Industrial vehicles

[0058] refer to Figure 2 The industrial vehicle 108 includes conventional features, including load handling features 130, such as forks, a bed or platform, towing capabilities, etc. The industrial vehicle may also optionally include an operator cabin 132, a battery compartment 134, a display 136, etc.

[0059] Display 136 may be a vehicle display that displays at least one gauge representing a status of the industrial vehicle, eg, as part of an instrument cluster, a vehicle-integrated display, etc. Alternatively, a display may be provided uniquely for the process, as described more fully herein.

[0060] The specific features of the industrial vehicle 108 will vary depending on the type of vehicle. Figure 1 As noted, one or more industrial vehicles 108 include a processing device 102 implemented as a dedicated, specialized computer. Figure 2 , the information linking device 102 mounted to or otherwise integrated with the industrial vehicle 108 may implement reference Figure 1 An example of a processing device 102 is described.

[0061] The information linking device 102 includes the necessary circuitry to enable wireless communication, data and information processing, and wired (and optionally wireless) communication with components of the industrial vehicle 108 and across the network 104. As a few illustrative examples, the information linking device 102 includes a transceiver 142 for wireless communication. Although a single transceiver 142 is shown for convenience, in practice, one or more wireless communication technologies may be provided. For example, the transceiver 142 may be configured to communicate across the network 104. Figure 1The access point 110 communicates with a remote server (e.g., Figure 1 The transceiver 142 may also optionally support other wireless communications, such as cellular, Bluetooth, infrared (IR), ultra-wideband (UWB), or any other technology or combination of technologies. For example, using a cellular to IP bridge, the transceiver 142 may use cellular signals to communicate across the network 104 ( Figure 1 ) communicates directly with a remote server (e.g., a manufacturer server).

[0062] The illustrated information linking device 102 also includes a controller 144 having a processor coupled to a memory for implementing computer instructions, including computer-implemented processes or aspects thereof, as more fully illustrated and described herein. The controller 144 utilizes the transceiver 142 to communicate with the remote server 112 ( Figure 1 ) exchange information to control the operation of the industrial vehicle 108, remotely store information extracted from the industrial vehicle 108, etc., for performing the vehicle-initiated rhythmic interactions described herein, etc.

[0063] The information linking device 102 also includes a power enable circuitry 146 controlled by the controller 144 to selectively enable or disable the industrial vehicle 108 (or alternatively, selectively enable or disable specific control modules, devices, or vehicle functions, such as hydraulics, traction, etc.). For example, the controller 144 can control the industrial vehicle power enable circuitry 146 to provide power to the industrial vehicle 108, to provide power to selected components of the industrial vehicle 108, to provide power to selected vehicle functions, etc. based on an operator login, detected geographic features, etc.

[0064] Furthermore, the information linking device 102 includes an input / output (I / O) monitor 148 for communicating with peripheral devices attached to or otherwise mounted on the industrial vehicle 108 (such as sensors, meters, encoders, switches, etc. (collectively indicated by reference numeral 150)) via a wired or wireless connection. The I / O monitor 148 can also be connected to other devices, such as third-party devices 152 (such as RFID scanners, displays, meters, or other devices). This allows the controller 144 to obtain and process information monitored on the industrial vehicle 108.

[0065] The information linking device 102 is coupled to and / or communicates with other industrial vehicle system components via a suitable vehicle network bus 154. The vehicle network bus 154 is any wired or wireless network bus or other communication capability that allows the electronic components of the industrial vehicle 108 to communicate with each other. By way of example, the vehicle network bus 154 may include a controller area network (CAN) bus, a local interconnect network (LIN), a time-triggered data bus protocol (TTP), or other suitable communication technology. Moreover, in actual implementations, the vehicle network bus may include multiple buses, each using the same or different technologies and / or protocols. For ease of discussion, the bus(es) will be collectively referred to as a "bus."

[0066] As will be described more fully herein, utilization of the vehicle network bus 154 enables seamless integration of the controller 144 and other components of the information linking device 102 into the local electronics of the industrial vehicle 108. In an exemplary configuration, the controller 144 of the information linking device 102 connects to, understands, and is able to communicate with local vehicle electronics components, such as traction controllers, hydraulic controllers, modules, devices, bus-enabled sensors, displays, lights, light bars, sound generating devices, headsets, microphones, haptic devices, and the like, collectively referred to as control module(s) 156. As such, by communicating set points, performance tuning parameters, and the like to the appropriate control module(s) 156 via the vehicle network bus 154, the controller 144 can modify vehicle performance, for example, by limiting maximum travel speed, setting maximum lift height, lifting weight, and the like.

[0067] The information linking device 102 may also interact with vehicle devices such as a key fob reader 158 across the vehicle network bus 154 to facilitate mechanisms that require an operator to log into a particular industrial vehicle before being authorized to operate the vehicle.

[0068] According to yet another aspect of the present disclosure, a context-based location tracking device 160 is provided on the industrial vehicle 108. As shown, the context-based location tracking device 160 is connected to the vehicle electronics via the vehicle network bus 154. Thus, the context-based location tracking device 160 can communicate directly with the controller 144 and other devices linked to the vehicle network bus 154 of the corresponding industrial vehicle 108. The context-based location tracking device 160 enables the industrial vehicle 108 to spatially know its location in a dimensionally constrained environment (e.g., a mapped portion of an industrial enterprise).

[0069] In applications described more fully herein, when the industrial vehicle 108 is operating indoors, conventional technologies such as a global positioning system (GPS) are unlikely to be effective. However, the environment-based location tracking device 160 can include a local awareness system using markers, including fiducial markers, RFID, beacons, lights, or other external devices to enable spatial awareness within an industrial (e.g., warehouse, manufacturing plant, etc.) environment. Furthermore, local awareness can be achieved through a machine vision guidance system, for example, using one or more cameras or other devices. The environment-based location tracking device 160 can also / alternatively use transponders and triangulation calculations to determine location. Furthermore, the environment-based location tracking device 160 can use a combination of the above and / or other techniques to determine the current (real-time) location of the industrial vehicle 108. As such, in certain embodiments, the location of the industrial vehicle 108 can be determined continuously (e.g., every second or less). Alternatively, other sampling intervals can be derived to continuously determine the industrial vehicle's location over time (e.g., at discretely defined time intervals, periodic or otherwise constant and recurring time intervals, intervals based on interrupts, triggers, or other measurements).

[0070] The context-based location tracking device 160 may also use knowledge read from inertial sensors, vehicle sensors, encoders, accelerometers, gyroscopes, and the like (e.g., via the control module 156 across the vehicle network bus 154, via sensors 150 and / or third-party devices 152 across the I / O monitor 148 and the vehicle network bus 154, and / or the like) to determine the location of the industrial vehicle 108 within the industrial enterprise and / or to enhance or modify the location determination from the location tracking device 160.

[0071] As will be described more fully herein, the controller 144 can execute computer code to perform vehicle-initiated operator interactions, including, for example, rhythmic operator interactions, personalized rhythmic operator interactions, and the like. Vehicle-initiated interactions can be performed, for example, by the controller 144 communicating with the vehicle operator via the display 136 and / or other input / output devices (e.g., lights, speakers, haptic devices, etc.). Vehicle-initiated interactions can also be performed, for example, by the controller 144 interacting with remote devices (e.g., server 112, platform 114, data source 116, etc.). Vehicle-initiated interactions can also be performed, for example, by the controller 144 interacting with vehicle components (e.g., sensors, gauges, encoders 150, third-party devices 152) via the I / O monitor 148. Further, vehicle-initiated interactions can be performed, for example, by the controller 144 interacting with components across the vehicle network bus 154, such as by interacting with a control module 156, a key fob reader 158, environment-based location tracking 160, combinations thereof, and the like. The controller 144 may also interact with electronic devices near the industrial vehicle 108 via direct communication (eg, Bluetooth, UWB, etc.) or via interaction with the server 112 , which then communicates with remote devices near the industrial vehicle 108 .

[0072] Spaced interaction system

[0073] In an example embodiment, classroom training and personalized and / or targeted coaching are both supported by machine-based (e.g., vehicle-initiated) training, observation, reinforcement, or a combination thereof. In particular, the computer-implemented process implements a vehicle-initiated rhythmic operator interaction system that can reside on the industrial vehicle 108 or in communication with a remote computer (e.g., server 112, Figure 1 ) and executed on the interacting industrial vehicle 108.

[0074] In a practical embodiment, the industrial vehicle 108 has a communication interface that is communicatively coupled to a user interface (e.g., via Figure 2 136 and / or other input / output devices) of the hardware (e.g., controller 144, Figure 2 Generally speaking, the vehicle controller interacts with the vehicle operator through a user interface to teach the operator how to best work with the industrial vehicle 108 .

[0075] refer to Figure 3 , a block diagram illustrating an environment that facilitates vehicle-initiated rhythmic operator interaction. In general, Figure 3 The system shown in FIG can include reference Figure 1 and / or Figure 2For clarity of discussion, the system includes a network 104 across which a server 112 executes a personalized, spaced-apart interactive platform 114 for communication with remote devices. The platform 114 also interacts with data sources 116, which may include, for example, industrial vehicle data 118, warehouse management data 120, personalized spatial interaction data (PSI data) 122, geographic data 124, and the like, as described more fully herein.

[0076] To ensure a high training success rate and improve operator compliance with vehicle operations, environmental restrictions, etc., the system includes an operator interface 162. In practice, when an operator logs into a corresponding industrial vehicle, the operator interface 162 can be presented as a graphical user interface on a vehicle display, for example, via the information link device 102 and the display 136 ( Figure 2 ); in the processing device 102 (reference Figure 1 Description), combinations thereof, etc. This enables the system to generate operator-oriented behavioral feedback messages, for example, when the vehicle operator is in the vehicle. The system can also implement an operator-oriented performance tracking dashboard via the operator interface 162.

[0077] In an exemplary embodiment, the trainer interface 164 provides a trainer-oriented operator performance dashboard. In practical applications, the trainer interface 164 can be implemented as a Figure 1 ) (such as a desktop computer, tablet computer, etc.). The trainer may also be a vehicle operator, and in this regard the trainer interface 164 may also / alternatively be implemented on a display of the industrial vehicle 108. The platform 114 may also communicate directly with the industrial vehicle 108. As will be described in more detail herein, the platform may communicate with the industrial vehicle 108A to execute a vehicle-initiated rhythmic operator interaction system as more fully described herein. The platform 114 may also communicate with other devices in the vicinity of the industrial vehicle 108A, for example, by communicating with the industrial vehicles 108B and 108C. In practice, the platform 114 may communicate with any electronic device in the environment of the industrial vehicle 108A that is configured to receive commands and / or messages from the platform 114. Thus, the platform 114 may tune the environment to the operator of the industrial vehicle 108A, examples of which are described more fully herein. As an alternative or replacement for the above, the industrial vehicle 108A may communicate directly with other devices (eg, the industrial vehicle 108B and the industrial vehicle 108C) via Bluetooth, UWB, or other local communication technologies to perform operator tuning of the work environment near the operator.

[0078] The trainer interface 164 enables a user, such as an administrator or trainer, to interact with the system, e.g., to load, program, configure, modify, etc., the vehicle operator's experience (e.g., pace of learning, topics of interaction, etc.). The trainer interface 164 may also be used to display training level metrics, e.g., via a dashboard.

[0079] The system may also optionally include a supervisor interface 166 that enables a supervisor to view supervisor-level dashboard statistics and reports on training progress, for example, by viewing the operator and trainer performance dashboard for the supervisor. In an actual implementation, the trainer interface 164 and / or the supervisor interface 166 may be implemented as a controller on the processing device 102 ( Figure 1 ) (such as a desktop computer, tablet computer, etc.).

[0080] Vehicle-initiated messages, controls, or a combination thereof

[0081] The system herein monitors operator knowledge via interaction with the vehicle operator and industrial vehicle electronics. The system may also learn the extent of the operator's knowledge and / or skills, or monitor both the operator's knowledge and learning knowledge.

[0082] Furthermore, the system monitors industrial vehicle usage against expected operator behavior(s) to determine whether operator knowledge is being translated into expected operator behavior. Furthermore, the system modifies operator behavior (when measured behavior deviates from expected operator behavior) through, for example, reinforcement, timely, in-process training, messaging, and other feedback. Thus, industrial vehicles, for example, via the vehicle-initiated system, are helped to become better vehicle operators.

[0083] In some embodiments, the industrial vehicle 108 learns the operator's knowledge and / or skills, for example, by learning the operator's limitations / capabilities. This allows the industrial vehicle 108 to dynamically adjust vehicle operating characteristics to suit the operator's skills, for example, by changing set points, setting speed limits, hydraulic limits, lift limits, combinations thereof, etc.

[0084] As an example, by monitoring the operator's interactions with prompts presented on a display screen, the system evaluates the operator's input and sets at least one internal operating state of the industrial vehicle (e.g., a set point for maximum speed, lift height, load weight, etc.). Then, by monitoring usage data generated by the electrical components of the industrial vehicle 108, the system dynamically adjusts the vehicle's internal operating state over time to tune the industrial vehicle for the operator by monitoring how well the operator operates the vehicle according to the skills presented via the prompts. This can prevent premature wear of components, prevent failures, avoid excessive energy consumption, and other drawbacks of improper operation, among other things.

[0085] Also, in some cases, instead of or in conjunction with adjusting the industrial vehicle 108, the system can adjust, control, modify, communicate with, and the like electronic equipment, machines, vehicles, and other electrical components in the vicinity of the operator's work environment. For example, the system (e.g., via the platform 114 and / or through direct communication with the industrial vehicle 108) can warn other industrial vehicle operators (e.g., by transmitting a message displayed on an operator display to a nearby industrial vehicle) that the operator being trained is traveling in the same aisle as other vehicle operators. As another example, the system can control other industrial vehicles, such as by sending commands to nearby industrial vehicles to implement lane avoidance, implement minimum passing distances, set a temporary maximum speed, maximum lift height, sound a horn, activate lights, set a temporary geographic zone, or a combination thereof. As such, in some embodiments, not only is the operator's industrial vehicle 108 controlled, but the operator's vicinity of the work environment is also controlled in a choreographed manner.

[0086] In an example embodiment, the operator interface 162 is used to provide vehicle-initiated messages to the vehicle operator. The vehicle-initiated messages can be provided while the operator is operating the corresponding industrial vehicle, or when the operator is not actively using the vehicle (e.g., performing training / learning on a computer, tablet, smartphone, etc.).

[0087] Simple reference Figure 1 、 Figure 2 and Figure 3 For example, controller 144 presents information to the vehicle operator via display 136. For example, display 136 may present training material about industrial vehicle features and then test the operator's knowledge, for example, by presenting multiple-choice questions on display 136. Controller 144 may access I / O devices that enable the user to respond to the questions and use information stored in memory to determine whether the operator has answered the questions correctly. Furthermore, the questions may elicit behaviors, actions, controls, demonstrate the use of vehicle technical features, demonstrate knowledge of vehicle technical limitations, and so on. Controller 144 monitors the operator's interactions with operator interface 162 and may also monitor data transmitted across vehicle network bus 154 to detect operator activity in response to the questions presented on the display. Thereafter, controller 144 may monitor operator activity during normal, ongoing use of the industrial vehicle. Based on the monitored data values, controller 144 may issue positive reinforcement, negative reinforcement, instructions, corrections, or other appropriate feedback on an ongoing basis. Thus, controller 144 observes operator behavior and provides feedback in real-time.

[0088] In another example embodiment, messages are triggered by detected patterns in industrial vehicle data. This enables the system to push training events and then monitor operator behavior, or monitor operator behavior to decide which topics / training to push to the operator.

[0089] In any event, the controller 144 can interface with and understand vehicle data (e.g., data values ​​transmitted by control modules, sensors, and other vehicle electronics) transmitted across the vehicle network bus 154. The controller 144 can also correlate the vehicle data with training topics associated with vehicle-initiated interactions, thereby facilitating automated machine responses as described more fully herein.

[0090] The controller 144 may also communicate with a remote computer system via the transceiver 142, e.g., across a wireless interface, e.g., to request that the platform 114 scan records associated with the vehicle operator stored in the industrial vehicle data 118, etc. Thus, the platform 114 may receive a request from a remote source (e.g., Figure 1 )) receive information to derive patterns. Furthermore, patterns can be derived from a combination of locally determined information and wirelessly received information. Data patterns can be associated with certain (correct or incorrect) operator behaviors, teaching or training data stored in the PSI data 122, and the like. Furthermore, patterns defining behaviors can be predefined. Alternatively, the industrial vehicle can learn patterns by observing data transmitted across the industrial vehicle network bus, thereby actively building patterns from monitored events. In this regard, building patterns can be performed by identifying certain data types, reordering the data, sorting the data, filtering the data, or otherwise identifying a combination of data that corresponds to a high probability of an event of interest.

[0091] Absolute control

[0092] Comparisons between vehicle operators typically provide at best relative differences. In contrast, the system herein can determine absolute characteristics of operator performance. For example, the system can evaluate a large amount of data (e.g., by evaluating data sources 116 across one or more enterprises). For example, using cloud-based storage, manufacturers can access a large amount of data. For example, data extracted from industrial vehicle data 118, PSI data 122, etc. across one or more fleets can be normalized to rate operators, such as by using a standard deviation distribution. For example, operators can be rated on a scale of five (5) categories according to a predetermined distribution, e.g., trainee, novice, intermediate, advanced, expert. Because of the distribution, this approach limits those who can become "experts." Similarly, this approach allows operators to quickly transition from trainee to novice, and from novice to intermediate. Becoming advanced is more difficult, and becoming expert is even more difficult. Moreover, all operators are normalized against the same scale. In this regard, the system makes a decision as to whether the operator is good or bad, etc. In other embodiments, there is no distribution, thereby providing a fixed scale to evaluate the operator's progress from trainee to expert.

[0093] Example user interface interactions

[0094] As described herein, at intervals determined by the cadence, the controller 144 interacts with the operator interface 162 to provide instructions, ask questions, provide training information, e.g., via video, text, images, combinations thereof, etc., to present information regarding the operation of the industrial vehicle, instructions regarding environmental operating procedures, etc.

[0095] As also described herein, the controller 144 occasionally interacts with other electronics of the industrial vehicle 108 to provide feedback to the operator, for example, in real time. In the illustrative embodiment, the controller 144 can interact with the operator interface 162 to provide at least three types of real-time messages.

[0096] The first message is considered an "approval (green) message," which may occur after recording one (or multiple repetitions thereof) of a correct action (eg, an operator correctly using the industrial vehicle's remote control to move the industrial vehicle 108 a short distance).

[0097] The second message is considered a "disagreement (red) message" which may appear after recording one (or multiple repetitions thereof) of an incorrect behavior (eg, an operator incorrectly leaving the industrial vehicle while the industrial vehicle is still moving).

[0098] The third message is considered a “reminder message” and can appear after multiple repetitions of the same purportedly correct or incorrect behavior are recorded. Because some data patterns can be repeated in multiple contexts, only some of which are clearly defined as correct or incorrect behavior, these reminder messages are intended to generate awareness of correct behavior without the risk of frustrating the operator.

[0099] Performance Tracker Dashboard for Operators

[0100] According to various aspects of this disclosure, the operator can use the vehicle-mounted device (e.g., the display 136 ( Figure 2 ), operator interface 162 ( Figure 3 ), tablet, offline laptop, etc., to instantly access an overview of their performance. In an example embodiment, this dashboard provides the vehicle operator with three key sets of information, including usage, compliance, and operation. Non-limiting examples of operator-facing performance tracker information can be used to answer questions such as:

[0101] “Do I use the vehicle features often enough?” (usage);

[0102] “Did I use the vehicle features correctly?” (Compliance); and

[0103] “Did I manipulate the feature correctly?” (Manipulate).

[0104] In an exemplary embodiment, a value indicating operator performance in each of these categories is calculated based on the number of correct and incorrect operator actions recorded by the controller 144 interacting with the operator through the display 136 by reading information from the I / O monitor 148, reading information across the industrial vehicle network bus 154, etc. Thus, operator performance may use the same application programming interface (API) as the feedback messages visible to the operator.

[0105] In some embodiments, additional information is provided, for example, about how the operator can improve the score in any category. This information is available upon user request and can be accessed through a submenu within the same tool.

[0106] Operator Performance Tracker Dashboard for Trainers

[0107] In an example embodiment, the trainer interface 164 provides a dashboard view designed for training employees. The trainer interface 164 uses the same API that also triggers operator feedback messages and generates data for the operator's personal dashboard, but combines all trainee-operator values ​​associated with the individual trainer. The example dashboard presents three key sets of information: combined trainees, combined trainee compliance, and combined trainee actions. Thus, the trainer interface 164 can answer questions for the trainer, such as:

[0108] “Does the trainee use the vehicle features often enough?” (combined trainee usage);

[0109] “Did the trainee use the vehicle features correctly?” (combined trainee compliance);

[0110] “Did the trainee operate the feature correctly?” (Combined trainee operation).

[0111] Through submenus or other suitable navigation features, the trainer can access the individual performance scores of each of his or her associated trainees, thereby identifying underperformers and training topics to focus attention on when approaching individual trainee command selectors. In the case where the trainer himself or herself may be an industrial vehicle operator, this dashboard should be accessible via the onboard device (e.g., information link device 102 and display 136 ( Figure 2 ))access.

[0112] Operator Performance Tracker Dashboard for Supervisors

[0113] The supervisor interface 166 can be implemented as a web-based dashboard and can be accessed by the supervisor to provide performance information for all associated employees (trainers and operators). The operator performance tracker dashboard for the supervisor can use the same indicators (usage, compliance, and operation) discussed above. In an example embodiment, the supervisor-oriented dashboard enables warehouse supervisors and managers to better identify the reasons for improper, inefficient, or inaccurate operation of industrial vehicles. This can lead to direct improvements in vehicle operation in terms of efficiency, energy, reduced impact, and other vehicle functional characteristics. This can therefore lead to reduced productivity losses because they are believed to be directly related to missed training opportunities.

[0114] Aspects of the present disclosure improve the training of technology products with a systems-based approach that can reduce manual training effort, provide faster results, maintain high and stable productivity levels over time, provide tangible value to customers that will generate long-term engagement, and so on.

[0115] Vehicle-initiated rhythmic operator interaction system

[0116] Personalized, spaced learning is a method for teaching knowledge and changing behavior. Personalized, spaced learning becomes effective in industrial environments when implemented as a vehicle-initiated, rhythmic operator interaction system. As described herein, this can include the industrial vehicle 108 pushing knowledge to the operator, operator behavior pushing knowledge to the industrial vehicle 108, or a combination thereof. In this regard, it is observed that teaching certain topics can increase knowledge but does not necessarily change behavior. To achieve this, memorized knowledge needs to be "translated" into behavior, as ultimately, the system needs to change behavior to become more productive, efficient, or better.

[0117] The system herein combines industrial vehicle operational data with a graphical user interface on the industrial vehicle 108 (e.g., communicating with the display 136 via the controller 144 via the information linking device 102 to implement the operator interface 162) to "generate" a better operator, dynamically change the internal operating state of the vehicle to "generate" a better vehicle that is specifically tuned to the operator's current skills / knowledge / abilities, tune an operating environment that is specifically tuned to the operator's current skills / knowledge / abilities, combinations thereof, and the like.

[0118] This system-based behavioral reinforcement is designed to support human-based behavioral reinforcement, meaning that classroom training and individual coaching can still be conducted using the industrial vehicle 108.

[0119] System-based behavioral reinforcement

[0120] System-based behavioral reinforcement helps vehicle operators transform knowledge into (changed or new) behaviors. This involves combining on-truck instructions (e.g., when the truck is stationary) with real-time feedback from the system (in this case, the industrial vehicle), as described more fully herein. For example, the system can immediately notify the operator the moment they exhibit incorrect behavior for a particular topic. Furthermore, the vehicle can immediately notify the operator the moment they employ the correct behavior for a particular topic. The system can also choose to suppress feedback, for example, to prevent operator information fatigue.

[0121] algorithm

[0122] According to aspects of the present disclosure, an algorithm performs one or more functions including, for example, monitoring industrial vehicle usage data, performing control personalization, data analysis for spaced messaging, monitoring industrial vehicle data to determine operator performance (e.g., based on pattern matching of vehicle data), implementing a feedback cadence for one or more topics based on user performance, adapting reinforcement, spacing between (new) topics, etc. based on user performance, deciding whether real-time user behavior is correct or incorrect (based on industrial vehicle data), taking action on correct or incorrect behavior, combinations thereof, and the like.

[0123] A general framework for vehicle-initiated rhythmic operator interaction

[0124] refer to Figure 4 (and overall return to reference Figure 1 、 Figure 2 and Figure 3 ), illustrating an example of a computer-implemented process 400 that implements vehicle-initiated rhythmic operator interaction. More particularly, the computer-implemented process 400 is performed in an environment including an industrial vehicle having a user interface communicatively coupled to a vehicle controller, as described more fully herein, e.g., with respect to Figure 1 、 Figure 2 、 Figure 3 or a combination thereof. For example, the process 400 may be performed by the information linking device 102 ( Figure 2 ) of the controller 144 and the display 136 ( Figure 2 ), operator interface 162 ( Figure 3 ) or a combination thereof. The process may also optionally interact with platform 114 ( Figure 1 ) interact to provide performance feedback of the industrial vehicle 108.

[0125] At 402, the process detects, for example, by a processor on an industrial vehicle, that an operator has entered appropriate login credentials. Figure 2 , the controller 144 can identify that the operator attempting to log into the industrial vehicle has the appropriate login credentials by comparing the login information received by the remote key reader 158 against a stored list of authorized operators. Figure 4 .

[0126] At 404, the process loads the interaction profile into the vehicle controller. By way of illustration and not limitation, the example interaction profile includes a pattern with a start action and an end action. For example, the start action and the end action can be performed by a controller on an industrial vehicle, by the platform 114 ( Figure 1 ) or a combination thereof, measured, discovered, or otherwise identified. Moreover, the pattern is associated with operator actions that can be performed when operating the industrial vehicle, for example, to control technical features of the industrial vehicle, control the industrial vehicle according to environmental regulations (policies), etc.

[0127] In some embodiments, operator actions are associated with definitions that characterize actions that may be performed when operating an industrial vehicle, such as operating vehicle technical features, controls, vehicle capabilities, environmental policies or procedures, and the like.

[0128] The interaction profile also includes rules defining measures of performance associated with the mode and target responses to the measures of performance.

[0129] Return to Brief Reference Figure 2, and for clarity purposes only, a few example interaction profiles are discussed.

[0130] For example, loading the interaction profile into the vehicle controller at 404 may include defining an operator action as an operation using a remote control feature of the industrial vehicle (e.g., a GO button as described herein). Here, while using the remote control feature, the process also includes defining a start action and an end action for a mode based on the operator's interaction with the remote control feature of the industrial vehicle, defining a rule as at least one of a travel distance limit or the operator's presence on the industrial vehicle.

[0131] As another example, loading the interaction profile into the vehicle controller at 404 may include defining the operator actions as operations for driving the industrial vehicle (e.g., operating traction control, accelerator, steering control, brakes, etc.) Here, the process also includes defining a start action and an end action of the pattern based on the operator interacting with at least one control of the industrial vehicle to drive the industrial vehicle (e.g., an accelerator pedal, a steering wheel, a joystick on a control arm, a brake control), and defining the rules as a function of at least one of travel speed, travel distance, acceleration, or braking.

[0132] As yet another example, loading the interaction profile into the vehicle controller at 404 may include defining the operator action as an operation using a load handling feature of the industrial vehicle (e.g., operating a hydraulic assembly, interacting with a rack height selection control, etc.) Here, the process also includes defining a start action and an end action of a pattern based on the operator interacting with at least one control of the industrial vehicle to operate the load handling feature of the industrial vehicle, and defining the rule as at least one of a lift height or a lift weight.

[0133] As another example, loading the interaction profile into the vehicle controller at 404 includes defining an operator action as an operation using a hybrid feature of the industrial vehicle (e.g., actuating a hybrid control that affects fork lift / hydraulics with steering and / or vehicle movement / traction control). Here, the process also includes defining a start action and an end action of a pattern based on the operator interacting with at least one control of the industrial vehicle to operate the hybrid feature of the industrial vehicle, and defining rules as a function of the hybrid operating feature.

[0134] As another example, loading the interaction profile into the vehicle controller at 404 includes defining an operator action as an operation on the industrial vehicle specified by an environmental protocol (e.g., stopping at the end of an aisle or an intersection and sounding the horn). Here, the process also includes defining a start action and an end action of a pattern based on an operator interacting with at least one control of the industrial vehicle to operate the industrial vehicle according to the environmental protocol, and defining a rule as a function of the environmental protocol.

[0135] To more clearly illustrate the foregoing, a specific non-limiting example is an interaction profile for teaching operator actions defined by industrial vehicle remote control operations.

[0136] An example definition of an operational characteristic is the use of a "GO" button on an industrial vehicle control.

[0137] An example pattern is defined by detecting that an operator presses a GO button, the industrial vehicle begins traveling in response to the operator pressing the GO button, and the vehicle stops after traveling a certain distance.

[0138] Thus, an example initiating action is detection by the controller 144 , for example, from the remote control module 156 across the vehicle network bus 154 , of an operator pressing a GO button, for example, on a wireless remote control in the operator's possession.

[0139] An example ending action corresponds to detection, such as by the controller 144 from the traction control module 156 across the vehicle network bus 154 , that the vehicle has stopped after traveling in response to the user pressing the GO button.

[0140] An example rule is "the distance traveled in response to pressing the GO button on the remote control should be less than 9 meters."

[0141] The target response is that "the vehicle travels less than 9 meters in response to pressing the GO button" is an appropriate behavior, while the vehicle travels 9 meters or more is an inappropriate behavior. Now return to reference Figure 4 .

[0142] The above examples are intended to be illustrative and not limiting, as industrial vehicles, industrial vehicle types, environmental policies and operating procedures, combinations thereof, and the like may be used to establish the content of an interaction profile.

[0143] At 406, the process determines the operator's cadence. For example, the cadence can be associated with the operator's actions for the interaction profile. Generally speaking, the cadence controls the timing / spacing when vehicle-initiated operator interactions occur, such as for training events, positive reinforcement events, negative reinforcement events, reminder events, etc. Example cadences are referred to herein as Figure 5 and Figure 6 However, a cadence describes the interval when the next cadence-based vehicle-initiated operator interaction occurs. Such interactions can be time-based, event-based, based on a predicted decay in operator knowledge retention, a combination thereof, etc. As such, vehicle-initiated operator interactions are not simply triggered by the simple occurrence of an isolated event.

[0144] In this regard, the process at 406 can perform loading a cadence into the vehicle controller, wherein the cadence defines the intervals between interactions associated with operator actions of the interaction profile. As described above, the intervals of the cadence can be established based on time, a predetermined number of instances of an event defined by a pattern being encountered, or a combination thereof (e.g., based on a predetermined number of instances of an event defined by a pattern being encountered and a predetermined amount of time).

[0145] In another embodiment, the process determines the cadence by detecting the identity of the operator by a processor on the industrial vehicle and by determining the operator's cadence based on an interaction profile.

[0146] The process may also optionally perform training / teaching operator interactions at 406. For example, the process may perform displaying information related to the operator actions of the interaction profile via the user interface of the industrial vehicle. As will be described in greater detail herein, the displayed information may include displaying training information that teaches the operator how to properly perform the operator actions to define correct and incorrect responses, teaches appropriate use and / or behavior of the operator actions, teaches what constitutes improper use and / or behavior of the operator actions, poses questions or tests the operator's knowledge of the operator actions, combinations thereof, and the like.

[0147] The training may also test the operator's knowledge of the training information by presenting questions (eg, multiple choice questions) that the operator needs to answer, where the operator's responses determine whether the operator knows the correct answer.

[0148] After the operator acquires knowledge of the operator's actions, the process then performs machine observation of the vehicle operator in an ongoing manner.

[0149] At 408, the process monitor is collected electronically by the industrial vehicle, such as information that may be transmitted across the vehicle network bus (e.g., the vehicle network bus 154), as detected by the I / O monitor 148, etc., as referenced. Figure 2 The monitored data may be aggregated, selectively aggregated, etc. to record appropriate information. For example, the data may be aggregated to retain only data that is between, near, around, or otherwise associated with a pattern (e.g., near a start action and / or an end action, etc.).

[0150] The process then performs the set of actions in response to detecting first data in the monitored information corresponding to a start action and second data in the monitored information corresponding to an end action.

[0151] Thus, at 410 , the process identifies the first data from the monitored information as satisfying a start action. Continuing with the above example, the process can detect, for example, that the vehicle operator has pressed a GO button via data transmitted across the vehicle network bus 154 . Even after a start action is detected, the process continues to monitor the information electronically collected by the industrial vehicle.

[0152] At 412, the process identifies the second data from the monitored information as satisfying the end action. Continuing with the above example, the process detects that the vehicle has stopped after driving in response to the operator pressing the GO button. In some cases, the start action and the end action may be satisfied by the same action / event. In other cases, the start action and the end action may be defined by different and / or unique operating parameters.

[0153] At 416 , the process applies the selected components of the monitored information against the rules to define a performance response (i.e., a performance calculation). As an example, the selected components of the information may be derived from data monitored (or otherwise electronically collected) across a vehicle network bus collected between a start action and an end action, immediately before a start action or an end action, in response to a start action, immediately after a start action or an end action, in response to an end action, combinations thereof, and the like.

[0154] Keep consistent with this example and refer back to Figure 2 , the monitored data notifies the controller 144 that the GO button was pressed by reading data from the remote control module 156 that transmits data across the vehicle network bus 154. The controller 144 then determines that the vehicle begins to travel and then stops. For example, the controller 144 may read odometer, encoder data, control information, etc. from the traction control module 156 across the vehicle network bus 154. As another example, the controller 144 may detect a change in position by reading data from the environment-based location tracking 160 across the vehicle network bus 154, etc. In any case, for this example, assume that the controller 144 determines that the vehicle has traveled 6 meters. The controller 144 applies a rule ("the distance traveled in response to pressing the GO button on the remote control should be less than 9 meters"). The performance calculation may be a Boolean value, for example, the rule evaluates to TRUE / FALSE. As another example, the performance calculation may be a value, for example, traveled 6 meters, or some other suitable format. Now referring to Figure 4 .

[0155] The process then compares the performance response to the target response at 416. Still staying with the above example, the process compares the performance calculation (eg, 6 meters) to the target response (eg, less than 9 meters).

[0156] At 418, the process generates performance feedback based on the comparison of the performance response to the target response. For example, performance feedback can be defined as the vehicle operator encountering or otherwise initiating an operational feature in the interaction profile, and the result is correct behavior. For some instances of detected behavior, the process can also generate operator feedback to the display 136 at 418, such as, in this case, an "approval (green) message," so that the operator knows at a glance that the correct behavior was not only performed, but also recognized by the system. In some cases, the performance feedback at 418 can also trigger a modification of at least one performance parameter of the industrial vehicle, equipment near the operator, or a combination thereof, as described more fully herein.

[0157] At 418, the process may also record instances of encountering events, such as by saving performance feedback in a profile history. A profile history is information stored in memory, for example, in an industrial vehicle memory, on a remote server (e.g., in PSI data 122 ( Figure 1 )) and their combinations, etc.

[0158] At 420, the process detects whether an observation event is due based on the rhythm. If no observation event is due, the process loops back to 408 so that monitoring can continue.

[0159] In some embodiments, regardless of whether the observation event expires (e.g., the rhythm interval is not triggered), the process outputs a negative performance feedback message to the display indicating that the performance response is unsatisfactory in view of the target response. Thus, if the operator response is unsatisfactory according to the interaction profile, the operator can be notified.

[0160] For example, if a vehicle operator has already learned an operating characteristic and is demonstrating correct behavior, the process may not wish to continue re-teaching the operating characteristic. However, at intervals determined by a cadence, such as once every 40 hours, a reminder message may be presented, training questions may be asked, or some combination of user interactions may be initiated. For example, the cadence may be time-based and therefore independent of the actual occurrence of an event. Thus, at the cadence time, the system may ask the operator questions regarding vehicle usage, operating policies, polling issues, and the like. If the answer is incorrect, the system may treat the incorrect answer similarly to detecting incorrect behavior. Correspondingly, if the answer is correct, the system may treat the correct answer similarly to detecting correct behavior. In this regard, the system may simply correct the incorrect answer by providing training. In some cases, the system may present the training topic, for example, via video, by remotely controlling the industrial vehicle 108, or in other ways. The system may also modify the industrial vehicle and / or the environment in which the vehicle operates in response to incorrect (or correct) operator interactions, as described in greater detail herein.

[0161] Furthermore, the process can periodically provide positive reinforcement or negative reinforcement based on the behavioral response. Most of the time, performance feedback can be suppressed. However, sometimes reinforcement (positive or negative) is appropriate. Therefore, observation events can be based on a rhythm such as time intervals, for example, providing a reminder every 4 hours. Observation events can be based on events, such as providing performance feedback every n observation behaviors (for example, every 10 times). Observation events can also be a combination of time and event, for example, after four hours, at the next detected occurrence of the operational feature, or whichever occurs first.

[0162] In some embodiments, observing event expiration based on cadence detection includes adjusting the cadence. Adjusting the cadence can be based on a desired spacing, such as a desired time spacing, a desired event spacing, or a combination thereof. For example, in an example embodiment, if performance feedback in the profile history indicates a predetermined number of consecutive correct behaviors, then the process can increase the cadence, and if performance feedback in the profile history indicates incorrect behaviors, then the process can decrease the cadence.

[0163] Furthermore, in some embodiments, for example, where time spacing is used, the process may set a minimum and maximum value for the time spacing, and may also increase the desired time spacing toward the maximum value when the performance feedback results in a correct response, and decrease the desired time spacing toward the minimum value when the performance feedback results in an incorrect response. For example, the process may decrease the desired time spacing toward the minimum value by a first amount when the performance feedback results in an incorrect response and the number of correct operator responses is below a threshold, and decrease the desired time spacing toward the minimum value by a second amount less than the first amount when the performance feedback results in an incorrect response and the number of correct operator responses is above the threshold.

[0164] At 422, the process outputs performance feedback if the observation event expires, otherwise suppresses performance feedback. After providing feedback, the process flows back to 408 to continue as more fully described herein. In some cases, such as when the tempo is changed (e.g., made longer or shorter), the process may loop back to 406. For example, if the observation event expires, the process may output a positive performance feedback message indicating that the performance response was satisfactory in view of the target response, a training message that reinforces the training information, or a combination thereof to the display.

[0165] In some embodiments, the process may also include presenting instructions regarding the operating feature, such as by the controller 144 via the display 136. The instructions may solicit operator feedback, such as by presenting the instructions regarding the operating feature as a question, a multiple-choice question, etc. This instruction may be part of the associated interaction profile and may provide content for training the operator regarding the proper functioning of the operating function.

[0166] As noted more fully herein, the system may also modify the performance of the industrial vehicle 108, electronic equipment in the operator's work environment, combinations thereof, etc. For example, the process may modify at least one operating parameter of the industrial vehicle to tune the performance of the industrial vehicle to the operator's skills.

[0167] Process 400 provides at least two different opportunities for this interaction. For example, at 418 , the system may simply record the response to the event. Alternatively, based on the appropriate response, the system may modify vehicle parameters, such as maximum speed, maximum lift weight, maximum lift height, hybrid functionality, etc. New capabilities may be added or existing features may be modified. This may be accomplished by modifying control module setpoints or other operating parameters, such as by commands transmitted across the vehicle network bus 154 by controller 144 . The system may also affect other electronic devices in the operator's work area. For example, upon exhibiting inappropriate behavior, the system may send a push notification to nearby vehicles, informing them that the operator is nearby and not exhibiting inappropriate behavior. For example, proximity may be determined based on location tracking, e.g., using environment-based location tracking 160 , knowledge of last access point 110 visits, or other location determination methods. In addition to push notifications, the system may also modify the operation of nearby vehicles, such as modifying the travel range enabled by the remote control (e.g., the GO button), setting a minimum passing distance range, setting a maximum lift height, maximum travel speed, etc. This ensures that the industrial vehicles and other industrial vehicles in the operator's vicinity are "tuned" to the operator's skills during operator training.

[0168] The system may also modify the performance of the industrial vehicle 108, electronic devices in the operator's work environment, combinations thereof, etc., at 422 in a manner similar to 418, for example, by modifying at least one operating characteristic of the industrial vehicle based on the profile history at the time established by the cadence. Furthermore, the system may modify the performance of the industrial vehicle 108, electronic devices in the operator's work environment, combinations thereof, etc., at 418 and / or 422. For example, in practice, positive modifications (e.g., unlocking a new capability, increasing a current capability (e.g., travel speed, lift height, lift weight, mix, etc.)) are associated with the cadence interval at 420. However, negative modifications (e.g., locking a capability, decreasing a current capability (e.g., travel speed, lift height, lift weight, mix, etc.), modifying the operating environment) may be performed at either 418 (e.g., taking immediate action) or 422 to associate the negative modification resulting from the erroneous behavior with the cadence at 420.

[0169] Rhythm

[0170] As described above, the cadence (including the spacing between messages) can be time-based, event-driven, or a combination thereof. Furthermore, messages can be generic, vehicle-specific, environment-specific, uniquely tailored to a particular vehicle operator, or a combination thereof. This is possible because an operator is required to log on to the industrial vehicle before the industrial vehicle is enabled for normal operation.

[0171] The algorithm that sets the pacing can be application-specific. As a few illustrative examples, the logic can change the spacing of messages based on correct or incorrect answers and / or behavioral performance. In an example embodiment, a correctly answered topic triggers a longer spacing, and when answered correctly again, an even longer spacing is triggered, and so on. In a practical application, the spacing has minimum and maximum times so that questions are not too frequent or too rare. If a question is answered incorrectly and / or incorrect behavior is observed, the system can revert to the original short spacing so that learning can focus on the missed topic.

[0172] In some embodiments, this dynamic spacing is driven uniquely per topic, mapped to each operator. Thus, an operator training on multiple topics can have several different cadences, with a specific cadence for each topic.

[0173] Furthermore, the cadence can be linked to a profile, as discussed in the "Absolute Control" section of this document. This allows an operator who performs at a high level to quickly move to a lower level (e.g., trainee, novice, etc., to intermediate), but gradually becomes more difficult to transition to advanced or expert. In other embodiments, the system can allow all operators to reach the same state, for example, all operators can become experts in one or more technical features, environmental procedures, etc.

[0174] Also, in some embodiments, successful interactions (i.e., correct answers, correct behaviors exhibited, combinations thereof, etc.) can be automatically rewarded by the vehicle to enable improved vehicle performance (e.g., higher travel speeds, heavier load weights, hybrid capabilities, etc.). Likewise, in some embodiments, the system can impose penalties based on incorrect operator answers and / or behaviors, such as degrading vehicle performance, reducing performance capabilities, etc. For example, the processor of the controller 144 can communicate across the vehicle network bus 154 to program set points in the controller, such as to set maximum travel speed, maximum acceleration, lifting / lowering, and other vehicle capabilities.

[0175] In some embodiments, there are multiple cadences, such as a unique cadence per operator, per topic, to set intervals for reinforcement, and a unique cadence to control the rate at which new topics are introduced. Each time a cadence is triggered, the system has the opportunity to modify the vehicle's internal operating state, for example, dynamically adjusting the vehicle operator based on operator interaction with a graphical user interface, based on recorded operator performance on the vehicle, based on environmental data, a combination thereof, and the like. In this regard, each cadence corresponding to each interaction profile topic need not modify the same industrial vehicle operating state. For example, an interaction profile instance for proper stopping can affect maximum travel speed, while an interaction profile instance for load handling can affect mixing capacity, and so on.

[0176] Sample Rhythm 1

[0177] In reference Figure 5 In the first example described, the cadence provides a default spacing for all operators. In this example, the process 500 assumes that the operator has successfully logged into the industrial vehicle (similar to 402, Figure 4 ), and the controller 144 has loaded the corresponding interaction profile (similar to 404, Figure 4 ).

[0178] The system introduces new topics, for example, by interacting with a processor on an industrial vehicle, outputting content to the operator interface 162 to provide training, guidance, presenting test questions to be answered, etc. Once the operator has satisfied the training, the operator must demonstrate correct behavior within a predetermined number of consecutive times (e.g., 4 times). Correct behavior can be determined, e.g. Figure 4 As described in (see, e.g., 408-418, Figure 4 After an initial number of consecutive correct behaviors are met, operator interaction occurs at fixed intervals. The intervals can be event / encounter based (e.g., every 10 event encounters), time based (e.g., every 80 hours), or a combination thereof (e.g., the earlier of 10 event encounters or 80 hours). Incorrect behavior resets the system back to initial training, and the process starts over.

[0179] For example, the system can be broken down into three steps. The first step is to introduce reinforcement messaging to the operator. In an example embodiment, the system introduces one new behavior per operator, per truck type, and every 40 hours of operation. Initially, the operator receives a first prompt message for the new behavior, after which the operator is required to receive four consecutive green messages, corresponding to four consecutive correct behaviors. After initial training, the second step is to calculate the operator's reinforcement interval. For example, after introduction, a green message / beep sound appears for every 10th correct behavior. A red message appears for incorrect behaviors, and the user is reset to step one. As an optional third step, the system can customize the cadence based on the operator, for example, based on accumulated data. Here, the system can update the cadence interval, the number of correct behaviors that are positively reinforced, adjust the number of negative reinforcements before returning to step one, and so on. In some embodiments, the system continues to monitor the operator's proficiency over time and updates the reinforcement interval accordingly in real time.

[0180] Specific reference Figure 5 , a flowchart illustrates an implementation of a process 500 for vehicle-initiated rhythmic operator interaction for a first example rhythm. In this regard, the process 500 can be implemented on computer-readable hardware storing machine-executable program code, where the program code instructs a processor to implement the described process. The process 500 can also be executed by a processor coupled to a memory, where the processor is programmed by the program code stored in the memory to perform the described process. For example, the process 500 can be executed by the controller 144 ( Figure 2 )accomplish.

[0181] exist Figure 5 In the flowchart:

[0182] L = Learning mode correct continuous behavior

[0183] T = a variable set to the number of consecutive correct actions to be considered trained, e.g., T = 4

[0184] C = count of correct behaviors after training

[0185] I = a variable set to the number of consecutive correct actions before the next green message, e.g., I = 10

[0186] Once the data is collected, the system can tune T and I for personalized interactions.

[0187] At 502, the process teaches the operator a new behavior (e.g., using any of the techniques described more fully herein). Furthermore, the process sets a number of consecutive behaviors (L) to zero. Additionally, the process sets a count of the number of times the operator performed the correct behavior (C) to zero.

[0188] For example, consider a simple case where there is only one topic to be addressed. Briefly refer back to Figure 4 At 404 , the processor of the controller 144 is loaded with the interaction profile. The interaction may be associated with a training topic and may include the code necessary for the controller 144 to communicate the training topic corresponding to the new behavior to the vehicle operator via the display 136 .

[0189] By way of illustration and not limitation, a plain English representation of an example topic might be "Automatically control the industrial vehicle 108 to move forward less than 9 meters using the vehicle remote control 'GO' button." However, the vehicle operating motion data underlying the topic may include code for implementing operator interaction on the display 136 (e.g., by presenting instructional statements, asking questions, asking multiple-choice questions, etc.). The vehicle operating motion data may also be used to express a first rule regarding how to interpret an operator response, such as interpreting an answer entered via data read from an appropriate input / output device on the vehicle, and a second rule for determining a measure of performance, as described above.

[0190] Furthermore, code may be necessary to convert generic rules into rules that can be interpreted by the vehicle controller. For example, a rule could be "TD = distance traveled by the truck from start to stop." If TD < 9 meters, the operator is not on the vehicle, and the GO button is pressed to initiate movement, then the behavior is correct; if (TD >= 9 meters AND the GO button is pressed) OR (TD < 9 meters AND the operator is on the truck) OR (TD < 9 meters and the traction control is manually engaged), then the behavior is incorrect.

[0191] At 504, the process detects an action from the operator. For example, the operator may perform an action that is registered in a programmed mode that is detected by the controller 144. For example, see Figure 4 Any combination of 408-416 and other examples throughout the text.

[0192] As mentioned above, the rules are interpreted based on actual vehicle data. As an example, the process herein can map patterns to actual vehicle network bus data (e.g., reference Figure 2Continuing with the above example, a rule may require detection of a GO button on a remote control being pressed. On a first vehicle, the system may interpret this requirement by issuing a query to the remote control module across the vehicle network bus 154 looking for a first specific value. Furthermore, the rule may require that the operator exit the vehicle when the GO button is pressed. As such, the process may issue a query to an operator presence sensor in the operator cabin platform, wherein the query is looking for a value indicating that no operator is present. A second industrial vehicle of a different type may detect that the GO button is pressed by directly identifying a wireless signal (e.g., a Bluetooth signal) from a wireless remote control. It may be determined that the operator has exited the vehicle based on the strength of the received wireless signal. In some embodiments, some vehicles may not be able to interpret every requirement and / or condition in the rule. Here, the process may omit components of the rule, modify the rule to accommodate available data, etc. Now return to reference Figure 5 .

[0193] At 506, the process determines whether the detected behavior is correct behavior. If the detected behavior is correct behavior, then process 500 proceeds to 508. Otherwise, process 500 proceeds to 510. The process determines whether the behavior is correct by using techniques, examples of which are described in greater detail herein.

[0194] At 508, the process provides a positive reinforcement message (e.g., a word, a symbol, etc.) displayed to the operator to let the operator know that the correct behavior has been received, and the number of consecutive behaviors (L) is incremented by one. Figure 5 In an example embodiment of , the positive reinforcement message is displayed in a green format (e.g., green font, green background, green symbol, or a combination thereof). Figure 4 428 and / or 422. In other embodiments, the affirmative reinforcement may be a tone, chime, flashing light, message, or other output.

[0195] At 510, the process provides a negative reinforcement message (e.g., a word, a symbol, etc.) to let the operator know that an incorrect behavior has been received. Figure 4 418 and / or 422. Here, the process also resets the number of consecutive actions (L) to zero. Additionally, the count of the number of times the operator performed the correct action (C) is also reset to zero (as it may have been incremented through other passes of the process, as discussed below). Figure 5 In the example embodiment of , the negative reinforcement message may be displayed in a red format (eg, red font, red background, red symbols, or a combination thereof). The process then proceeds to 512.

[0196] Regardless of how the process proceeds to 512, if the number of consecutive behaviors (L) does not equal the number of consecutive correct behaviors required for training (T), then the process 500 loops back to 504 to wait for another instance of the topic behavior. However, if the number of consecutive behaviors (L) equals the number of consecutive correct behaviors required for training (T), then the process exits the training mode (i.e., 502 to 512) and enters the proficiency mode by proceeding to 514.

[0197] At 514, the process is in a skilled mode and the operator is considered trained. Therefore, the frequency of display of affirmative reinforcement messages will be reduced.

[0198] At 514 , the process detects further actions by the operator, similar to 504 .

[0199] At 516, the process determines whether the detected behavior is the correct behavior. If the received behavior is the correct behavior, then the process 500 proceeds to 518. Otherwise, the process 500 proceeds to 510.

[0200] Again, at 510, a negative reinforcement message (e.g., a word, symbol, etc.) may be displayed to the operator to let the operator know that an incorrect behavior has been received, and the number of consecutive behaviors (L) is reset to zero. Additionally, the count of the correct number of times the operator performed the correct behavior (C) is also reset to zero (as it may have been incremented through other passes of the process, as discussed below). Figure 5 In an embodiment, the negative reinforcement message may be displayed in a red format (eg, red font, red background, red symbols, or a combination thereof). The process 516 then re-enters the training mode and proceeds to 512.

[0201] However, at 518 (because the correct behavior was received), the process increments the correct count (C) of the number of times the operator performed the correct behavior in the skilled mode by one. However, no positive reinforcement message is displayed.

[0202] At 520, the process determines whether the number of consecutive correct actions has reached a threshold value (1). If the number of consecutive correct actions is less than the threshold value, the process loops back to 514 and waits for an action to be received. On the other hand, if the number of consecutive correct actions is equal to the threshold value, the process proceeds to 522.

[0203] At 522, the process displays a positive reinforcement message (e.g., a word, a symbol, etc.) to the operator to let the operator know that the correct behavior has been received. Figure 4422. The process also sets the number of consecutive behavior counts (C) in the proficiency mode to zero. Continuing with the above example, the positive reinforcement message can be displayed in a green format (e.g., green font, green background, green symbol, or a combination thereof). The process 500 loops back to 514 to wait for another behavior.

[0204] Thus, in training mode, the operator receives positive reinforcement for each correct action. However, in proficiency mode, positive reinforcement is shown only once for consecutive correct actions equal to a threshold.

[0205] Figure 5 Process 500 can be used for one or more new behaviors. For example, one process can train an operator to use a remote feature, and another process can train an operator to honk at an intersection. Failure in one process will not affect the status of the other. As a different example, the same process can be used to train two behaviors, where an incorrect behavior in either process will affect the other.

[0206] As another example, if the new behavior is honking at an intersection, the industrial vehicle (or a remote server in communication with the industrial vehicle) can know when the industrial vehicle is approaching an intersection due to a location tracking system on the vehicle or elsewhere in the facility. If the user honks at the intersection, that behavior is detected. However, the lack of an action can also be detected. Thus, in the example where the operator does not honk, even if the operator does not take action, the fact that the operator did nothing is a behavior detected because the system detected the vehicle at the intersection.

[0207] Modifications to the industrial vehicle 108 can be performed at several points during the implementation of the process 500 (e.g., as described at 418 and / or 422). For example, industrial vehicle modifications can be performed at 502, the "YES" transition from 512 to 514, the negation message at 510, each instance of 522, each nth instance of 522, combinations thereof, and the like.

[0208] Sample Rhythm 2

[0209] As another example, cadences can be personalized based on operator and / or topic, e.g. Figure 6 In this regard, process 600 may be substituted for Figure 5 Process 500. Figure 5 Again, this process assumes that the operator has successfully logged into the industrial vehicle (similar to Figure 4 402), and the controller 144 has loaded the corresponding interaction profile (similar to Figure 4 404).

[0210] As an example, personalization can be performed based on the ratio of correct to incorrect behavior events.As another example, personalization can be performed based on total behavior events to achieve a proficiency goal.

[0211] The first sub-process is to introduce reinforcement messaging to the operator. In the example, the system introduces one new behavior at a time to each operator and each truck type. First, the operator receives the first training, for example, the first prompt message for the new behavior. The prompt message is followed by 3 consecutive green messages for 3 consecutive correct behaviors. Before the next new behavior is introduced, the operator must reach a predetermined proficiency goal. This first sub-process is similar to Figure 4 In this example, the rhythm is defined by the spacing between affirmative reinforcements that increases each time an appropriate behavior is observed. As an example, after each correct behavior in a sequence or set of correct sequential behaviors, the affirmative message spacing (spacing rate) increases, for example, by one (1) to a maximum (e.g., 10 intervals) between affirmative messages, based on graded increments, such as 3, 6, 9, according to some other scale, such as non-linear, etc. If an incorrect behavior is detected, then the training mode is set / reset, requiring the operator to demonstrate a predetermined number of correct sequential behaviors, such as 3, before resuming the rhythm of increasing spacing. Here, the spacing rate can be reset to a previous state, such as to the beginning of the training process, such as one (1) interval. Alternatively, after retraining, the operator can restart counting spacings where the operator stopped. Thus, if the operator was at 5 intervals when the incorrect behavior occurred, then after completing the required sequential behavior (e.g., 3 in this case), the user would return to a spacing of 5.

[0212] Determining or otherwise calculating an operator's initial learning proficiency can be performed in a variety of ways. As an example, initial learning proficiency can be determined by the ratio of correct to incorrect behavior events. As an illustration, an x-hour (e.g., 40-hour) rolling average ratio of correct to incorrect behaviors defines a point on a bell curve distribution that sets when a new task can be added for a particular operator.

[0213] As another example, learning proficiency can be set based on the total number of behavioral events to achieve a proficiency goal. As an example, the system can count the total number of correct behavioral events + incorrect behavioral events required for the operator to reach a predetermined proficiency goal (e.g., a 90% correct to incorrect behavior ratio, where the number of behavioral events required to achieve 90% proficiency defines a point on a bell curve distribution).

[0214] The system can also personalize the spacing of reinforcements between green messages / tones. As an example, a distribution point on a curve (e.g., a bell curve) controls the spacing of green messages and takes over the initial spacing. As habits are formed, the distribution point on the curve also controls how quickly the temporal spacing increases. The system calculates a bell curve distribution, which may require aggregated proficiency data from many operators. However, as the number of operators and the aggregated data grow, the bell curve becomes more accurate over time. Optionally, the accumulated data about individual operators can eventually replace the original calculation, providing highly accurate personalized spacing. Over time, the system continues to monitor the operator's proficiency and updates the reinforcement spacing accordingly in real time.

[0215] Specific reference Figure 6 , a flow chart implementing a process 600 for learning reinforcement for an operator of an industrial vehicle, wherein there is a variable threshold for the number of times a reinforcement message is suppressed. In this regard, unless otherwise noted, process 600 is similar to Figure 5 Process 500.

[0216] exist Figure 6 In the process 600, the threshold value of the number of times the reinforcement message is suppressed is constant.

[0217] And, as shown in the figure:

[0218] L = Learning mode correct continuous behavior

[0219] T = a variable set to the number of consecutive correct actions to be considered trained, e.g., T = 3

[0220] C = count of correct behaviors after training

[0221] I = increment, for example 1 = 1, 2, etc., controls each sequential interval

[0222] S = Spacing between affirmative reinforcement messages

[0223] Smax = maximum spacing between green messages

[0224] At 602, the process teaches the operator a new behavior. The process also sets the number of consecutive behaviors (L) in the training mode to zero. Additionally, the process sets the count (C) of the number of times the operator has performed the correct behavior in the proficiency mode to zero. In this respect, the teaching of the new behavior is similar to that of 502 ( Figure 5 ).

[0225] At 604, the process detects an action performed by the operator. Examples of detecting an action are similar to those at 504 ( Figure 5 ), and are similar to those examples described in more detail herein.

[0226] At 606, the process determines whether the received behavior is the correct behavior. If the received behavior is the correct behavior, then the process 600 proceeds to 608. Otherwise, the process 600 proceeds to 610. The determination at 606 is similar to that at 506 ( Figure 5 ) and may be performed using any of the techniques described more fully herein.

[0227] At 608, the process provides a positive reinforcement message (e.g., a word, a symbol, etc.) displayed to the operator to let the operator know that the correct behavior has been received. Also, the process increments the number of consecutive behaviors (L) by one. Figure 6 In the embodiment of the present invention, the affirmative reinforcement message is displayed in a green format (e.g., green font, green background, green symbol, or a combination thereof). In this regard, the message at 608 is similar to that at 508 ( Figure 5 )

[0228] At 610, the process displays a negative reinforcement message (e.g., a word, a symbol, etc.) to the operator to let the operator know that an incorrect behavior has been received, e.g., similar to 510 ( Figure 5 ). Here, the process also sets / resets the number of consecutive actions (L) to zero. Additionally, the process sets the count (C) of the correct number of times the operator performed the correct action to some number, such as zero, a previous state, etc., as described more fully herein. Figure 6 In an embodiment, the negative reinforcement message is displayed in a red format (e.g., red font, red background, red symbols, or a combination thereof).

[0229] The process then proceeds to 612, which is similar to 512 ( Figure 5 Regardless of how the process proceeds to 612, if the number of consecutive behaviors (L) does not equal the number of consecutive correct behaviors required for training (T), then process 600 loops back to 604 to receive another behavior. However, if the number of consecutive behaviors (L) equals the number of consecutive correct behaviors required for training (T), then the process exits training mode (i.e., 602 to 612) and enters mastery mode by proceeding to 614. In some embodiments, the spacing S can be adjusted, for example, set to S=max(1, S1), or some other desired value.

[0230] At 614, the process is in a skilled mode and the operator is considered trained. Therefore, affirmative reinforcement messages will be displayed less frequently so as not to annoy, frustrate, distract, or otherwise create noise for the operator. At 614, similar to 604 and 514 ( Figure 5 ), detect the operator's further behavior.

[0231] At 616, the process determines whether the received behavior is the correct behavior. If the received behavior is the correct behavior, then the process 600 proceeds to 618. Otherwise, the process 600 proceeds to 610. In this respect, the process at 616 is similar to that at 515 ( Figure 5 ).

[0232] At 618 (because the correct behavior was received), the process increments the count of the number of times the operator performed the correct behavior correctly in the skilled mode (C). However, the positive reinforcement message is not displayed. In some embodiments, the increment is 1, in which case 618 is similar to 518 ( Figure 5 In other example embodiments, the increments may be based on some hierarchical, increasing, decreasing, non-linear, or other increments. An example is to implement by 3, up to 21; increment by 2, then 3, then 4, and so on.

[0233] At 620, the process determines whether the number of consecutive correct behaviors has reached a variable set representing the spacing (S) between positive reinforcement messages. If the number of consecutive correct behaviors is less than the variable (S), the process loops back to 614 and waits for another instance of the behavior to be encountered. On the other hand, if the number of consecutive correct behaviors is equal to the variable (S), the process proceeds to 622.

[0234] At 622, the process provides a positive reinforcement message (e.g., a word, a symbol, etc.) to the operator to let the operator know that the correct behavior has been received, and the number of consecutive correct behaviors (C) in the skilled mode is reset to zero. In this respect, 622 is similar to 522 ( Figure 5 ).exist Figure 6 In an embodiment, the affirmative reinforcement message is displayed in a green format (e.g., green font, green background, green symbol, or a combination thereof).

[0235] At 624, the process determines whether the variable (S) is equal to the maximum spacing between green messages (Smax). If not, the process 600 increases the variable (S) by the increment value (e.g., any positive integer) at 626 and proceeds to 614 to receive another action. Otherwise, the process 600 skips 626 and loops back to 614 to wait for the next encounter with an instance of the event.

[0236] Thus, in training mode, the operator receives positive reinforcement for each correct action. However, when in proficiency mode, positive reinforcement is shown only once for consecutive correct actions equal to the variable (S).

[0237] Figure 6Process 600 can be used for one or more new behaviors. For example, one process can be run to train an operator to honk at an intersection, while another process can be run to train an operator to perform a mixed function. Failure in one process will not affect the status of the other. As a different example, the same process can be used to train two behaviors, where an incorrect behavior in one will affect the other.

[0238] The process 600 can be expanded. For example, each time Smax is reached (or each nth time Smax is reached, where n is any integer), the system can spawn another instance of the process for a new feature, proficiency, behavior, etc., which can begin at 602 while the current behavior continues in its own loop.

[0239] Example Process for Providing Performance Feedback

[0240] refer to Figure 7 , illustrates a computer-implemented process 700 for providing performance feedback for an industrial vehicle. The industrial vehicle has hardware including a user interface communicatively coupled to a vehicle controller, as more fully described herein. The illustrated process may be performed using any combination of components as described herein. In this regard, Figure 7 The process 700 is executed with Figure 4 Note that process 700 assumes that the operator has logged in, similar to 402 ( Figure 4 ).

[0241] At 702, the process loads the interaction profile into the vehicle controller. Here, the interaction profile corresponds to the training topic and includes rules associated with the vehicle's operational capabilities. The process at 702 is similar to that at 404 ( Figure 4 ).

[0242] At 704, the process associates the target vehicle activation with the rule, wherein the target vehicle activation enables the vehicle controller to apply the rule. Here, the target activator at 704 can be used to define the start action 410 ( Figure 4 ) and end action 412( Figure 4 ).

[0243] At 706, the process determines whether a training event is due, for example based on cadence (see 406, Figure 4 ).

[0244] As an example, process 706 may include determining whether a training event is due at 708, for example, based on a predetermined cadence and training status. For example, it may be determined whether the operator is in training mode or proficiency mode as described above. Furthermore, due to the cadence, message opportunities may be suppressed due to cadence requirements.

[0245] Assuming the cadence is triggered to the feedback state, the process can detect whether the industrial vehicle is stopped at 710. If the industrial vehicle is not stopped, then the process 700 can continue (illustrated), or the process can loop back to wait for the industrial vehicle to stop. If the industrial vehicle is stopped, then the process performs a first vehicle-initiated interaction at 712 by presenting instructions, environmental procedures, etc. on how to implement vehicle operating capabilities to the operator via a user interface on the industrial vehicle, corresponding to the training topic described more fully herein.

[0246] At 714, the process performs machine observation. For example, the process 700 can perform machine observation by monitoring vehicle usage information during normal operation of the industrial vehicle via the vehicle controller 144 on the industrial vehicle at 716. For example, the monitoring at 716 can be implemented similar to monitoring 408 ( Figure 4 ). The process at 714 also includes extracting operator-initiated vehicle activations from the monitored vehicle usage information at 718. For example, the process may be similar to that of 410-416 ( Figure 4 ) method to achieve the extraction at 718.

[0247] At 720, the process uses rules to compare the target vehicle activations with the monitored operator-initiated vehicle activations to derive a performance state that indicates the operator has achieved vehicle operational capability. The comparison at 720 may be similar to the comparison at 416 ( Figure 4 ) to achieve it.

[0248] At 722, a decision is made based on the tempo whether to output feedback or suppress feedback. If feedback is not given, the process loops back to 706 to continue monitoring and processing as described more fully herein. In this regard, the decision 420 ( Figure 4 ) to implement the decision at 722.

[0249] At 724, process 700 performs a second vehicle-initiated interaction, for example, for each machine observation. The second vehicle-initiated interaction may be performed by outputting performance feedback indicating the derived performance status to a graphical user interface based on a predetermined cadence. Process 724 may be similar to output 422 ( Figure 4 ) to achieve it.

[0250] The process then loops back to, for example, 706 or 714 , depending on the implementation, to continue monitoring and processing as more fully described herein.

[0251] Process 700 may optionally implement industrial vehicle control, modification, control, or messaging of neighboring equipment near the operator / industrial vehicle, or a combination thereof, e.g., at 720 and / or 724, e.g., as more fully described herein (e.g., see Figure 4418 and / or 422 of the specification).

[0252] Example Process for Providing Performance Feedback

[0253] Figure 7 Illustration from the perspective of an industrial vehicle Figure 4 aspects of the process, and Figure 8 Graphical user interface view Figure 4 all aspects of the process.

[0254] refer to Figure 8 , a computer-implemented process 800 for providing performance feedback for an industrial vehicle is provided. The industrial vehicle has hardware including a user interface communicatively coupled to a vehicle controller, as more fully described herein. The illustrated process can be performed using any combination of components as described herein.

[0255] At 802, the process displays information related to the operation, technical features, operating environment regulations, etc. of the industrial vehicle on a user interface of the industrial vehicle based on an operator-specific cadence. In some cases, the information may require an operator response. The process at 802 may, for example, Figure 7 712 is implemented and thus may be performed using any of the techniques described more fully herein.

[0256] At 804 , the process receives an operator response at a user interface.

[0257] At 806 , the process transmits the operator response from the user interface to the processing system.

[0258] At 808 , the process compares the operator response to a reference response, such as by a processing system.

[0259] At 810 , the process generates information feedback, such as by a processing system, based on comparing the operator response to a reference response.

[0260] At 812 , the process transmits the information feedback to a user interface for displaying the information feedback at the user interface.

[0261] Depending on the information required by the associated interaction profile, the operator may be required to interact with the industrial vehicle as part of the training, for example, to demonstrate the ability to operate the technical features of the industrial vehicle. Here, additional user interactions are captured at 814.

[0262] At 814 , the process performs machine observation to determine whether the operator response and the reference response are consistent.

[0263] As a non-limiting example, machine observation at 814 can be performed by the processes described at 816-828. For example, the example machine observation can include monitoring information transmitted across a vehicle network bus at 816, and identifying monitored information associated with the operation of a technical feature of the industrial vehicle at 818. In this example, machine observation at 814 can also be performed by applying the identified monitored information against a rule at 820 to define a performance calculation and comparing the performance calculation to a target response at 822. In this example, machine observation at 814 can be further performed by generating performance feedback based on the comparison at 824, detecting whether an observation event is due based on the cadence at 826, outputting the performance feedback when the observation event is due at 828, and suppressing the performance feedback when the observation event is not due.

[0264] In this regard, process steps 816-828 may also be used to perform steps 408-422 ( Figure 4 ) and / or 714-724( Figure 7 ) or vice versa.

[0265] Miscellaneous

[0266] As described more fully herein, and with reference to any of the foregoing figures, calculating an operator-specific cadence can be based on one or more factors, such as based on analyzing operator performance, detecting operator learning and / or forgetting patterns, and the like. Generating performance feedback can include generating positive performance feedback based on an analysis of positive operator performance. Similarly, generating performance feedback can include generating negative performance feedback based on an analysis of negative operator performance. As noted more fully herein, generating performance feedback can include providing a selected one of an approval message, a disapproval message, and a reminder message. Additionally, generating performance feedback can include providing performance feedback after a plurality of repetitions of the same correct performance state. Generating performance feedback can include generating visual feedback, generating audible feedback corresponding to the visual feedback, transmitting the visual feedback from a controller to a user interface, transmitting the audible feedback from the controller to the user interface, and providing audible feedback at the user interface substantially concurrently with displaying the visual feedback.

[0267] As another example, the processes herein may provide an operator-oriented performance tracker dashboard displayed on a graphical user interface. The operator-oriented performance tracker dashboard may display a metric defining the frequency with which the operator used each vehicle operating action presented to the operator via the graphical user interface. The operator-oriented performance tracker dashboard may also display a compliance mark with each corresponding displayed vehicle operating action. The processes herein may also provide a trainer-oriented dashboard that identifies whether the frequency with which the operator used each vehicle operating action presented to the operator via the graphical user interface meets a first predetermined threshold and whether compliance with each corresponding vehicle operating action exceeds a second predetermined threshold.

[0268] In some embodiments, the controller is located on the industrial vehicle. In other embodiments, the controller is located remotely from the industrial vehicle, for example, implemented on a remote server. In some example embodiments, responsibilities can be split between the controller on the industrial vehicle and the controller on the remote server. For example, the controller on the industrial vehicle can transmit the operator's response to a question, inferred operator performance, etc. to a server remote from the industrial vehicle. Similarly, the controller on the remote server can process the industrial vehicle data against rules and identify behavior using techniques as described more fully herein. The processor on the remote server can then notify the industrial vehicle in real time to present feedback to the operator on the industrial vehicle's display.

[0269] As another example, the remote server may collect operator responses to questions. The remote server may also collect inferred operator performance data corresponding to one or more detected instances of associated operator behavior. The remote server generates aggregated personalized data for the operator, which is formatted for display on a graphical user interface, such as a reference. Figure 2 or Figure 3 Any interface described.

[0270] The figures generally illustrate features that can be combined in any combination. Figure 4 Each feature disclosed and described in the Figures (or any single Figure) needs to be implemented in a working embodiment. Furthermore, embodiments may be derived from selected portions of the description associated with any Figure, or from a combination of selected features described across multiple Figures.

[0271] Example Computer System

[0272] refer to Figure 9, depicts a block diagram of a hardware data processing system according to the present disclosure. Data processing system 900 may include a symmetric multiprocessor (SMP) system or other configuration including multiple processors 910 connected to system bus 920. Alternatively, a single processor 910 may be employed. Local memory 930 is also connected to system bus 920. I / O bus bridge 940 interfaces system bus 920 to I / O bus 950. I / O bus 950 is used to support one or more buses and corresponding devices, such as storage devices 960, removable media storage devices 970, input / output devices 980, network adapter 990, other devices, combinations thereof, and the like. For example, network adapter 990 can be used to enable data processing system 900 to communicate with other data processing systems or remote printers or storage devices via an intervening private or public network.

[0273] The memory 930, storage device 960, removable media storage device 970, or a combination thereof may be used to store data executed by the processor(s) 910 to implement the Figure 1-8 Program code for any aspect of the disclosure described and illustrated in FIG.

[0274] As will be appreciated by those skilled in the art, aspects of the present disclosure may be implemented as a system, process, or computer program product. Moreover, some aspects of the present disclosure may be implemented in hardware, software (including firmware, resident software, microcode, etc.), or by combining software and hardware. In addition, aspects of the present disclosure may be implemented in the form of a computer program product embodied in one or more computer-readable storage media having a computer-readable program code implemented thereon.

[0275] In certain embodiments, any combination of one or more computer-readable media may be utilized. A computer-readable medium may be a computer-readable storage medium or a computer-readable signal medium. A computer-readable storage medium may be a primary storage device or an auxiliary storage device (which may be internal, external, or removable from a host hardware processing device). Examples of computer-readable storage media include, but are not limited to, a portable computer floppy disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, a portable optical disk read-only memory (e.g., CD-ROM, CD-R, CD-RW, DVD, Blu-Ray), or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium may be any tangible (hardware) medium that may contain or otherwise store a program used by or in conjunction with an instruction execution system, device, or apparatus.

[0276] 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. Specifically, a computer readable signal medium is not a computer readable storage medium, and a computer readable storage medium is not a computer readable signal medium.

[0277] 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.

[0278] The computer program code for performing the operations of various aspects of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Java, Smalltalk, C++, etc.) and conventional procedural programming languages ​​(such as the "C" programming language or similar programming languages). The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0279] Aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the processes, devices (systems) and computer program products according to embodiments of the present disclosure. It will be understood that each block of the flowcharts and / or block diagrams, and the combination of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine, so that instructions executed by the processor of the computer or other programmable data processing device create a means for implementing the function / action specified in one or more blocks of the flowcharts and / or block diagrams.

[0280] These computer program instructions may also be stored in a computer-readable storage medium, which may direct a computer, other programmable data processing apparatus, or other device to operate in a specific manner, so that the instructions stored in the computer-readable storage medium produce an article of manufacture, which includes instructions for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0281] The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices, so that a series of operational steps are performed on the computer, other programmable apparatus, or other devices to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide a process for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0282] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functions and operations of possible implementations of the systems, processes and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, fragment or part of code, which includes one or more executable instructions for implementing (one or more) specified logical functions. It should also be noted that in some alternative embodiments, the functions marked in the box may not appear in the order marked in the accompanying drawings. For example, depending on the functions involved, two boxes shown in succession can actually be executed substantially simultaneously, or sometimes the boxes can be executed in the opposite order. It will also be noted that each box in the block diagram and / or flowchart illustration, as well as the combination of boxes in the block diagram and / or flowchart illustration, can be implemented by a dedicated hardware-based system or a combination of dedicated hardware and computer instructions that performs the specified function or action.

[0283] The terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the present disclosure. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

Claims

1. A computer-implemented process for vehicle-initiated rhythmic operator interaction, the process being performed in an environment including an industrial vehicle having a user interface communicatively coupled to a vehicle controller, the process comprising: An interaction profile is loaded into the vehicle controller, which has: a pattern including a start action and an end action, wherein the pattern is associated with an operator action achievable while operating the industrial vehicle; rules defining measures of performance associated with the pattern; and target responses to measures of performance; loading a cadence into a vehicle controller, wherein the cadence defines an interval as a time interval between interactions associated with an operator action of an interaction profile, wherein the time interval is sufficient to enable a vehicle operator to exhibit the operator action associated with at least one prior interaction; displaying information related to operator actions of the interaction profile via a user interface of the industrial vehicle; as well as Perform ongoing monitoring by: monitoring information collected electronically by industrial vehicles; as well as In response to detecting first data within the monitored information corresponding to a start action and second data within the monitored information corresponding to an end action, executing: applying selected components of the monitored information against rules to define performance responses; generating performance feedback based on a comparison of the performance response with the target response; Save performance feedback in profile history; Observe whether the event is due based on rhythm detection; as well as At least one operating characteristic of the industrial vehicle is modified based on the profile history at times established by the cadence.

2. The process of claim 1 , wherein loading the interaction profile into the vehicle controller comprises: An operator action is defined as an operation using the remote control features of an industrial vehicle; defining a start action and an end action of a pattern based on an operator's interaction with a remote control feature of the industrial vehicle; as well as A rule is defined as at least one of a travel distance limit or an operator presence on the industrial vehicle while using the remote control feature.

3. The process of claim 1 , wherein loading the interaction profile into the vehicle controller comprises: Operator actions are defined as the operation of driving an industrial vehicle; defining a starting motion and an ending motion of a pattern based on an operator interaction with at least one control of the industrial vehicle to drive the industrial vehicle; as well as The rules are defined as a function of at least one of travel speed, travel distance, acceleration, or braking.

4. The process of claim 1 , wherein loading the interaction profile into the vehicle controller comprises: An operator action is defined as an operation using the load handling features of an industrial vehicle; defining a starting action and an ending action of a pattern to operate a load handling feature of the industrial vehicle based on an operator interaction with at least one control of the industrial vehicle; as well as The rules are defined as a function of at least one of lift height or lift weight.

5. The process of claim 1 , wherein loading the interaction profile into the vehicle controller comprises: Operator actions are defined as operations using mixed features of industrial vehicles; defining a starting action and an ending action of a pattern to operate a hybrid feature of the industrial vehicle based on an operator interaction with at least one control of the industrial vehicle; as well as Define the rules as functions of the characteristics of the mixing operation.

6. The process of claim 1 , wherein loading the interaction profile into the vehicle controller comprises: Operator actions are defined as operations on industrial vehicles dictated by environmental regulations; defining a start action and an end action of a pattern based on an operator interaction with at least one control of the industrial vehicle to operate the industrial vehicle according to environmental regulations; as well as Define rules as functions of the environment's rules.

7. The process of claim 1 , wherein an operator-specific cadence is loaded into the vehicle controller, wherein the cadence defines intervals as time intervals between interactions associated with operator actions of the interaction profile, including establishing intervals of the cadence based on fixed time intervals.

8. The process of claim 1 , wherein an operator-specific cadence is loaded into a vehicle controller, wherein the cadence defines intervals as time intervals between interactions associated with operator actions of an interaction profile, including establishing intervals of the cadence based on a predetermined number of encounters of instances of an event defined by a pattern.

9. The process of claim 1 , wherein an operator-specific cadence is loaded into a vehicle controller, wherein the cadence defines intervals as time intervals between interactions associated with operator actions of an interaction profile, including establishing intervals of the cadence based on a predetermined number of times and a predetermined amount of time that instances of an event defined by a pattern are encountered.

10. The process of claim 1, wherein displaying information related to the operator actions of the interaction profile via the user interface of the industrial vehicle includes displaying training information that teaches the operator how to properly perform the operator actions so as to define correct responses and incorrect responses.

11. The process of claim 10, further comprising testing the operator's knowledge of the training information by displaying questions that the operator is required to answer, wherein the operator's answers determine whether the operator knows the correct response.

12. The process of claim 10, further comprising: If an observation event expires, a training message reinforcing the training information is output to the display.

13. The process of claim 10, further comprising: If an observation event expires, a positive performance feedback message is output to the display indicating that the performance response was satisfactory in view of the target response.

14. The process of claim 1 , further comprising: Regardless of whether the observation event is due, a negative performance feedback message is output to the display indicating that the performance response is unsatisfactory in view of the target response.

15. The process of claim 1 , further comprising: increasing the tempo if performance feedback in the profile history indicates a predetermined number of consecutive correct behaviors; as well as If performance feedback in the profile history indicates incorrect behavior, then the cadence is reduced.

16. A computer-implemented process for vehicle-initiated rhythmic operator interaction, the process being performed on an industrial vehicle having hardware including a user interface communicatively coupled to a vehicle controller, the process comprising: An interaction profile is loaded into the vehicle controller, which has: a pattern including a start action and an end action, wherein the pattern is associated with an operator action achievable while operating the industrial vehicle; rules defining measures of performance associated with the pattern; and target responses to measures of performance; loading a cadence into a vehicle controller, wherein the cadence defines an interval between interactions associated with an operator action of the interaction profile; Perform ongoing monitoring by: monitoring information transmitted across the vehicle network bus; identifying first data from the monitored information as satisfying an initiating action; identifying second data from the monitored information as satisfying an ending action; applying data monitored across the vehicle network bus against rules between the start action and the end action to define performance calculations; Compare performance calculations to target responses; Generate performance feedback based on comparison; Observe whether the event is due based on rhythm detection; as well as Output performance feedback when an observation event expires, otherwise suppress performance feedback.

17. The process of claim 16, further comprising presenting instructions regarding operator actions requiring operator feedback prior to monitoring.

18. The process of claim 16, further comprising: At least one operating parameter of the industrial vehicle is modified to tune the performance of the industrial vehicle according to the skills of the operator.

19. The process of claim 16, wherein detecting whether an observation event is due based on a cadence comprises adjusting the cadence based on a desired spacing comprising a desired time spacing, a desired event spacing, or a combination thereof.

20. The process of claim 19, further comprising: Set minimum and maximum values ​​for spacing; increasing the expected spacing toward a maximum value in the event that performance feedback results in detecting the occurrence of a correct response; as well as The desired spacing is reduced towards the minimum value in cases where performance feedback results in an incorrect response.

21. The process of claim 16, wherein: Determining the rhythm includes: detecting the identity of the operator by a processor on the industrial vehicle; and A cadence is determined for the operator based on the interaction profile.

22. A computer-implemented process for providing performance feedback of an industrial vehicle having hardware including a user interface communicatively coupled to a vehicle controller, the process comprising: An interaction profile is loaded into the vehicle controller, which has: a mode comprising a starting action and an ending action, wherein the mode is associated with an operational feature that can be achieved while operating the industrial vehicle; rules defining measures of performance associated with the pattern; and target responses to measures of performance; as well as Perform ongoing monitoring by: recording, by the controller, first data indicating that information transmitted across the vehicle bus satisfies a starting action into a first memory; recording, by the controller, second data in a second memory indicating that the information transmitted across the vehicle bus satisfies the end action; evaluating rules to define performance calculations by monitoring data transmitted across a vehicle bus between a start action and an end action; outputting performance feedback to an output device on the industrial vehicle when an event expires based on a cadence observation, wherein the performance feedback is generated based on a comparison of the performance calculation to a target response; Suppressing performance feedback when rhythm-based observation events are not due.

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