Abstract: An autonomous agricultural carrier vehicle, for carrying an agricultural implement includes a chassis with steerable wheels or tracks attached to a frame, wherein the frame includes a mounting apparatus for connection to the agricultural implement. The vehicle further includes an environmental sensor system for determining obstacles or elements present in the vicinity of the carrier vehicle and a control device for controlling the carrier vehicle or the at least one implement, wherein the control device is connected to a position determination system that captures and issues a position to create an autonomous agricultural carrier vehicle with which the impact force is not reduced. No additional tractor vehicle is needed for the implement and no operator is necessary during field work, wherein it is provided that position-dependent working instructions for the carrier vehicle are preferably stored in the control device.

Abstract: The technology relates to handling of self-reflections of sensor signals off of a portion of a vehicle that is operating in an autonomous driving mode. Vehicle pose information and sensor pose information are determined at a given point in time while operating in the autonomous driving mode. Return signals from one or more scans of the environment are received from onboard sensors such as lidar sensors. The system evaluates, based on the vehicle and sensor pose information, whether a segment between a given one of the one or more sensors and a received point from a selected one of the return signals crosses any surface of a 3D model of the vehicle. The received point is identified as a self-return point. In response to identifying the received point as a self-return point, the vehicle is able to perform a driving operation in the autonomous driving mode.

Abstract: The present disclosure relates to a method for straight edge detection by a robot and a method for reference wall edge selection by a cleaning robot. The method for straight edge detection by the robot includes that: position coordinates of detection points are determined according to distance values detected by a distance sensor of the robot and angle values detected by an angle sensor of the robot, and then a final straight edge is determined according to a slope of a straight line formed by adjacent two of the detection points.

Abstract: An autonomous vehicle configured to facilitate its use and/or enhance what it and/or occupants can do with it, such as, for example, by: autonomously acting based on events within the autonomous vehicle, including by autonomously rerouting itself, altering a cabin of the autonomous vehicle, notifying a third party external to the autonomous vehicle, stopping (e.g., parking) the autonomous vehicle, altering how the autonomous vehicle drives itself, and/or performing other actions based on what and/or how an occupant is doing, an emergency, or another event occurring in the cabin of the autonomous vehicle; autonomously acting based on interactions with (e.g., gestures of) humans (e.g., police officers, school-crossing guards, traffic guards at roadwork or other temporary traffic control sites, drivers of other vehicles, etc.) external to the autonomous vehicle; autonomously acting based on indicators placed at particular locations (e.g., drive-through establishments, potholes, parking spots, etc.

Abstract: Systems and methods for facilitating communication with autonomous vehicles are provided. In one example embodiment, a computing system can obtain a first type of sensor data (e.g., camera image data) associated with a surrounding environment of an autonomous vehicle and/or a second type of sensor data (e.g., LIDAR data) associated with the surrounding environment of the autonomous vehicle. The computing system can generate overhead image data indicative of at least a portion of the surrounding environment of the autonomous vehicle based at least in part on the first and/or second types of sensor data. The computing system can determine one or more lane boundaries within the surrounding environment of the autonomous vehicle based at least in part on the overhead image data indicative of at least the portion of the surrounding environment of the autonomous vehicle and a machine-learned lane boundary detection model.

Abstract: A vehicular control system includes a camera, a radar sensor and a processor operable to process image data captured by the camera and radar data captured by the radar sensor. Responsive at least in part to determination that the equipped vehicle will not complete a turn at an intersection before an estimated time to arrival of a detected approaching vehicle at the intersection elapses, the system determines that it is not safe to proceed along the projected path of travel. The system determines that it is safe for the equipped vehicle to proceed along the projected path of travel responsive at least in part to (i) determination that the equipped vehicle will complete the turn at the intersection before the estimated time to arrival of the approaching vehicle at the intersection elapses and (ii) determination that no object is present in the projected path of travel of the equipped vehicle.

Abstract: This disclosure relates in general to systems and methods for optically tracking objects proximate an autonomous vehicle. In particular, an object tracking system capable of refining position data for objects being tracked by determining a location of the objects surrounding the autonomous vehicle at least on part on previously determined locations of the objects. In certain instances, the predicted and detected locations used to arrive at a refined location for the objects can be weighted in different ways depending on conditions of the sensor data and quality of the historical data.

Abstract: Described is a method for detecting a calibration requirement for image sensors in a vehicle. The method includes detecting a ground pattern in a generated image associated with a surrounding of a vehicle. The method includes extracting at least one key point associated with the detected ground pattern, from the generated image. The method includes determining a relative motion parameter associated with the extracted at least one key point based on tracking of the extracted at least one key point over a period of time. The method further includes detecting the calibration requirement for the image sensor based on the determined relative motion parameter and generating an output signal based on the detected calibration requirement.

Abstract: An alert system includes a display device, a display control unit, and a target recognition unit. The display control unit: when the number of targets recognized by the target recognition unit is equal to or more than an upper limit number, sets a display range of a surrounding icon as a whole circular area; and in a state where the number of targets is equal to or more than the upper limit number and the display range of the surrounding icon is set as the whole circular area, when the number of targets increases, reduces the size of the surrounding icon.

Abstract: An information processing apparatus generates a map element that is based on a position and orientation of an image capturing apparatus and three-dimensional position information about a feature point included in an image, generates three-dimensional map information based on a plurality of map elements at a plurality of different positions and orientations, corrects, in such a way as to make smaller a reprojection error of a common feature point in a first map element group, at least one of a position and orientation of the image capturing apparatus and the three-dimensional position included in the first map element group, and corrects, in such a way as to make smaller a reprojection error of the common feature point with use of a second map element group, at least one of a position and orientation of the image capturing apparatus and the three-dimensional position information included in the second map element group.

Abstract: A mobility augmentation system monitors data representative of a user's motor intent and augments the user's mobility based on the monitored motor intent data. A machine-learned model is trained to identify an intended movement based on the monitored motor intent data. The machine-learned model may be trained based on generalized or specific motor intent data (e.g., user-specific motor intent data). A machine-learned model initially trained on generalized motor intent data may be re-trained on user-specific motor intent data such that the machine-learned model is optimized to the movements of the user. The system uses the machine-learned model to identify a difference between the user's monitored movement and target movement signals. Based on the identified difference, the system determines actuation signals to augment the user's movement. The actuation signals determined can be an adjustment to a currently applied actuation such that the system optimizes the actuation strategy during application.

Abstract: A display control device includes an acquisition unit for acquiring a communication state of each of a plurality of communication devices, and a control unit for displaying the communication state of each of the plurality of communication devices in association with each communication device.

Abstract: In accordance with an aspect of the present disclosure, there is provided a method for acquiring coordinate system conversion information, the method comprising: acquiring three-dimensional information including first lane information corresponding to a lane adjacent to a vehicle, through a LiDAR installed at the vehicle, and a surrounding image including second lane information corresponding to the lane, through a camera installed at the vehicle; acquiring first coordinate system conversion information on the LiDAR and the camera by matching the second lane information with the first lane information; and acquiring second coordinate system conversion information on the vehicle and the camera by using top view image conversion information acquired based on the surrounding image, and the driving direction of the vehicle.

Abstract: The present application discloses a method, a device, equipment, and storage medium for determining a sensor solution, where the method includes: establishing a simulated unmanned vehicle and a simulation scene, where the simulation scene is used for the simulated unmanned vehicle to perform simulation driving; determining a first sensor solution according to a initialization parameter, and determining, according to the first sensor solution, simulation data generated by the simulated unmanned vehicle during the simulation driving in the simulation scene; and; and determining a first perception parameter of the first sensor solution according to the simulation data, and correcting the first sensor solution according to the first perception parameter to obtain a sensor solution applied to an unmanned vehicle.

Abstract: A method for regulating falls of a user-controlled character through gaps between surfaces in an extended reality (XR) space in which the user is playing a game includes compiling a record from a previously generated spatial mapping mesh (SMM) of the XR space of surfaces of real elements present in that space, with corresponding positions and dimensions; and, after the game begins, determining whether, if the character approaches a substantially vertical gap between an edge of a first surface at a first level and a second surface, at a second level lower than the first level, continuing motion of the character to fall through the vertical gap will be permitted or prevented. A similar method for regulating jumps rather than falls of a user-controlled character through gaps between surfaces in an extended reality (XR) space in which the user is playing a game is also described.

Abstract: A method of operating a mobile cleaning robot in an environment can include detecting, such as using an optical stream from the mobile cleaning robot, a seasonal object located in the environment. A seasonal cleaning zone can be created based on the detected seasonal object when a current date is within a specified date range. The seasonal cleaning zone can be displayed on a map of the environment.

Abstract: Systems and methods for determining a current state of at least one traffic light (“TL”) for use in controlling an autonomous vehicle are described herein. Implementations can generate a corresponding numerical measure, for each of multiple candidate states of the TL, based on processing an image capturing a region that is predicted to include the TL using machine learning classifier(s). Implementations further generate a corresponding final measure, for each of the candidate states, based on processing each of the corresponding numerical measures using TL model(s), select one of the candidate states as a current state of the TL based on the final measures, and control the autonomous vehicle based on the current state of the TL. Notably, each of the corresponding final measures can be generated as a function of the corresponding numerical measures of the candidate states, and the corresponding numerical measures of the other candidate states.

Abstract: An image processing apparatus includes at least one processor operatively coupled to at least one memory that function as an input unit configured to receive an image signal of a visible light pixel and an image signal of a near-infrared pixel, output from a sensor that includes a visible light pixel component and a near-infrared pixel component, a determination unit configured to determine whether or not an output signal of the near-infrared pixel is higher than a threshold and to produce a determination result, a detector configured to detect a saturated visible light pixel, and a switching unit configured to switch over saturation processing to be applied to the saturated visible light pixel on the basis of the determination result of the determination unit.

Abstract: In a network of autonomous or semi-autonomous vehicles, an alert may be triggered when one of the vehicles switches from autonomous to manual mode. The alert may be communicated to nearby autonomous vehicles so that drivers of those vehicles may become aware of a potentially unpredictable manual driver nearby. Drivers of autonomous vehicles who may have become disengaged (e.g., sleeping, reading, talking, etc.) during autonomous driving may become re-engaged upon noticing the alert. A re-engaged driver may choose to switch his/her own vehicle from autonomous to manual mode in order to appropriately react to an unpredictable nearby manual driver. In additional or alternative embodiments, the alert may be triggered or intensified when indications of impairment of a nearby driver or malfunction of a nearby vehicle are detected.

Abstract: Apparatus and methods for carpet drift estimation are disclosed. In certain implementations, a robotic device includes an actuator system to move the body across a surface. A first set of sensors can sense an actuation characteristic of the actuator system. For example, the first set of sensors can include odometry sensors for sensing wheel rotations of the actuator system. A second set of sensors can sense a motion characteristic of the body. The first set of sensors may be a different type of sensor than the second set of sensors. A controller can estimate carpet drift based at least on the actuation characteristic sensed by the first set of sensors and the motion characteristic sensed by the second set of sensors.

Abstract: A system, a transportation vehicle, a network component, apparatuses, methods, and computer programs for a transportation vehicle and a network component. The method for a transportation vehicle to determine a route section includes operating the transportation vehicle in an automated driving mode and determining an exceptional traffic situation. The method also includes transmitting information related to the exceptional traffic situation to a network component using a mobile communication system and receiving information related to driving instructions for the route section to overcome the exceptional traffic situation from the network component, wherein the receiving of the driving instructions includes tele-operating the transportation vehicle along the route section to overcome the exceptional traffic situation.

Abstract: A materials handling vehicle comprising a path validation tool and a drive unit, steering unit, localization module, and navigation module that cooperate to navigate the vehicle along a warehouse travel path. The tool comprises warehouse layout data, a proposed travel path, vehicle kinematics, and a dynamic vehicle boundary that approximates the vehicle physical periphery. The tool executes path validation logic to determine vehicle pose along the proposed travel path, update the dynamic vehicle boundary to account for changes in vehicle speed and steering angle, determine whether the dynamic vehicle boundary is likely to intersect obstacles represented in the layout data based on the determined vehicle pose at candidate positions along the proposed travel path, determine a degree of potential impingement at the candidate positions by referring to the dynamic vehicle boundary and obstacle data, and modify the proposed travel path to mitigate the degree of potential impingement.

Abstract: A mobile robot includes a communication unit that communicates with another mobile robot, a sensing unit for sensing the other mobile robot existing in a detection area encompassing a predetermined projected angle with respect to the front of a main body of the mobile robot, and a control unit configured for rotating the main body so that the other mobile robot is sensed in the detection area. The communication unit transmits a control signal configured to cause the other mobile robot to travel in a linear direction by a predetermined distance, to the other mobile robot when the other mobile robot is present in the detection area.

Abstract: A system includes a memory device and a processing device, operatively coupled to the memory device, to obtain a set of predicted behavior data including data indicative of one or more predicted object behaviors for one or more respective objects in an environment of an autonomous vehicle, obtain a set of observed behavior data including data indicative of one or more observed object behaviors of the one or more objects, determine, based on a comparison of the set of predicted behavior data and the set of observed behavior data, whether a set of incorrect object behavior predictions exists within the set of predicted behavior data, and upon determining that the set of incorrect object behavior predictions exists, initiate, based on at least a generated subset of the set of incorrect object behavior predictions, one or more operations to address the set of incorrect object behavior predictions.

Abstract: Systems and methods of unmanned vehicles having self-calibrating sensors and actuators are provided. The unmanned vehicle comprises a communication interface and a processor for controlling a propulsion system of the vehicle and receiving sensor data from one or more sensors of the vehicle. The processor is configured to operate in a guided calibration mode by controlling the propulsion system according to commands received from an external guided control system, while processing the sensor data to determine a degree of certainty on a calibration the sensor data and a position of the vehicle. The processor determines that the degree of certainty is above a threshold value associated with safe operation of the propulsion system in an autonomous calibration mode, and subsequently switch operation of the propulsion system to the autonomous calibration mode based on the determination that the degree of certainty is above the threshold value.

Abstract: A coordinate conversion system including a controller that converts geographic-coordinate-system coordinates of a certain point into site-coordinate-system coordinates includes: an image-capturing device that captures an image of a reference point; and a GNSS antenna that receives a navigation signal.

Abstract: A method, a computer program, and an apparatus for controlling operation of a transportation vehicle equipped with an automated driving function and a transportation vehicle equipped with an automated driving function which uses the method or apparatus. A first signal state of a traffic light is determined from data of at least one vehicle sensor, the determined first signal state of the traffic light is then validated based on information available in the transportation vehicle, and an operation mode of the transportation vehicle is set as a function of a validation result.

Abstract: A vehicle traveling controller according to the present disclosure performs automatic steering so that a vehicle travels along a target path. The vehicle traveling controller allows a driver to intervene in steering. When the driver intervenes in steering and thereby a traveling position of the vehicle deviates outside from a threshold line set apart from the target path in a lane width direction, the vehicle traveling controller increases a steering reaction force acting on steering operation by the driver.

Abstract: The subject disclosure relates to ways to determine a data collection surveillance cadence. In some aspects, a process of the disclosed technology includes steps for receiving historic map data for one or more geographic regions, wherein each of the one or more geographic regions comprises one or more map features, calculating a change rate for each of the one or more map features, and determining a surveillance cadence for each of the one or more geographic regions based on the change rate for each of the one or more map features. Systems and machine-readable media are also provided.

Abstract: Systems and methods for determining a current state, of at least one traffic light, for use in controlling an autonomous vehicle are described herein. Implementations determine, based on a pose instance of the autonomous vehicle and a stored mapping of an environment of the autonomous vehicle, a region of the environment includes the traffic light and a configuration that is assigned to the traffic light. Further, those implementations process an image capturing the region, using a machine learning classifier, to generate predicted output associated with multiple candidate states of the traffic light, and determine a current state of the traffic light based on the predicted output. Processing the image using the machine learning classifier can be based on the configuration of the traffic light.

Abstract: A method for evaluating sensor data. The sensor data are ascertained by scanning a surrounding area, using at least one sensor. On the basis of the sensor data, object detection is carried out for determining objects from the sensor data. Object filtering is carried out. Surface characteristics of at least one object are identified, and/or the surface characteristics of at least one object are ascertained with the aid of access to a database. A control unit is also described.

Abstract: Disclosed are techniques for fusing camera and radar frames to perform object detection in one or more spatial domains. In an aspect, an on-board computer of a host vehicle receives, from a camera sensor of the host vehicle, a plurality of camera frames, receives, from a radar sensor of the host vehicle, a plurality of radar frames, performs a camera feature extraction process on a first camera frame of the plurality of camera frames to generate a first camera feature map, performs a radar feature extraction process on a first radar frame of the plurality of radar frames to generate a first radar feature map, converts the first camera feature map and/or the first radar feature map to a common spatial domain, and concatenates the first radar feature map and the first camera feature map to generate a first concatenated feature map in the common spatial domain.

Abstract: An autonomous vehicle is described herein. The autonomous vehicle is configured to determine that a person is proximate the autonomous vehicle at a location that is external to the autonomous vehicle. The autonomous vehicle is further configured to determine that the person is attempting to hail the autonomous vehicle through use of a gesture. The autonomous vehicle identifies the gesture being performed by the person, and responsive to identifying the gesture, identifies a travel intent that the person is imparting to the autonomous vehicle by way of the gesture. The autonomous vehicle then operates based upon the travel intent.

Abstract: In general, an indication is received through a user interface of an intention of a potential rider to use an autonomous vehicle. In response to the receipt of the indication, a hailing request is sent by a signaling mode to at least one autonomous vehicle that can receive the hailing request directly in accordance with the signaling mode.

Abstract: A deep neural network provides real-time pose estimation by combining two custom deep neural networks, a location classifier and an ID classifier, with a pose estimation algorithm to achieve a 6D0F location of a fiducial marker. The locations may be further refined into subpixel coordinates using another deep neural network. The networks may be trained using a combination of auto-labeled videos of the target marker, synthetic subpixel corner data, and/or extreme data augmentation. The deep neural network provides improved pose estimations particularly in challenging low-light, high-motion, and/or high-blur scenarios.

Abstract: Systems and methods are provided for modulating headlight characteristics according to a calculated probability of future unplanned disengagement of advanced driver-assistance systems or automated driving systems, collectively referred to as driving automation systems. In particular, some embodiments aim to alert drivers of both a likelihood of unplanned disengagement, as well as a source of unplanned disengagement.

Abstract: In one aspect, a system for controlling the operation of an agricultural implement may include a ground-engaging tool configured to engage soil within a field such that the tool creates a field material cloud aft of the tool as the implement is moved across the field. Furthermore, the system may include an imaging device configured to capture image data associated with the field material cloud created by the ground-engaging tool. Moreover, a controller of the disclosed system may be configured to identify a plurality of field material units within the field material cloud based on the image data captured by the imaging device. Additionally, the controller may be configured to determine a characteristic associated with the identified plurality of field material units.

Abstract: A method for positioning a mobile object in an underground tunnel includes the steps of determining horizontal progression of a mobile object in an underground tunnel from a preceding position estimate or an initial position of the mobile object; determining horizontal position of the mobile object on the basis of a floor model of the tunnel and the estimated horizontal progression of the mobile object; and generating a three-dimensional position indicator on the basis of the horizontal position of the mobile object and a three-dimensional model of the tunnel.

Abstract: Aspects of the disclosure relate generally to detecting and avoiding blind spots of other vehicles when maneuvering an autonomous vehicle. Blind spots may include both areas adjacent to another vehicle in which the driver of that vehicle would be unable to identify another object as well as areas that a second driver in a second vehicle may be uncomfortable driving. In one example, a computer of the autonomous vehicle may identify objects that may be relevant for blind spot detecting and may determine the blind spots for these other vehicles. The computer may predict the future locations of the autonomous vehicle and the identified vehicles to determine whether the autonomous vehicle would drive in any of the determined blind spots. If so, the autonomous driving system may adjust its speed to avoid or limit the autonomous vehicle's time in any of the blind spots.

Abstract: A domestic robotic system includes a moveable robot having an image obtaining device for obtaining images of the exterior environment of the robot, and a processor programmed to detect a predetermined pattern within the obtained images. The processor and image obtaining device form at least part of a first navigation system for the robot which can determine a first estimate of at least one of the position and orientation of the robot. A second navigation system for the robot determines an alternative estimate of the at least one of the position and orientation of the robot. Calibration of the second navigation system can be performed using the first navigation system.

Abstract: Embodiments of the present disclosure provide a method, apparatus, device, and storage medium for determining a lane where a vehicle is located. In the method, the image lane information of the position where the vehicle is located is determined from a lane line image; actual lane information of a position where the vehicle is located is acquired from existing lane information according to the positioning information of the vehicle; and an actual lane where the vehicle is located is determined based on the image lane information and the actual lane information.

Abstract: A device may determine, based on image data of an environment that includes a ground surface, depth information indicating distances from a vehicle to different areas of the ground surface. The device may generate, based on the depth information, an overlay to indicate a range of distances from the vehicle. The range of distances may be associated with an area. The device may provide, for display, a video feed including the image data with the overlay on the ground surface. The overlay may provide the perception of depth, with respect to the area, during an operation of the vehicle.

Abstract: A method includes: receiving data identifying, for each of one or more objects, a respective target location to which a robotic agent interacting with a real-world environment should move the object; causing the robotic agent to move the one or more objects to the one or more target locations by repeatedly performing the following: receiving a current image of a current state of the real-world environment; determining, from the current image, a next sequence of actions to be performed by the robotic agent using a next image prediction neural network that predicts future images based on a current action and an action to be performed by the robotic agent; and directing the robotic agent to perform the next sequence of actions.

Abstract: Techniques for determining a safety margin by which to limit trajectory(ies) generated by a vehicle control system such that the vehicle will not exceed the safety margin more than a target occurrence rate. The techniques may include determining a first spectrum associated with trajectory data generated by one or more vehicles, generating a model of a vehicle, and determining a spectrum of an error signal based at least in part on the model and the first spectrum. Determining the safety margin may be based at least in part on the spectrum of the error signal and a target occurrence rate. Operation characteristics of components of the vehicle (e.g., controller, steering actuator) may be tuned based at least in part on the model, first spectrum, and/or second spectrum. The techniques enable determining safety margins for untested vehicles and/or for different operating states of a vehicle.

Abstract: A system for controlling operation of a vehicle during a turning maneuver includes an object detection sensor, a vehicle turning angle sensor, and a processor. A memory is communicably coupled to the processor and stores a turning alert zone module configured to determine if a speed of the vehicle is within a predetermined range of speeds and, if the speed of the vehicle is within the predetermined range of speeds, determine if a vehicle turning angle is above a predetermined threshold value. If the turning angle is above the predetermined threshold value, and using the turning angle, a turning alert zone is determined. The system may then determine if at least a portion of an object resides in the turning alert zone and, if at least a portion of an object resides in the turning alert zone, control an operation of the vehicle (for example, a braking operation).

Abstract: Terrain characteristics of an off-road area are determined based on a map. The terrain characteristics include a terrain type, a terrain grade, and a presence or an absence of an obstacle. Vehicle characteristics are determined including a ground clearance and a breakover angle. Based on a user level, vehicle parameters for the off-road area are determined based on the terrain characteristics, the vehicle characteristics, and the user input. The vehicle parameters include a speed and a transmission gear. The vehicle parameters for the off-road area are output.

Abstract: Provided are a method of controlling a mobile robot, apparatus for supporting the method, and delivery system using the mobile robot. The method, which is performed by a control apparatus, comprises acquiring a first control value for the mobile robot, which is input through a remote control apparatus, acquiring a second control value for the mobile robot, which is generated by an autonomous driving module, determining a weight for each control value based on a delay between the mobile robot and the remote control apparatus and generating a target control value of the mobile robot in combination of the first control value and the second control value based on the determined weights, wherein a first weight for the first control value and a second weight for the second control value are inversely proportional to each other.

Abstract: An image processing device applied to a driving assistance system, which includes: a driving assistance device that detects a relative position of a lane marking with respect to a vehicle and assists a driving of the vehicle based on a detected positional information; and a head-up display device that projects a display image on a projection area arranged on the vehicle to visually recognize a virtual image of the display image, includes: an acquisition device that acquires the positional information; and a generation device that generates the display image including a predetermined display element. The generation device generates the display image to visually recognize the display element at a position associated with the positional information and to visually recognize the display element as a shape inclined toward the vehicle from the lane marking.

Abstract: A controller is provided to control movement of transporting devices. Embodiments limit the loads imparted on a grid of pathways structure of transporting devices to prevent non-safety-critical damage from excess loads and/or fatigue. A controller is arranged to control movement of transporting devices arranged to transport containers stored in a facility. The facility includes pathways arranged in cells to form a grid-like structure which extends in first and second directions. A route determination unit determines a route from one location to another, and a clearance unit to provide clearance for each transporting device to traverse a portion of the determined route. A constraint area determination unit determines constraint areas based on the grid-like structure and a calculation unit calculates a constraint limit in each constraint area.

Abstract: Systems, devices and methods provide, implement, and use vision-based methods of sequence inference for a device affixed to a vehicle.