Patent Publication Number: US-2023150478-A1

Title: System for identifying road type

Description:
TECHNICAL FIELD 
     The present disclosure generally relates to a vehicle system for identifying road type. More specifically, the present disclosure relates to a vehicle system to identify road type and driving condition using vehicle sensor input. 
     BACKGROUND 
     Vehicles may operate on a variety of roadways (e.g. highway, city, residential) having different driving conditions. Tailoring the vehicle power configurations to the appropriate roadway type may improve fuel economy and driver experience. For instance, a hybrid electric vehicle may be configured to more actively pull down the internal combustion engine and rely on the electric machine for propulsion in the city where stop and go traffic provides more opportunity for regenerative braking, and keep the engine running on the highway to provide better performance. 
     SUMMARY 
     In one or more illustrative example of the present disclosure, a vehicle includes an engine; an electric machine; a transceiver, programmed to communicate with a server; and a controller, programmed to responsive to identifying a route, obtain road data associated with the route via the transceiver, wherein the road data include locations of stop signs and traffic lights, and identify a sign classification using a sign and traffic light density calculated using a total number of stop signs and traffic lights within a lookahead distance on the route, identify a road type classification using the sign classification, responsive to determining the sign and traffic light density within the lookahead distance is above a threshold indicative of a city road type classification, adjust a drivetrain of the vehicle to reduce engine operation and increasingly propel the vehicle via the electric machine. 
     In one or more illustrative example of the present disclosure, a method for a vehicle includes responsive to identifying a route, obtaining road data associated with the route via a transceiver, wherein the road data include locations of traffic controls; and identify a traffic control classification using a traffic control density calculated using a total number of traffic controls within a lookahead distance on the route; identifying a road type classification using the traffic control classification; and adjusting a vehicle powertrain setting using the road type classification. 
     In one or more illustrative example of the present disclosure, a system for a vehicle includes a transceiver, programmed to communicate with a server; and a controller, programmed to responsive to identifying a route, obtain road data associated with the route via the transceiver, wherein the road data include locations of stop signs and traffic lights and speed limit information within a lookahead distance on of the route, and identify a sign classification using a sign and traffic light density calculated using a total number of stop signs and traffic lights within the lookahead distance on the route, identify a speed classification using the speed limit information, identify a road type classification using the sign classification and the speed classification, and adjust a vehicle powertrain setting using the road type classification. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a better understanding of the invention and to show how it may be performed, embodiments thereof will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which: 
         FIG.  1    illustrates an example block topology of a vehicle system of one embodiment of the present disclosure; 
         FIG.  2    illustrates an example block diagram for a road type module of one embodiment of the present disclosure; and 
         FIG.  3    illustrates an example flow diagram of a process for predicting road types of one embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     As required, detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention. 
     The present disclosure generally provides for a plurality of circuits or other electrical devices. All references to the circuits and other electrical devices, and the functionality provided by each, are not intended to be limited to encompassing only what is illustrated and described herein. While particular labels may be assigned to the various circuits or other electrical devices, such circuits and other electrical devices may be combined with each other and/or separated in any manner based on the particular type of electrical implementation that is desired. It is recognized that any circuit or other electrical device disclosed herein may include any number of microprocessors, integrated circuits, memory devices (e.g., FLASH, random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), or other suitable variants thereof) and software which co-act with one another to perform operation(s) disclosed herein. In addition, any one or more of the electric devices may be configured to execute a computer-program that is embodied in a non-transitory computer readable medium that is programed to perform any number of the functions as disclosed. 
     The present disclosure, among other things, proposes a vehicle system for predicting a road type and adjusting vehicle operations accordingly. More specifically, the present disclosure proposes a system configured to identify and predict the road type based on various inputs including number of traffic lights and stops signs (traffic controls) within a predefined distance ahead of the vehicle. Frequent traffic lights and stop signs may be observed during a city driving and no traffic light or stop sign may be observed during highway driving. Therefore, the traffic light and stop sign information may facilitate the identification of road type by the vehicle. 
     Referring to  FIG.  1   , an example block topology of a vehicle system  100  of one embodiment of the present disclosure is illustrated. A vehicle  102  may include various types of automobile, crossover utility vehicle (CUV), sport utility vehicle (SUV), truck, recreational vehicle (RV), boat, plane, or other mobile machine for transporting people or goods. In many cases, the vehicle  102  may be powered by an internal combustion engine. As another possibility, the vehicle  102  may be a battery electric vehicle (BEV), a hybrid electric vehicle (HEV) powered by both an internal combustion engine and one or move electric motors, such as a series hybrid electric vehicle (SHEV), a plug-in hybrid electric vehicle (PHEV), or a parallel/series hybrid vehicle (PSHEV), a boat, a plane or other mobile machine for transporting people or goods. As an example, the system  100  may include the SYNC system manufactured by The Ford Motor Company of Dearborn, Mich. It should be noted that the illustrated system  100  is merely an example, and more, fewer, and/or differently located elements may be used. 
     As illustrated in  FIG.  1   , a computing platform  104  may include one or more processors  106  configured to perform instructions, commands, and other routines in support of the processes described herein. For instance, the computing platform  104  may be configured to execute instructions of vehicle applications  108  to provide features such as navigation, remote controls, and wireless communications. Such instructions and other data may be maintained in a non-volatile manner using a variety of types of computer-readable storage medium  110 . The computer-readable medium  110  (also referred to as a processor-readable medium or storage) includes any non-transitory medium (e.g., tangible medium) that participates in providing instructions or other data that may be read by the processor  106  of the computing platform  104 . Computer-executable instructions may be compiled or interpreted from computer programs created using a variety of programming languages and/or technologies, including, without limitation, and either alone or in combination, Java, C, C++, C#, Objective C, Fortran, Pascal, Java Script, Python, Perl, and SQL. 
     The computing platform  104  may be provided with various features allowing the vehicle occupants/users to interface with the computing platform  104 . For example, the computing platform  104  may receive input from HMI controls  112  configured to provide for occupant interaction with the vehicle  102 . As an example, the computing platform  104  may interface with one or more buttons, switches, knobs, or other HMI controls configured to invoke functions on the computing platform  104  (e.g., steering wheel audio buttons, a push-to-talk button, instrument panel controls, etc.). 
     The computing platform  104  may also drive or otherwise communicate with one or more displays  114  configured to provide visual output to vehicle occupants by way of a video controller  116 . In some cases, the display  114  may be a touch screen further configured to receive user touch input via the video controller  116 , while in other cases the display  114  may be a display only, without touch input capabilities. The computing platform  104  may also drive or otherwise communicate with one or more speakers  118  configured to provide audio output and input to vehicle occupants by way of an audio controller  120 . 
     The computing platform  104  may also be provided with navigation and route planning features through a navigation controller  122  configured to calculate navigation routes responsive to user input via e.g., the HMI controls  112 , and output planned routes and instructions via the speaker  118  and the display  114 . Location data that is needed for navigation may be collected from a global navigation satellite system (GNSS) controller  124  configured to communicate with multiple satellites and calculate the location of the vehicle  102 . The GNSS controller  124  may be configured to support various current and/or future global or regional location systems such as global positioning system (GPS), Galileo, Beidou, Global Navigation Satellite System (GLONASS) and the like. Map data used for route planning may be stored in the storage  110  as a part of the vehicle data  126 . Navigation software may be stored in the storage  110  as one the vehicle applications  108 . 
     The computing platform  104  may be configured to wirelessly communicate with a mobile device  128  of the vehicle users/occupants via a wireless connection  130 . The mobile device  128  may be any of various types of portable computing devices, such as cellular phones, tablet computers, wearable devices, smart watches, smart fobs, laptop computers, portable music players, or other device capable of communication with the computing platform  104 . A wireless transceiver  132  may be in communication with a Wi-Fi controller  134 , a Bluetooth controller  136 , a radio-frequency identification (RFID) controller  138 , a near-field communication (NFC) controller  140 , and other controllers such as a Zigbee transceiver, an IrDA transceiver, an ultra-wide band (UWB) controller (not shown), and configured to communicate with a compatible wireless transceiver  142  of the mobile device  128 . 
     The mobile device  128  may be provided with a processor  144  configured to perform instructions, commands, and other routines in support of the processes such as navigation, telephone, wireless communication, and multi-media processing. For instance, the mobile device  128  may be provided with location and navigation functions via a navigation controller  146  and a GNSS controller  148 . The mobile device  128  may be provided with a wireless transceiver  142  in communication with a Wi-Fi controller  150 , a Bluetooth controller  152 , a RFID controller  154 , an NFC controller  156 , and other controllers (not shown), configured to communicate with the wireless transceiver  132  of the computing platform  104 . The mobile device  128  may be further provided with a non-volatile storage  158  to store various mobile application  160  and mobile data  162 . 
     The computing platform  104  may be further configured to communicate with various components of the vehicle  102  via one or more in-vehicle network  166 . The in-vehicle network  166  may include, but is not limited to, one or more of a controller area network (CAN), an Ethernet network, and a media-oriented system transport (MOST), as some examples. Furthermore, the in-vehicle network  166 , or portions of the in-vehicle network  166 , may be a wireless network accomplished via Bluetooth low-energy (BLE), Wi-Fi, UWB, or the like. 
     The computing platform  104  may be configured to communicate with various ECUs  168  of the vehicle  102  configured to perform various operations. For instance, the computing platform  104  may be configured to communicate with a TCU  170  configured to control telecommunication between vehicle  102  and a wireless network  172  through a wireless connection  174  using a modem  176 . The wireless connection  174  may be in the form of various communication network e.g., a cellular network. Through the wireless network  172 , the vehicle may access one or more servers  178  to access various content for various purposes. It is noted that the terms wireless network and server are used as general terms in the present disclosure and may include any computing network involving carriers, router, computers, controllers, circuitry or the like configured to store data and perform data processing functions and facilitate communication between various entities. The ECUs  168  may further include a powertrain control module (PCM)  180  configured to operate powertrain of the vehicle  102 . For instance, the PCM  180  may be configured to adjust vehicle powertrain and energy storage operation based on various inputs from the cloud server  178  and/or one or more sensors  182 . The sensors  182  may include various types of electronic devices configured to sense objects at the vicinity of the vehicle  102  and generate signals to send to various vehicle components via the in-vehicle network  166 . As a few non-limiting examples, the sensors  182  may include one or more cameras configured to capture images in front of the vehicle. Alternatively, cameras  182  may be a surrounding view camera. The sensors  182  may further include one or more radar sensors configured to detect object near the vehicle  102 . the sensors  182  may further include one or more speed sensors and acceleration sensors configured to measure the speed and acceleration of the vehicle  102 . The ECUs  168  may further include an autonomous driving controller (ADC)  184  configured to control an autonomous driving feature of the vehicle  102 . Driving instructions may be received remotely from the server  178 . The ADC  182  may be configured to perform the autonomous driving features using the driving instructions combined with navigation instructions from the navigation controller  122 . 
     The PCM  180  may be configured to operate powertrain of the vehicle  102  based on a current and future road types (a.k.a. road conditions). The road type may be identified and predicted using one or more associated characteristics (such as speed limit and/or density of road signs and traffic lights). Table 1 below illustrates an example road classification table on one embodiment of the present disclosure. It is noted that the parameters presented in Table 1 are merely examples and the present disclosure is not limited thereto. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Road type classification 
               
            
           
           
               
               
               
            
               
                   
                 Remote input 
                 Sensor input 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                   
                 Sign/light 
                 Form 
                   
                 Current avg. 
               
               
                   
                 Speed 
                 Sign 
                 (traffic control) 
                 of 
                 Functional 
                 speed/ 
               
               
                 Road Type 
                 limit 
                 type 
                 density 
                 way 
                 Class 
                 Acceleration 
               
               
                   
               
               
                 City (low steady state, 
                 &lt;40 mph 
                 Stop signs 
                 &gt;=4 
                 Not 
                 &lt;=class 4 
                 21 mph/ 
               
               
                 frequent stops, lot of 
                   
                 and traffic 
                 in 800 m 
                 Freeway 
                   
                 non-positive 
               
               
                 idle, low speed limit) 
                   
                 lights 
               
               
                 Residential (moderate 
                 &lt;50 mph 
                 Stop signs 
                 &gt;=1 
                 Not 
                 Any 
                 35 mph/ 
               
               
                 steady state, some 
                   
                   
                 in 1000 m 
                 multiple 
                   
                 non-negative 
               
               
                 stops) 
                   
                   
                   
                 carriageway 
               
               
                 Highway (high steady 
                 &gt;50 mph 
                 N/A 
                 N/A 
                 Freeway 
                 &lt;=class 3 
                 68 mph/ 
               
               
                 state) 
                   
                   
                   
                   
                   
                 non-negative 
               
               
                   
               
            
           
         
       
     
     As illustrated in Table 1, the road types may be classified into a plurality of categories including, a city road category, a residential road category and a highway category each corresponding to a different driving condition. It is noted that although only three categories of road type are presented in Table 1, the present disclosure is not limited thereto any more or less of road types may be used to satisfy different design needs under essentially the same concept of the present disclosure. Each road type may be associated with one or more pre-assigned characteristics indicative of a general condition for the road. For instance, the city road may be associated with a “low steady state” characteristic due to the frequency speed change and stops, more often idling and low speed limits. The residential road may be associated with a “moderate steady state” with some stops. The highway traffic may be associated with a “high steady state” with no traffic light or stop sign. Each road type category may be further associated with a variety of remote input information received from the sources such as the cloud server  178  via the TCU  170 . Additionally or alternatively, the remote input information may be at least partially captured via sensors  182  of the vehicle  102 . For instance, the front-facing camera  182  may capture images ahead of the vehicle and the computing platform  104  may analyze the image and extract the input information in lieu of or in addition to the remote input information received from the server  178 . As a few non-limiting examples each road type may correspond to a speed limit. The city road may correspond to less than 40 miles-per-hour (mph), whereas the residential road and highway may correspond to less than 50 mph and over 50 mph speed limits respectively. The road types may be further differentiated via signs and traffic lights associated with each different category. For instance, the city road may be associated with both stops signs and traffic lights, and it is very common for more than 4 stop signs and/or traffic lights to be present within 800 meters on a city road. In other words, if one or both of the above two conditions are met for a lookahead distance from the current location of the vehicle  102 , there is a good chance that the road within the lookahead distance will be of the city road type although other characteristics may also be used for such an identification. Similarly, the residential road may be associated no traffic light/stop signs only. The density of the stop signs may be 1 or more per 1000 meters in the present example. The highway may be associated with no stop sign or traffic light. 
     The remote input information may further include form of way entry indicative of a characteristic of the road within the lookahead distance. In the present example, city roads may never include any freeways, and the residential roads may not include any multiple carriageways. Only highway road type may include freeways. The remote input may further include a functional class entry which assigns an arbitrary value to allow the vehicle  102  to further differentiate between each different road type. In addition, vehicle sensor data may be further relied upon to identify a current road type. For instance, the vehicle sensor data may include a current average speed and acceleration entry to facilitate the identification of the current road type by the vehicle  102 . 
     Referring to  FIG.  2   , an example block diagram for a road type module  200  of one embodiment of the present disclosure is illustrated. With continuing reference to  FIG.  1    and Table 1, the road type module  200  may be implemented via the computing platform  104  individually or in combination with other components of the vehicle  102 . The road type module  200  may be configured to predict and identify road types using various data such as the data described with reference to Table 1. A data collector  202  may be configured to collect data from various sources. In the present example, the data collector  202  may collect road data from the cloud server  178  via the TCU  170 . Additionally or alternatively, the road data may be collected from the mobile device  128  via the wireless transceiver  132 . The mobile device  128  configured to pre-download the road data of a local area from the server  178  via a free-of-charge network (e.g. a home Wi-Fi) and store the data in the storage  158  as a part of the mobile data  162  to save data expenses. Alternatively, the mobile device  128  may selectively pre-download the road data on one or more given route predicted using data entries such as the user calendar appointment. Alternatively, the route may be provided by the navigation controller  122  of the computing platform  104 . Responsive to the planning the route via the navigation controller  122 , the computing platform  104  may send the route to the mobile device  128 . The computing platform  104  may further instruct the mobile device  128  to download road data along the route via a free-of-charge network before a predicted time when the vehicle  102  is predicted to start the route. Responsive to receiving the road data by the data collector  202 , the data may be classified into a plurality of categories and sent to the corresponding buffers. Road sign and traffic light data may be sent to a sign buffer  204  for temporary storage until further processing. As discussed above, the road sign and traffic light data may include information indicative of the location of the signs and traffic lights within a predefined distance from the current location of the vehicle  102 . In one example, the road sign data may only reflect the location of stop signs. Alternatively, the road sign data may further reflect the location of other signs (e.g. yield signs or the like) depending on different design needs. The data collector  202  may be further configured to send the speed limit data to a speed limit buffer  206 , the form of way data to a form of way buffer  208 , and the function class data to a functional class buffer  210  for temporary storage. 
     The data stored in each buffer  204 - 210  may be subsequently sent to a corresponding extractor for further analysis and processing. In the present example, the road sign and traffic light data  204  stored in the sign buffer  204  may be sent to a sign extractor  212 ; the speed limit data stored in the speed limit buffer  206  may be sent to a speed limit extractor  214 ; the form of way data stored in the form of way buffer  208  may be sent to a form of way extractor  216 ; and the functional class data stored in the functional class buffer  210  may be sent to a functional class extractor  218  analysis. 
     The road type module  200  may further include a route and distance calibrator  220  configured to determine a most probable path by calculating path probability for one or more possible vehicle route from the current location of the vehicle  102 . The path probabilities may be calculated using various factors such as vehicle route history, user calendar entry or the like. The route and distance calibrator  220  may be further configured to adjust the look ahead distance using parameters such as vehicle speed received from the vehicle speed sensor  182 , current and predicted road types or the like. For instance, responsive to detecting an increase in the average vehicle speed or a highway road type, the route and distance calibrator  220  may increase the lookahead distance, and responsive to detecting a decrease in the average vehicle speed or a city road type, the route and distance calibrator  220  may decrease the lookahead distance. The most probable path, the path probabilities and the adjusted lookahead distance may be provided to the extractors  212 - 218  to facilitate the classification extraction. Each extractor  212 - 218  may individually determine a road type classification based on the individual input. For instance, the sign extractor may perform road sign density calculations to closely predict an upcoming road type within the look ahead distance and output a road sign and traffic light classification output to a road type constructor  222 . The path probabilities and the adjusted lookahead distance may be used to form confidence assessment to generate and output a confidence rating to improve robustness of the system. Similarly, the speed limit extractor  214  may extracts the road sign and traffic light input data and output a road sign and traffic light classification along with a confidence rating to the road type constructor  222 . The form of way extractor  216  may extract the form of way input data and output a form of way classification along with a confidence rating to the road type constructor  222 . The functional class extractor  218  may extract the functional class input data and output a functional class classification along with a confidence rating to the constructor  222 . The road type constructor  222  may arbitrate the classifications and confidence ratings received from each of the extractors  212 - 218  to predict a road type within the lookahead distance on the most probable path. For instance, different weights may be assigned to outputs from different extractors  212 - 218  to facilitate the arbitration at the constructor  222 . In addition, higher confidence ratings may increase the weight of the respective extractor output. Different input data entries may be associated with different confidence ratings due to the varying quality of input data. For instance, the road sign and traffic light input data may be of a lower confidence rating in an area where construction work is present resulting in more temporary signs. Alternatively, the road type constructor  222  may simultaneously predict the road type for multiple paths having a path probability above a predefined threshold. 
     The road type may be output from the constructor  222  to the PCM  180  for powertrain operation adjustment. For instance, under a city road driving condition where stop signs and traffic signals are often, the PCM  180  for a hybrid electric vehicle may be configured to operate the vehicle  102  more dependent upon the electric machine powered by a traction battery (not shown) to save fuel and reduce emission. In other words, the PCM  180  may more actively pull down and less actively pull up the internal combustion engine during the city driving such that the vehicle  102  is propelled more often in the electric mode. Under a highway driving condition, the PCM  180  may be configured to more dependent on the vehicle engine to provide better performance. In an alternative example, the PCM  180  may be configured to adjust the transmission operating based on the road types. The PCM  180  may more actively perform downshift responsive to a throttle pedal input signal in the city to adapt to the changing speed in the city, and less actively perform downshift on the highway. 
     Referring to  FIG.  3   , an example flow diagram of a process  300  for predicting road types of one embodiment of the present disclosure is illustrated. With continuing reference to  FIGS.  1 - 2   , the process  300  may be implemented via various components of the road type module  200 . Some operations in the process  300  have already been introduced in the discussion with reference to  FIG.  2    and therefore will not be repeated in detail here. At operation  302 , the route and distance calibrator  220  determines the most probable path as well as one or more alternative path based on the current location of the vehicle  102 . As discussed above, the route and distance calibrator  220  may further send the paths to the mobile device  128  and request the mobile device  128  to download road data associated with the paths in advance. At operation  304 , the route and distance calibrator  220  adjusts the lookahead distance based on various factors and outputs an adjusted lookahead distance. At operation  306 , the TCU  170  downloads the road data for the most probable path and the alternative paths from the remote server  176 . The road data may include the road sign and traffic light data, the speed limit data, the form of way data, and the functional class data. At operation  308 , the sign extractor  212  extracts the road sign and traffic light data and calculates a sign and traffic light density within the lookahead distance on the one or more routes. The sign extractor  212  further determines the road type classification based on the sign and traffic light density with the confidence rating. In the present embodiment, the road type classification based on the sign and traffic light density may be given the greatest weight although the present disclosure is not limited thereto. At operation  310 , other extractors  214 - 218  extract the input road data and outputs a road type classification along with the confidence rating respectively. At operation  312 , responsive to receiving the road type classifications from the various extractors  212 - 218 , the road type constructor predicts the road type within the lookahead distance by arbitrating the plurality of road type classifications using the corresponding weights and confidence ratings. If the predicted road type is the same as the current road type, the process proceeds from operation  314  to operation  316  and the PCM keeps the current settings unchanged. Otherwise, if the predicted road type is different from the current road type, the process proceeds to operation  318  and the PCM adjust the powertrain settings based on the newly predicted road type. 
     While exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms of the invention. Rather, the words used in the specification are words of description rather than limitation, and it is understood that various changes may be made without departing from the spirit and scope of the invention. Additionally, the features of various implementing embodiments may be combined to form further embodiments of the invention.