Abstract:
An on-board computing system for determining an opportune time to log data into a first memory. A sensor system collects data of a vehicle&#39;s environment. A controller of the on-board computing system logs the data to a first memory when it determines an opportune time to log data to the first memory. The controller holds data in a second memory if it determines it is not an opportune time to log data into the first memory. The controller resumes logging data to the first memory when an opportune time presents itself.

Description:
BACKGROUND 
     The present disclosure relates to systems, components, and methodologies for logging data through sensors in a vehicle. More particularly, the present disclosure relates to systems, components, and methodologies that improve logging data through sensors in a vehicle in situations such as a high traffic density environment. 
     SUMMARY 
     According to the present disclosure, systems, components, and methodologies are provided for logging data in an intelligent manner to reduce the computational load on an on-board computing system. 
     In illustrative embodiments, an on-board computing system of an autonomous vehicle assesses the criticality of a situation before determining whether or not to log data to a first memory or hold data in a second memory until a more opportune time presents itself to log data to a first memory. This is because logging data to a first memory that may be embodied as a hard drive is orders of magnitude slower than holding data in a second memory that may be embodied as RAM. The second memory in this case can be accessed hundreds of times faster than the first memory. Therefore, in potential critical situations when a faster cycle of processing is needed, the second memory holds the data in order to speed up the process of data logging during that duration in order to address the potential critical situation. 
     The assessment of criticality of the situation may be based upon a determination if a number of objects in the near vicinity of the autonomous vehicle exceed a predetermined threshold value. The assessment of criticality of the situation may also be based upon a determination if a predicted time to collision of the autonomous vehicle with another object falls below a predetermined threshold value. If there is a determined potential critical situation, the on-board computing system holds data in the second memory to reduce the computational load and allow the on-board computing system to react faster to a potential critical situation. 
     Additional features of the present disclosure will become apparent to those skilled in the art upon consideration of illustrative embodiments exemplifying the best mode of carrying out the disclosure as presently perceived. 
    
    
     
       BRIEF DESCRIPTIONS OF THE DRAWINGS 
       The detailed description particularly refers to the accompanying figures in which: 
         FIG. 1  depicts an autonomous vehicle having an autonomous driving system in accordance with the present disclosure, and shows that the autonomous driving system includes cameras that collect image data that can be used to assess the criticality of the situation; 
         FIG. 2  is a diagrammatic view of an on-board computer system in accordance with the present disclosure that includes one or more data collectors configured to collect data related to vehicle environment, one or more modules representing the various systems of the vehicle, a controller to process the data collected and log the data into first memory after an assessment of the criticality of the situation; 
         FIG. 3  depicts a stretch of highway with a group of secondary vehicles in a traffic jam wherein the autonomous vehicle is entering the highway and analyzing the secondary vehicles to assess the criticality of the situation in order to determine whether or not to log data of the vehicle environment in accordance with the present disclosure; 
         FIG. 4  is a detail view of  FIG. 3  taken from the perspective of the autonomous vehicle which shows one of the secondary vehicles from the group of secondary vehicles, wherein identifying features including light emissions, thermal emissions, audio emissions, radio emissions, and other identifiers are collected by the primary vehicle to log data of the vehicle environment and analyzed to determine the distance, relative velocity, relative acceleration, and predicted time to collision with the secondary vehicle in order to assess the criticality of the situation in accordance with the present disclosure; 
         FIG. 5  is a partial perspective view of an interior of the autonomous vehicle which depicts a navigation screen displaying the present location of the autonomous vehicle, wherein the navigation screen is displaying messages to indicate to a user of the autonomous vehicle that a traffic jam is ahead and as a result may halt logging of data to memory until an assessment of the criticality of the situation is resolved in accordance with the present disclosure; 
         FIG. 6  depicts a stretch of residential roadway with a group of secondary vehicles parked along a side of the roadway, wherein the autonomous vehicle is driving along the roadway to park along the side of the roadway and analyzing the secondary vehicles to assess the criticality of the situation in order to determine whether or not to log data of the vehicle environment in accordance with the present disclosure; 
         FIG. 7  is a detail view of  FIG. 6  taken from the perspective of the autonomous vehicle which shows one of the secondary vehicles from the group of secondary vehicles, wherein identifying features regarding the secondary vehicle&#39;s status including light emissions, thermal emissions, audio emissions, radio emissions, and other identifiers are collected by the autonomous vehicle to determine a status of the secondary vehicle to log data of the vehicle environment and analyzed to determine the distance, relative velocity, relative acceleration, and predicted time to collision with the secondary vehicle in order to assess the criticality of the situation in accordance with the present disclosure; 
         FIG. 8  is a partial perspective view of the interior of the autonomous vehicle which depict the navigation screen displaying the present location of the autonomous vehicle, wherein the navigation screen is displaying messages to indicate to a user of the autonomous vehicle that pedestrians may be present in the area and as a result may halt logging of data to memory until an assessment of the criticality of the situation is resolved in accordance with the present disclosure; 
         FIG. 9A-9C  depicts different scenarios where the assessment of the criticality of the situation may affect the logging of data of the vehicle environment; 
         FIG. 9A  depicts the autonomous vehicle assessing the criticality of the situation and determining whether or not to log data of the vehicle environment based upon an assessment of the time to collision to a neighboring vehicle; 
         FIG. 9B  depicts the autonomous vehicle assessing the criticality of the situation and determining whether or not to log data of the vehicle environment based upon an assessment of the potential time to collision to a neighboring vehicle; 
         FIG. 9C  depicts the autonomous vehicle assessing the criticality of the situation and determining whether or not to log data of the vehicle environment based upon an assessment of the number of objects in the near vicinity; and 
         FIG. 10  is a flow diagram illustrating a methodology for assessing criticality of the situation before logging data into memory. 
     
    
    
     DETAILED DESCRIPTION 
     The figures and descriptions provided herein may have been simplified to illustrate aspects that are relevant for a clear understanding of the herein described devices, systems, and methods, while eliminating, for the purpose of clarity, other aspects that may be found in typical devices, systems, and methods. Those of ordinary skill may recognize that other elements and/or operations may be desirable and/or necessary to implement the devices, systems, and methods described herein. Because such elements and operations are well known in the art, and because they do not facilitate a better understanding of the present disclosure, a discussion of such elements and operations may not be provided herein. However, the present disclosure is deemed to inherently include all such elements, variations, and modifications to the described aspects that would be known to those of ordinary skill in the art. 
       FIG. 1  depicts an autonomous vehicle  10  driving on a roadway  104  having four lanes  104   a ,  104   b ,  104   c , and  104   d . Several neighboring vehicles  106 ,  108 ,  110 , and  112  are driving in proximity of the autonomous vehicle  10 , wherein neighboring vehicle  112  carries an exposed cargo  114 . 
     The autonomous vehicle  10  may include an on-board computing system  200  (depicted in  FIG. 2  and to be described in more detail below). The on-board computing system  200  may include a front camera  212  and a rear camera  214  that may capture image data  12  of the proximity of the autonomous vehicle  10 . Thus, for example, the front camera  212  may capture image data  12  of the neighboring vehicles  110  and  112 , both of which may be located generally forward of the vehicle  10 . Similarly, the rear camera  214  may capture image data  12  of the neighboring vehicles  106  and  108 , both of which may be located generally rearward of the vehicle  10 . 
     The on-board computing system  200  may use image data  12  of the neighboring vehicles  106 ,  108 ,  110 , and  112  to develop an assessment of criticality of a given situation to determine if is an opportune time to log data  12  into a first memory  208  or hold data  12  in a second memory  206 . The assessment of criticality of the situation may include identifying objects in the near vicinity of the autonomous vehicle  10  to see if the number of objects exceeds a predetermined threshold value. The assessment of criticality of the situation may also include evaluating a time to collision with a neighboring vehicle  106 ,  108 ,  110 , or  112  to determine if the time to collision falls below a predetermined threshold value. In the illustrative embodiment, if it is deemed not to be an opportune time to log data  12  into a first memory  208  based on the assessment of criticality because the predetermined threshold value was exceeded, then second memory  206  may hold data  12  until an opportune time presents itself, i.e., the threshold is not exceeded. The on-board computing system  200  may also compare the amount of space on second memory  206  and the assessment of criticality of the situation to determine if it is necessary to log data  12  into first memory  208 . 
     As a result, the on-board computing system  200  may provide improved safety and efficiency. With respect to safety, the on-board computing system  200  enables the autonomous vehicle  10  to log data  12  in a situation where it is less likely to collide with a neighboring vehicle  106 ,  108 ,  110 , or  112 . With respect to efficiency, the on-board computer system  200  logs data  12  in situations that are less computationally intensive on the processor  204  to process the data  12  and store the data  12  into first memory  208 . 
       FIG. 2  is a diagrammatical view of the illustratively embodied on-board computing system  200  in accordance with the present disclosure. The on-board computing system  200  may include the controller  202  having a processor  204 , first memory  208 , and a second memory  206 . In accordance with a main embodiment, the on-board computing system  200  may also include a sensor system  210  that includes the previously described cameras  212 ,  214 , Lidar  216 , radar  218 , and other sensors  220 . The illustratively embodied on-board computing system  200  may also include a steering and acceleration/braking system  222  and a human machine interface  224 , side mirror adjustment system with proximity detectors, a headlight control system with proximity detectors, a window control system with proximity detectors, an information and entertainment system with proximity detectors, a climate control system with proximity detectors, a gear and power train adjustment system with proximity detectors, an audio control system, and a multifunction display control system. Lidar technology collects data using remote sensing technology to measure distance by illuminating a target with a laser and analyzing the reflected light. 
     In other, additional and/or optional embodiments, the other sensors may include, for example, microphones, air and particulate detector, etc. 
     In accordance with disclosed embodiments, controller  202  may be electrically coupled to the sensor system  210  and the electrical systems  220 ,  222 ,  224 ,  226 ,  228 ,  230 ,  232 , and  234 . The electrical connections can be made using any mechanism known in the art, such as a communication bus. 
     The illustratively embodied on-board computing system  200  may use the controller  202  to process the electrical systems  222  and  224  and the sensor system  210  to send data  12  to the controller  202  to log into first memory  208  or hold data  12  in second memory  206  until the processor  204  can log data  12  in first memory  208 . 
       FIG. 3  depicts a highway  300  having an on-ramp  302  connecting with lanes  306  of highway  300 . An autonomous vehicle  10  entering highway  300  from an on-ramp  302  may capture data  12  regarding secondary vehicles  304  on highway  300 . The data  12  may be used to assess the criticality of the situation to determine if it is an opportune time to log the data  12  into first memory  208  or hold the data  12  in second memory  206  until an opportune time presents itself. For example, if the amount of secondary vehicles  304  exceeds a predetermined threshold value, then the autonomous vehicle  10  may stop logging data  12  regarding its environment. 
     In the illustrative embodiment of  FIG. 3 , data  12  may include information regarding thermal emissions  311 ,  312 , light emissions  313 ,  314 , audio emissions  315 , and radio emissions  316 ,  317  from secondary vehicle  304 , as illustrated in  FIG. 4 . These emissions  311 - 317  may be logged by the autonomous vehicle  10  to analyze parameters indicative to activity in a vehicle environment (e.g. the surrounding thermal data, the surrounding light data, the surrounding audio data, etc.). In some embodiments, data  12  regarding multiple secondary vehicles  304  in a group of adjacent secondary vehicles  304  may be captured to analyze the parameters indicative of the activity in the vehicle environment (e.g. the surrounding thermal data, the surrounding light data, the surrounding audio data, etc.). The data  12  may be used to determine the distance, relative velocity, relative acceleration, and predicted time to collision with the secondary vehicles  304  in order to assess the criticality of the situation to determine if it is an opportune time to log data  12  into the first memory  208  or hold data  12  in the second memory  206  until an opportune time presents itself as described in the disclosure. 
     In the illustrative embodiment, a notification  16  of traffic ahead may be displayed to a user of the autonomous vehicle  10  if the probability that adjacent secondary vehicles  304  are in a traffic jam, as determined by the on-board computing system  200 , reaches or exceeds a predetermined threshold limit as shown in  FIG. 5 . In some embodiments, a prompt  18  may be displayed to the user to activate an autonomous driving function of vehicle  10  which may operate when vehicles  10 ,  304  are in a traffic jam. In some embodiments, identification of a traffic jam may prompt autonomous vehicle  10  to send location data  12  to a server for mapping traffic patterns. Other uses for identification of traffic jams are also contemplated. 
       FIG. 6  depicts a residential roadway  400  having lanes  406 . Autonomous vehicle  10  driving on roadway  400  may capture data  12  regarding secondary vehicles  404  positioned alongside a curb  402  of right-side lane  406 . An opening  408  for autonomous vehicle  10  to park in may also be identified as part of an auto-park function of autonomous vehicle  10  if it is determined that secondary vehicles  404  are also parked. The data  12  may be used to determine the distance, relative velocity, relative acceleration, and predicted time to collision with the secondary vehicles  404  in order to assess the criticality of the situation to determine if it is an opportune time to log the data  12  into first memory  208  or hold the data  12  in second memory  206  until an opportune time presents itself. For example, if the amount of secondary vehicles  404  exceeds a predetermined threshold value then the autonomous vehicle  10  may stop logging data  12  of its environment. However, in an illustrative embodiment, the on-board computing system  200  may determine that it is safe to log data  12  in first memory  208  if there is a low potential for a collision as a result of the secondary vehicle  404 . 
     As shown in  FIG. 7 , data  12  captured by autonomous vehicle  10  may indicate that secondary vehicle  404  lacks any active-status indicators, making the probability unlikely that secondary vehicles  404  are a possible object to collide with. As a result, the on-board computing system  200  may log data  12  to first memory  208  after assessing the criticality of the situation. Location data  12  of autonomous vehicle  10  may indicate that the autonomous vehicle  10  is travelling on residential roadway  400 , as depicted in  FIG. 8 , which may confirm that the probability of secondary vehicles  404  being part of a traffic jam is low and more likely that the secondary vehicles  404  are actually parked. 
     In the illustrative embodiment, a notification  17  that pedestrians may be present may be displayed to a user of autonomous vehicle  10  if it is determined by the on-board computing system  200  that secondary vehicles  404  are parked on a residential or other non-controlled access roadway as shown in  FIG. 8 . A detection that a number of pedestrians may be present may cause the on-board computing system  200  to stop logging data  12  in first memory  208  and hold data in second memory  206  as a result of the number of objects in the vicinity of the autonomous vehicle exceeding a predetermined threshold value. In some embodiments, a prompt  19  may be displayed to the user to activate the auto-park function of vehicle  10  to guide autonomous vehicle  10  into opening  408 . In some embodiments, access to the autonomous driving function of autonomous vehicle  10  may be blocked or prohibited if it is determined that autonomous vehicle  10  is on a residential or other non-controlled access roadway. 
     In an illustrative embodiment, the autonomous driving function of a primary vehicle may use Lidar or radar based cruise control to maintain spacing from other secondary vehicles on the roadway. However, such a Lidar or radar based system may not be able to distinguish objects smaller than a vehicle, such as a pedestrian or bicycle user. In such an embodiment, access to the autonomous driving function may be blocked, or prohibited, if the primary vehicle is on a non-controlled access roadway where pedestrians are likely to be present. 
     The on-board computing system  200  may include certain components for detecting and analyzing characteristics of secondary vehicles  304 ,  404 . A sensor system  210  may be provided on autonomous vehicle  10  and configured to capture data  12  including emissions  311 - 317  of secondary vehicles  304 ,  404 . In an illustrative embodiment, sensor system  210  may include the cameras  212 ,  214  for obtaining image data  12  regarding light emissions  313 ,  314  and image data  12  regarding thermal emissions  311 ,  312 , such as through infrared signals for example, and a radio receiver for obtaining signal data  12  regarding radio emissions  316 ,  317  coming from secondary vehicles  304 ,  404 . Sensor system  210  may be coupled to autonomous vehicle  10  in an area where a wide range of views are visible, such as, for example, a bumper, hood, roof, side mirror, rear-view mirror, front fascia, or dashboard  13  of autonomous vehicle  10 , etc. 
     In some embodiments that include optional sensors, audio emissions  315  include passenger voices, such as indicated at  315  in  FIG. 4 , sounds from an entertainment system, engine noise, and braking noise, to name a few. In some embodiments, radio emissions  316 ,  317  include distance sensor signals, ultrasonic parking signals, blind spot radar, and adaptive cruise control radar such as indicated at  317 , wi-fi signals, BLUETOOTH™ signals, cellphone signals, and entertainment system signals, such as indicated at  316 , to name a few. In some embodiments, light emissions  313 ,  314  may include tail or brake light emissions, such as indicated at  313 , headlamp or turn signal emissions, such as indicated at  314 , and internal cabin light emissions, etc. In some embodiments, thermal emissions  311 ,  312  may include brake heat emissions, such as indicated at  311 , engine heat emissions, such as indicated at  312 , cabin heat emissions, and exhaust heat emissions, etc. 
     Moreover, in accordance with such embodiments, a microphone may be used to detect audio emissions  315  from particular neighboring vehicles. Typical driving patterns of secondary vehicles  304  or  404  may be informed by audio emissions  315 . For example, if a neighboring vehicle  304  or  404  may be emitting sounds suggesting engine trouble, the on-board computing system  200  may determine that the neighboring vehicle  304  or  404  could suddenly decelerate or pull over. If a neighboring vehicle may be emitting sounds suggesting loud music, the on-board computing system  200  may determine that a driver of the secondary vehicle  304  or  404  may be distracted and that the secondary vehicle  304  or  404  may drive erratically. The on-board computing system  200  may use the audio emissions  315  to assess the criticality of the situation and determine that a collision may occur with a secondary vehicle  304  or  404 . If the on-board computing system  200  determines that the time to collision falls below a predetermined threshold then the on-board computing system  200  may stop logging data  12  to first memory  208  and hold the data  12  in second memory  206  until a more opportune to log data  12  presents itself. 
     In accordance with embodiments including such optional sensors, an air and particulate detector may be used to measure air composition through, for example, olfactory analysis, akin to how a human may smell odors in the air representing impurities. If it is determined that a particular secondary vehicle  304  or  404  may be emitting excessive exhaust, the on-board computing system  200  may avoid that neighboring vehicle  304  or  404 . The air and particulate detector may be of any type suitable for performing chemical or olfactory analysis to detect impurities typically present in air on roadways. The on-board computing system  200  may use the data  12  collected from the air and particulate detector to avoid needing to collect more data regarding excessive exhaust from a secondary vehicle  304  or  404 . Therefore, the computational load of the on-board computing system  200  could be reduced. 
     The on-board computing system  200  may use the controller  202  to pre-process signals generated by the sensor system  210 . For example, the controller  202  may apply filters to signals transmitted by the sensor system  210  to remove noise and isolate meaningful data using signal processing techniques. This could reduce the computational load of logging data  12  to first memory  208  when the on-board computing system  200  deems it an opportune time to log data  12 . 
       FIGS. 9A-9C  depict an autonomous vehicle in different driving scenarios.  FIG. 9A  depicts the autonomous vehicle  10  following a neighboring vehicle  902  at a following distance  904  to the neighboring vehicle  902 . The following distance  904  may be used to assess the criticality of the situation and determine if it is an opportune time to log data  12  to first memory  208 . The following distance may be used in an evaluation in a potential time to collision assessment. If the on-board computing system  200  determines that the time to collision falls below a predetermined threshold amount then the on-board computing system  200  may stop logging data  12  to first memory  208 . If it is determined that a collision is eminent then the autonomous vehicle  10  may continue to log data  12  to first memory  208 . 
       FIG. 9B  depicts the autonomous vehicle  10  on a roadway  901  in a middle lane  901   b , and suggests that the autonomous vehicle may switch to the left lane  901   a  or the right lane  901   c . There are also neighboring vehicles  908  and  910  in lanes  901   c  and  901   a . The assessment of the criticality of the situation of the autonomous vehicle may determine that a time to collision falls below a predetermined threshold value when switching to either lanes  901   a ,  901   c . In addition, neighboring vehicle  908  or  910  may be driving aggressively and may accelerate and decelerate quickly. The on-board computing system  200  may determine that a potential time to collision may fall below a threshold value because of the aggressive driving nature or a neighboring vehicle  908 ,  910 . As a result of the time to collision falling below a threshold value, the autonomous vehicle  10  may stop logging data  12  to first memory  208  and hold the data  12  in second memory  206  until a more opportune time presents itself. 
       FIG. 9C  depicts the autonomous vehicle  10  at an intersection  912 , and suggests that the autonomous vehicle  10  is attempting to execute a left turn. The on-board computing system  200  may assess the criticality of the situation by analyzing all of the objects at the intersection and determine that the number of objects in the nearby vicinity exceeds a predetermined threshold value. In addition, pedestrians may be detected at the intersection  912 . The pedestrians may not obey traffic laws and as a result cause the autonomous vehicle  10  to react in a quick manner to avoid colliding with the pedestrians. Furthermore, the neighboring vehicles  914 ,  916 ,  918 , and  920  may drive in an aggressive manner. This may cause the on-board computing system  200  to assess the criticality of the situation and determine the time to collision falls below a predetermined threshold value. A vehicle  918  may be carrying exposed cargo  922 , and the autonomous vehicle  10  may predict that some of the exposed cargo  922  may fall off of the vehicle  918  and be a hazard to avoid. As a result of these scenarios, the on-board computing system  200  may stop logging data  12  to first memory  208  and hold data  12  in second memory  206  until a more opportune time presents itself. 
       FIG. 10  is a flow diagram  1000  illustrating a methodology for operation of an intelligent logging system in accordance with the present disclosures. The illustrative methodology begins with operation  1005 , in which a controller  202  of the on-board computing system  200  queries sensors  212 ,  214 ,  216 ,  218  and  220  of the sensor system  210 . After receiving sensor data  12 , controller  202  proceeds with operation  1010  and processes sensor data  12 . In operation  1015 , the controller  202  plans actions in response to the sensor data  12 . In this illustrative embodiment, these actions may be related to the functions of the autonomous vehicle  10 . In operation  1020 , the on-board computing system  200  may determine if there is a critical situation in which a predetermined threshold value has been exceeded. The assessment of the criticality of a situation may be based upon the number of objects in the vicinity of an autonomous vehicle  10  or a time to collision as described above. If there is a critical situation as a result of a predetermined threshold value being exceeded then operation may continue to operation  1025  and data  12  may be held in second memory  206 . If not, operation  1030  may be executed and data  12  may be logged into first memory  208 . 
     In operation  1025 , there may be an evaluation to see if the data  12  held in second memory  206  is reaching a critical amount and preventing the controller  202  from processing functions for other electrical systems. The sensor data  12  may be held in second memory  206  if it is determined that storage in second memory  206  has not reached a critical amount and operation returns to operation  1005  to restart the process. The frequency of the processing may be increased to improve the reaction time of the autonomous vehicle  10  to potential critical situations. Until operation proceeds to  1030 , data may be held in second memory  206 . If the controller  202  determines that the data  12  stored on second memory  206  has exceeded a determined threshold value then operations may proceed to operation  1030 , and the on-board computing system  200  may start logging data  12  to first memory  208  to reduce the computational load on the on-board computing system  200 . After data  12  is logged to first memory  208 , operation  1035  may be executed and the on-board computing system  200  may wait out the rest of the loop and then return to operation  1005  to restart the process. 
     The technical problem that arises when logging data  12  into an autonomous vehicle  10  is the gathering of sensor data  12  becomes computationally intensive. The sensor data  12  is important for system debugging and development. As such, the excessive data  12  logging can place a large burden on the on-board computing system  200  and negatively impact performance because file I/O is performance-intensive for computers. It may even prevent the autonomous vehicle  10  from operating in certain complex scenarios, where massive data  12  gathering is required to properly develop and debug the system in those situations. 
     Certain conventional solutions to this problem have been to record less data  12  (downsampling or simply leaving out data  12 ). Other conventional solutions to the problem are to invest in additional computing power dedicated for logging data  12 . 
     To the contrary, the proposed logging innovation approaches the problem in such a way as to provide an improved technical solution by maintaining the amount of data  12  logged using fewer computational resources. Sensor data  12  is held in second memory  206  until an assessment of the criticality of the situation determines that it is an opportune time to log data  12  into the first memory  208 . The on-board computing system  200  may assess the calculated performance impact of logging data  12  to the first memory  208  and use that to determine if it is an opportune time to log data  12  to the first memory  208  or hold data  12  in the second memory  206 . In critical situations (e.g. lots of surrounding cars, cyclists, pedestrians) where faster cycle times are required, the system may choose to avoid logging data  12  to the first memory  208  until an opportune time presents itself. The described logging data  12  approach reduces the computational resources needed while maintaining the level of data  12  logged for system debugging and development. 
     Although certain embodiments have been described and illustrated in exemplary forms with a certain degree of particularity, it is noted that the description and illustrations have been made by way of example only. Numerous changes in the details of construction, combination, and arrangement of parts and operations may be made. Accordingly, such changes are intended to be included within the scope of the disclosure, the protected scope of which is defined by the claims.