ABSTRACT
Abstract
The technology relates to the prediction and handling of tire blowouts for vehicles operating in a self-driving (autonomous) mode. Aspects involve determining a likelihood of tire failure, including actions the vehicle may take to reduce the likelihood of failure. Pre-trip and real-time system checks can be taken. A vehicle model including the tires may be employed in blowout prediction. The on-board system may store received data and detected sensor regarding tire pressure and temperature, which can be evaluated based on the model in order to avoid or minimize the likelihood of a blowout given various factors. The factors can include the load weight and distribution of cargo, current and upcoming weather conditions on the route, the number of miles traveled per tire, and detected obstructions, such as potholes, debris or other roadway impairments. Should a blowout occur, the autonomous system may immediately take any necessary corrective action.
Description
BACKGROUND
Autonomous vehicles, such as vehicles that do not require a human driver, can be used to aid in the transport of trailered (e.g., towed) cargo, such as freight, livestock or other items from one location to another. Such vehicles may operate in a fully autonomous mode or a partially autonomous mode where a person may provide some driving input. Regardless of the driving mode, tire blowouts are significant events that can be dangerous for the autonomous vehicle and other road users.
BRIEF SUMMARY
The technology involves self-driving cargo trucks and other types of self-driving vehicles (SDVs). In particular, aspects of the technology relate to how tire blowouts on SDVs can be addressed. This can include prevention, detection and handling of vehicle responses should a blowout occur.
According to one aspect, a method of performing tire evaluation for an autonomous vehicle is provided. The method comprises obtaining, by one or more processors of the autonomous vehicle, baseline information for a set of tires of the autonomous vehicle; receiving, by the one or more processors during driving of the autonomous vehicle, sensor data regarding at least one tire of the set of tires; updating a dynamics model for the set of tires based on the baseline information and the received sensor data; receiving, by the one or more processors, information regarding at least one of (i) a roadway condition for a portion of a roadway, or (ii) an environmental condition; determining by the one or more processors, based on the updated dynamics model and the received information, whether a possibility of a tire failure for the at least one tire in the set exceeds a threshold possibility; and upon determining that the threshold possibility is exceeded, the one or more processors causing the autonomous vehicle to take a corrective action.
In one example, the received information includes an indication that the autonomous vehicle will encounter an obstacle on the portion of the roadway. For instance, the obstacle may be a pothole. The received information regarding the environmental condition may be an ambient temperature. The sensor data regarding the at least one tire may be pressure data, temperature data or shape data. Here, the pressure data can be obtained from a tire pressure monitoring system.
In another example, the sensor data regarding the at least one tire is received from a camera or lidar sensor of the autonomous vehicle. For instance, the sensor data may include a thermal image from an infrared camera of the autonomous vehicle. The corrective action may include adjusting a position of the autonomous vehicle within a lane to minimize possible impact with an obstacle on the portion of the roadway. The possibility of a tire failure may be, e.g., one of a blowout, slow leak, tread damage, sidewall damage or rim damage.
In a further example, the method also includes detecting a failure of the at least one tire of the set of tires and, in response to detecting the failure, the corrective action is selected from the group consisting of: adjusting a position of the autonomous vehicle within a lane to minimize possible impact with an obstacle on the portion of the roadway; pulling over and monitoring tire pressure for a first selected period of time; continuing driving and performing enhanced monitoring of the at least one tire for a second selected period of time; evaluating whether the at least one tire is safety critical for a current driving operation; changing a route of the autonomous vehicle; or notifying a remote service or another vehicle about either the roadway conditions for the portion of a roadway or the environmental condition.
Obtaining the baseline information may include performing one or more driving maneuvers to obtain data about the set of tires. And the method may further comprise training the dynamics model based on tire information received from a plurality of other autonomous vehicles.
According to another aspect, a vehicle configured to operate in an autonomous driving mode is provided. The vehicle comprises a driving system, a perception system, a positioning system and a control system. The driving system includes a steering subsystem, an acceleration subsystem and a deceleration subsystem to control driving of the vehicle in the autonomous driving mode. The perception system includes one or more sensors configured to detect objects in an environment external to the vehicle. The positioning system is configured to determine a current position of the vehicle. And the control system including one or more processors, wherein the control system is operatively coupled to the driving system, the perception system and the positioning system. The control system is configured to: obtain baseline information for a set of tires of the vehicle; receive, during driving of the autonomous vehicle, sensor data from the perception system regarding at least one tire of the set of tires; update a dynamics model for the set of tires based on the baseline information and the received sensor data; receive information regarding at least one of (i) a roadway condition for a portion of a roadway, or (ii) an environmental condition; determine, based on the updated dynamics model and the received information, whether a possibility of a tire failure for the at least one tire in the set exceeds a threshold possibility; and upon determining that the threshold possibility is exceeded, cause the autonomous vehicle to take a corrective action.
The sensor data regarding the at least one tire may be pressure data, temperature data or shape data. The sensor data regarding the at least one tire can be received from a camera or lidar sensor of the perception system. The control system may be configured to obtain the baseline information by causing the driving system to perform one or more driving maneuvers.
In one scenario, the control system is further configured to detect a failure of the at least one tire of the set of tires and, in response to detection of the failure, the corrective action is selected from the group consisting of: adjust a position of the autonomous vehicle within a lane to minimize possible impact with an obstacle on the portion of the roadway; pull over and monitor tire pressure for a first selected period of time; continue driving and perform enhanced monitoring of the at least one tire for a second selected period of time; evaluate whether the at least one tire is safety critical for a current driving operation; change a route of the autonomous vehicle; or notify a remote service or another vehicle about either the roadway conditions for the portion of a roadway or the environmental condition.
According to a further aspect, a control system is provided which includes memory storing a dynamics model of a vehicle configured to operate in an autonomous driving mode, and one or more processors operatively coupled to the memory. The one or more processors are configured to: obtain baseline information for a set of tires of the vehicle; receive sensor data from a perception system of the vehicle regarding at least one tire of the set of tires; update the dynamics model for the set of tires based on the baseline information and the received sensor data; receive information regarding at least one of (i) a roadway condition for a portion of a roadway, or (ii) an environmental condition; determine, based on the updated dynamics model and the received information, whether a possibility of a tire failure for the at least one tire in the set exceeds a threshold possibility; and upon determining that the threshold possibility is exceeded, issue a driving instruction for the autonomous vehicle to take a corrective action. By way of example, the possibility of a tire failure is one of a blowout, slow leak, tread damage, sidewall damage or rim damage.
BRIEF DESCRIPTION OF THE DRAWINGS
FIGS. 1A-B illustrates an example cargo vehicle arrangement for use with aspects of the technology.
FIG. 1C illustrates an example passenger vehicle arrangement for use with aspects of the technology.
FIGS. 2A-B are functional diagrams of an example tractor-trailer vehicle in accordance with aspects of the disclosure.
FIG. 3 is a function diagram of an example passenger vehicle in accordance with aspects of the disclosure.
FIGS. 4A-C illustrate example sensor fields of view for use with aspects of the technology.
FIGS. 5A-B illustrate a tire evaluation scenario in accordance with aspects of the technology.
FIG. 6 illustrates a vehicle shifting scenario in accordance with aspects of the technology.
FIG. 7 illustrates a cargo shifting scenario in accordance with aspects of the technology.
FIG. 8 illustrates an obstruction scenario in accordance with aspects of the technology.
FIGS. 9A-B illustrate an example arrangement in accordance with aspects of the technology.
FIG. 10 illustrates an example method of operating a vehicle in accordance with aspects of the technology.
DETAILED DESCRIPTION
The technology involves prediction of a likelihood of tire failure, including actions the vehicle may take to reduce the likelihood of failure. The technology also involves pre-trip and real-time system checks, and performing corrective actions should a tire fail. While many of the examples presented below involve commercial cargo vehicles, aspects of the technology may be employed with other types of vehicles.
Tire blowouts and other tire failures can occur due to various causes. For instance, underinflation may lead to overheating. A Tire Pressure Monitoring System (TPMS) can help with the status, but may not issue a pressure alert until a tire is significantly underinflated and by then that may be too late. Another factor is overloading the vehicle beyond its Gross Vehicular Weight Rating. Events during driving, such as passing over a pothole or encountering road debris can also cause tire failures such as a blowout, slow leak or physical damage that can affect tire integrity (e.g., tread or sidewall damage, rim damage, etc.). Environmental and component-related factors, such as extreme heat during the summer or the gradual wearing down of the tire, can also contribute to the likelihood of a blowout. However, it is recognized that tire failures may occur due to a combination of factors rather than a single issue. Thus, aspects of the technology involve monitoring different variables in real time that might contribute to a blowout or other failure. These aspects are discussed in detail below.
Example Vehicle Systems
FIGS. 1A-B illustrates an example cargo vehicle 100 , such as a tractor-trailer truck, and FIG. 1B illustrates an example passenger vehicle 150 , such as a minivan. The cargo vehicle 100 may include, e.g., a single, double or triple trailer, or may be another medium or heavy duty truck such as in commercial weight classes 4 through 8 . As shown, the truck includes a tractor unit 102 and a single cargo unit or trailer 104 . The trailer 104 may be fully enclosed, open such as a flat bed, or partially open depending on the freight or other type of cargo (e.g., livestock) to be transported. The tractor unit 102 includes the engine and steering systems (not shown) and a cab 106 for a driver and any passengers. In a fully autonomous arrangement, the cab 106 may not be equipped with seats or manual driving components, since no person may be necessary.
The trailer 104 includes a hitching point 108 , known as a kingpin. The kingpin is configured to pivotally attach to the tractor unit. In particular, the kingpin attaches to a trailer coupling, known as a fifth- wheel 109 , that is mounted rearward of the cab. Sensor units may be deployed along the tractor unit 102 and/or the trailer 104 . The sensor units are used to detect information about the surroundings around the cargo vehicle 100 . For instance, as shown the tractor unit 102 may include a roof-mounted sensor assembly 110 and one or more side sensor assemblies 112 , and the trailer 104 may employ one or more sen
BACKGROUND
Autonomous vehicles, such as vehicles that do not require a human driver, can be used to aid in the transport of trailered (e.g., towed) cargo, such as freight, livestock or other items from one location to another. Such vehicles may operate in a fully autonomous mode or a partially autonomous mode where a person may provide some driving input. Regardless of the driving mode, tire blowouts are significant events that can be dangerous for the autonomous vehicle and other road users.
BRIEF SUMMARY
The technology involves self-driving cargo trucks and other types of self-driving vehicles (SDVs). In particular, aspects of the technology relate to how tire blowouts on SDVs can be addressed. This can include prevention, detection and handling of vehicle responses should a blowout occur.
According to one aspect, a method of performing tire evaluation for an autonomous vehicle is provided. The method comprises obtaining, by one or more processors of the autonomous vehicle, baseline information for a set of tires of the autonomous vehicle; receiving, by the one or more processors during driving of the autonomous vehicle, sensor data regarding at least one tire of the set of tires; updating a dynamics model for the set of tires based on the baseline information and the received sensor data; receiving, by the one or more processors, information regarding at least one of (i) a roadway condition for a portion of a roadway, or (ii) an environmental condition; determining by the one or more processors, based on the updated dynamics model and the received information, whether a possibility of a tire failure for the at least one tire in the set exceeds a threshold possibility; and upon determining that the threshold possibility is exceeded, the one or more processors causing the autonomous vehicle to take a corrective action.
In one example, the received information includes an indication that the autonomous vehicle will encounter an obstacle on the portion of the roadway. For instance, the obstacle may be a pothole. The received information regarding the environmental condition may be an ambient temperature. The sensor data regarding the at least one tire may be pressure data, temperature data or shape data. Here, the pressure data can be obtained from a tire pressure monitoring system.
In another example, the sensor data regarding the at least one tire is received from a camera or lidar sensor of the autonomous vehicle. For instance, the sensor data may include a thermal image from an infrared camera of the autonomous vehicle. The corrective action may include adjusting a position of the autonomous vehicle within a lane to minimize possible impact with an obstacle on the portion of the roadway. The possibility of a tire failure may be, e.g., one of a blowout, slow leak, tread damage, sidewall damage or rim damage.
In a further example, the method also includes detecting a failure of the at least one tire of the set of tires and, in response to detecting the failure, the corrective action is selected from the group consisting of: adjusting a position of the autonomous vehicle within a lane to minimize possible impact with an obstacle on the portion of the roadway; pulling over and monitoring tire pressure for a first selected period of time; continuing driving and performing enhanced monitoring of the at least one tire for a second selected period of time; evaluating whether the at least one tire is safety critical for a current driving operation; changing a route of the autonomous vehicle; or notifying a remote service or another vehicle about either the roadway conditions for the portion of a roadway or the environmental condition.
Obtaining the baseline information may include performing one or more driving maneuvers to obtain data about the set of tires. And the method may further comprise training the dynamics model based on tire information received from a plurality of other autonomous vehicles.
According to another aspect, a vehicle configured to operate in an autonomous driving mode is provided. The vehicle comprises a driving system, a perception system, a positioning system and a control system. The driving system includes a steering subsystem, an acceleration subsystem and a deceleration subsystem to control driving of the vehicle in the autonomous driving mode. The perception system includes one or more sensors configured to detect objects in an environment external to the vehicle. The positioning system is configured to determine a current position of the vehicle. And the control system including one or more processors, wherein the control system is operatively coupled to the driving system, the perception system and the positioning system. The control system is configured to: obtain baseline information for a set of tires of the vehicle; receive, during driving of the autonomous vehicle, sensor data from the perception system regarding at least one tire of the set of tires; update a dynamics model for the set of tires based on the baseline information and the received sensor data; receive information regarding at least one of (i) a roadway condition for a portion of a roadway, or (ii) an environmental condition; determine, based on the updated dynamics model and the received information, whether a possibility of a tire failure for the at least one tire in the set exceeds a threshold possibility; and upon determining that the threshold possibility is exceeded, cause the autonomous vehicle to take a corrective action.
The sensor data regarding the at least one tire may be pressure data, temperature data or shape data. The sensor data regarding the at least one tire can be received from a camera or lidar sensor of the perception system. The control system may be configured to obtain the baseline information by causing the driving system to perform one or more driving maneuvers.
In one scenario, the control system is further configured to detect a failure of the at least one tire of the set of tires and, in response to detection of the failure, the corrective action is selected from the group consisting of: adjust a position of the autonomous vehicle within a lane to minimize possible impact with an obstacle on the portion of the roadway; pull over and monitor tire pressure for a first selected period of time; continue driving and perform enhanced monitoring of the at least one tire for a second selected period of time; evaluate whether the at least one tire is safety critical for a current driving operation; change a route of the autonomous vehicle; or notify a remote service or another vehicle about either the roadway conditions for the portion of a roadway or the environmental condition.
According to a further aspect, a control system is provided which includes memory storing a dynamics model of a vehicle configured to operate in an autonomous driving mode, and one or more processors operatively coupled to the memory. The one or more processors are configured to: obtain baseline information for a set of tires of the vehicle; receive sensor data from a perception system of the vehicle regarding at least one tire of the set of tires; update the dynamics model for the set of tires based on the baseline information and the received sensor data; receive information regarding at least one of (i) a roadway condition for a portion of a roadway, or (ii) an environmental condition; determine, based on the updated dynamics model and the received information, whether a possibility of a tire failure for the at least one tire in the set exceeds a threshold possibility; and upon determining that the threshold possibility is exceeded, issue a driving instruction for the autonomous vehicle to take a corrective action. By way of example, the possibility of a tire failure is one of a blowout, slow leak, tread damage, sidewall damage or rim damage.
BRIEF DESCRIPTION OF THE DRAWINGS
FIGS. 1A-B illustrates an example cargo vehicle arrangement for use with aspects of the technology.
FIG. 1C illustrates an example passenger vehicle arrangement for use with aspects of the technology.
FIGS. 2A-B are functional diagrams of an example tractor-trailer vehicle in accordance with aspects of the disclosure.
FIG. 3 is a function diagram of an example passenger vehicle in accordance with aspects of the disclosure.
FIGS. 4A-C illustrate example sensor fields of view for use with aspects of the technology.
FIGS. 5A-B illustrate a tire evaluation scenario in accordance with aspects of the technology.
FIG. 6 illustrates a vehicle shifting scenario in accordance with aspects of the technology.
FIG. 7 illustrates a cargo shifting scenario in accordance with aspects of the technology.
FIG. 8 illustrates an obstruction scenario in accordance with aspects of the technology.
FIGS. 9A-B illustrate an example arrangement in accordance with aspects of the technology.
FIG. 10 illustrates an example method of operating a vehicle in accordance with aspects of the technology.
DETAILED DESCRIPTION
The technology involves prediction of a likelihood of tire failure, including actions the vehicle may take to reduce the likelihood of failure. The technology also involves pre-trip and real-time system checks, and performing corrective actions should a tire fail. While many of the examples presented below involve commercial cargo vehicles, aspects of the technology may be employed with other types of vehicles.
Tire blowouts and other tire failures can occur due to various causes. For instance, underinflation may lead to overheating. A Tire Pressure Monitoring System (TPMS) can help with the status, but may not issue a pressure alert until a tire is significantly underinflated and by then that may be too late. Another factor is overloading the vehicle beyond its Gross Vehicular Weight Rating. Events during driving, such as passing over a pothole or encountering road debris can also cause tire failures such as a blowout, slow leak or physical damage that can affect tire integrity (e.g., tread or sidewall damage, rim damage, etc.). Environmental and component-related factors, such as extreme heat during the summer or the gradual wearing down of the tire, can also contribute to the likelihood of a blowout. However, it is recognized that tire failures may occur due to a combination of factors rather than a single issue. Thus, aspects of the technology involve monitoring different variables in real time that might contribute to a blowout or other failure. These aspects are discussed in detail below.
Example Vehicle Systems
FIGS. 1A-B illustrates an example cargo vehicle 100 , such as a tractor-trailer truck, and FIG. 1B illustrates an example passenger vehicle 150 , such as a minivan. The cargo vehicle 100 may include, e.g., a single, double or triple trailer, or may be another medium or heavy duty truck such as in commercial weight classes 4 through 8 . As shown, the truck includes a tractor unit 102 and a single cargo unit or trailer 104 . The trailer 104 may be fully enclosed, open such as a flat bed, or partially open depending on the freight or other type of cargo (e.g., livestock) to be transported. The tractor unit 102 includes the engine and steering systems (not shown) and a cab 106 for a driver and any passengers. In a fully autonomous arrangement, the cab 106 may not be equipped with seats or manual driving components, since no person may be necessary.
The trailer 104 includes a hitching point 108 , known as a kingpin. The kingpin is configured to pivotally attach to the tractor unit. In particular, the kingpin attaches to a trailer coupling, known as a fifth- wheel 109 , that is mounted rearward of the cab. Sensor units may be deployed along the tractor unit 102 and/or the trailer 104 . The sensor units are used to detect information about the surroundings around the cargo vehicle 100 . For instance, as shown the tractor unit 102 may include a roof-mounted sensor assembly 110 and one or more side sensor assemblies 112 , and the trailer 104 may employ one or more sensor assemblies 114 , for example mounted on the left and/or right sides of the trailer 104 . In some examples, the tractor unit 102 and trailer 104 also may include other various sensors for obtaining information about the tractor unit 102 's and/or trailer 104 's interior spaces, including the cargo hold of the trailer.
Similarly, the passenger vehicle 150 may include various sensors for obtaining information about the vehicle's external environment. For instance, a roof- top housing 152 may include a lidar sensor as well as various cameras and/or radar units. Housing 154 , located at the front end of vehicle 150 , and housings
156 a , 156 b on the driver's and passenger's sides of the vehicle may each incorporate a lidar sensor and/or other sensors such as cameras and radar. For example, housing 156 a may be located in front of the driver's side door along a quarterpanel of the vehicle. As shown, the passenger vehicle 150 also includes housings
158 a , 158 b for radar units, lidar and/or cameras also located towards the rear roof portion of the vehicle. Additional lidar, radar units and/or cameras (not shown) may be located at other places along the vehicle 100 . For instance, arrow 160 indicates that a sensor unit may be positioned along the read of the vehicle 150 , such as on or adjacent to the bumper. In some examples, the passenger vehicle 150 also may include various sensors for obtaining information about the vehicle 150 's interior spaces.
While certain aspects of the disclosure may be particularly useful in connection with specific types of vehicles, the vehicle may be any type of vehicle including, but not limited to, cars, trucks, motorcycles, buses, recreational vehicles, etc.
FIG. 2A illustrates a block diagram 200 with various components and systems of a cargo vehicle (e.g., as shown in FIGS. 1A-B ), such as a truck, farm equipment or construction equipment, configured to operate in a fully or semi-autonomous mode of operation. By way of example, there are different degrees of autonomy that may occur for a vehicle operating in a partially or fully autonomous driving mode. The U.S. National Highway Traffic Safety Administration and the Society of Automotive Engineers have identified different levels to indicate how much, or how little, the vehicle controls the driving. For instance, Level 0 has no automation and the driver makes all driving-related decisions. The lowest semi-autonomous mode, Level 1, includes some drive assistance such as cruise control. Level 2 has partial automation of certain driving operations, while Level 3 involves conditional automation that can enable a person in the driver's seat to take control as warranted. In contrast, Level 4 is a high automation level where the vehicle is able to drive without assistance in select conditions. And Level 5 is a fully autonomous mode in which the vehicle is able to drive without assistance in all situations. The architectures, components, systems and methods described herein can function in any of the semi or fully-autonomous modes, e.g., Levels 1-5, which are referred to herein as âautonomousâ driving modes. Thus, reference to an autonomous driving mode includes both partial and full autonomy.
As shown in the block diagram of FIG. 2A , the vehicle includes a control system of one or more computing devices, such as computing devices 202 containing one or more processors 204 , memory 206 and other components typically present in general purpose computing devices. The control system may constitute an electronic control unit (ECU) of a tractor unit or other computing system of the vehicle. The memory 206 stores information accessible by the one or more processors 204 , including instructions 208 and data 210 that may be executed or otherwise used by the processor 204 . The memory 206 may be of any type capable of storing information accessible by the processor, including a computing device-readable medium. The memory is a non-transitory medium such as a hard-drive, memory card, optical disk, solid-state, tape memory, or the like. Systems may include different combinations of the foregoing, whereby different portions of the instructions and data are stored on different types of media.
The instructions 208 may be any set of instructions to be executed directly (such as machine code) or indirectly (such as scripts) by the processor. For example, the instructions may be stored as computing device code on the computing device-readable medium. In that regard, the terms âinstructionsâ and âprogramsâ may be used interchangeably herein. The instructions may be stored in object code format for direct processing by the processor, or in any other computing device language including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance. The data 210 may be retrieved, stored or modified by one or more processors 204 in accordance with the instructions 208 . In one example, some or all of the memory 206 may be an event data recorder or other secure data storage system configured to store vehicle diagnostics and/or detected sensor data, which may be on board the vehicle or remote, depending on the implementation. As illustrated in FIG. 2A , the data may include information about the tires (e.g., a tire dynamics model based on tire pressure data, tire age, rotation history, etc.) and/or the vehicle (e.g., current loaded weight, trip and/or total mileage, planned route, component status, a general model of the vehicle, etc.).
The one or more processor 204 may be any conventional processors, such as commercially available CPUs. Alternatively, the one or more processors may be a dedicated device such as an ASIC or other hardware-based processor. Although FIG. 2A functionally illustrates the processor(s), memory, and other elements of computing devices 202 as being within the same block, such devices may actually include multiple processors, computing devices, or memories that may or may not be stored within the same physical housing. Similarly, the memory 206 may be a hard drive or other storage media located in a housing different from that of the processor(s) 204 . Accordingly, references to a processor or computing device will be understood to include references to a collection of processors or computing devices or memories that may or may not operate in parallel.
In one example, the computing devices 202 may form an autonomous driving computing system incorporated into vehicle 100 . The autonomous driving computing system may capable of communicating with various components of the vehicle. For example, returning to FIG. 2A , the computing devices 202 may be in communication with various systems of the vehicle, including a driving system including a deceleration system 212 (for controlling braking of the vehicle), acceleration system 214 (for controlling acceleration of the vehicle), steering system 216 (for controlling the orientation of the wheels and direction of the vehicle), signaling system 218 (for controlling turn signals), navigation system 220 (for navigating the vehicle to a location or around objects) and a positioning system 222 (for determining the position of the vehicle).
The computing devices 202 are also operatively coupled to a perception system 224 (for detecting objects in the vehicle's environment), a power system 226 (for example, a battery and/or gas or diesel powered engine) and a transmission system 230 in order to control the movement, speed, etc., of the vehicle in accordance with the instructions 208 of memory 206 in an autonomous driving mode which does not require or need continuous or periodic input from a passenger of the vehicle. Some or all of the wheels/ tires 228 are coupled to the transmission system 230 , and the computing devices 202 may be able to receive information about tire pressure (e.g., TMPS data), balance and other factors that may impact driving in an autonomous mode.
The computing devices 202 may control the direction and speed of the vehicle by controlling various components. By way of example, computing devices 202 may navigate the vehicle to a destination location completely autonomously using data from the map information and navigation system 220 . Computing devices 202 may use the positioning system 222 to determine the vehicle's location and the perception system 224 to detect and respond to objects when needed to reach the location safely. In order to do so, computing devices 202 may cause the vehicle to accelerate (e.g., by increasing fuel or other energy provided to the engine by acceleration system 214 ), decelerate (e.g., by decreasing the fuel supplied to the engine, changing gears, and/or by applying brakes by deceleration system 212 ), change direction (e.g., by turning the front or other wheels of vehicle 100 by steering system 216 ), and signal such changes (e.g., by lighting turn signals of signaling system 218 ). Thus, the acceleration system 214 and deceleration system 212 may be a part of a drivetrain or other transmission system 230 that includes various components between an engine of the vehicle and the wheels of the vehicle. Again, by controlling these systems, computing devices 202 may also control the transmission system 230 of the vehicle in order to maneuver the vehicle autonomously.
As an example, computing devices 202 may interact with deceleration system 212 and acceleration system 214 in order to control the speed of the vehicle. Similarly, steering system 216 may be used by computing devices 202 in order to control the direction of vehicle. For example, if the vehicle is configured for use on a road, such as a tractor-trailer truck or a construction vehicle, the steering system 216 may include components to control the angle of wheels of the tractor unit 102 to turn the vehicle. Signaling system 218 may be used by computing devices 202 in order to signal the vehicle's intent to other drivers or vehicles, for example, by lighting turn signals or brake lights when needed.
Navigation system 220 may be used by computing devices 202 in order to determine and follow a route to a location. In this regard, the navigation system 220 and/or memory 206 may store map information, e.g., highly detailed maps that computing devices 202 can use to navigate or control the vehicle. As an example, these maps may identify the shape and elevation of roadways, lane markers, intersections, crosswalks, speed limits, traffic signal lights, buildings, signs, real time traffic information, vegetation, or other such objects and information. The lane markers may include features such as solid or broken double or single lane lines, solid or broken lane lines, reflectors, etc. A given lane may be associated with left and right lane lines or other lane markers that define the boundary of the lane. Thus, most lanes may be bounded by a left edge of one lane line and a right edge of another lane line.
The perception system 224 also includes sensors for detecting objects external to the vehicle and/or aspects of the vehicle itself (e.g., tire status). The detected objects may be other vehicles, obstacles in the roadway, traffic signals, signs, trees, etc. For example, the perception system 224 may include one or more lidar sensors, sonar devices, radar units, cameras (e.g., optical and/or infrared), acoustic sensors, inertial sensors (e.g., gyroscopes or accelerometers), and/or any other detection devices that record data which may be processed by computing devices 202 . The sensors of the perception system 224 may detect objects and their characteristics such as location, orientation, size, shape, type (for instance, vehicle, pedestrian, bicyclist, etc.), heading, and speed of movement, etc. The raw data from the sensors and/or the aforementioned characteristics can be sent for further processing to the computing devices 202 periodically and continuously as it is generated by the perception system 224 . Computing devices 202 may use the positioning system 222 to determine the vehicle's location and perception system 224 to detect and respond to objects when needed to reach the location safely. In addition, the computing devices 202 may perform calibration of individual sensors, all sensors in a particular sensor assembly, or between sensors in different sensor assemblies.
As indicated in FIG. 2A , the sensors of the perception system 224 may be incorporated into one or more sensor assemblies 232 . In one example, the sensor assemblies 232 may be arranged as sensor towers integrated into the side-view mirrors on the truck, farm equipment, construction equipment or the like. Sensor assemblies 232 may also be positioned at different locations on the tractor unit 102 or on the trailer 104 (see FIGS. 1A-B ), or along different portions of passenger vehicle 150 (see FIG. 1C ). The computing devices 202 may communicate with the sensor assemblies located on both the tractor unit 102 and the trailer 104 or distributed along the passenger vehicle 150 . Each assembly may have one or more types of sensors such as those described above.
Also shown in FIG. 2A is a communication system 234 and a coupling system 236 for connectivity between the tractor unit and the trailer. The coupling system 236 includes a fifth-wheel at the tractor unit and a kingpin at the trailer. The communication system 234 may include one or more wireless network connections to facilitate communication with other computing devices, such as passenger computing devices within the vehicle, and computing devices external to the vehicle, such as in another nearby vehicle on the roadway or at a remote network. The network connections may include short range communication protocols such as Bluetoothâ¢, Bluetooth low energy (LE), cellular connections, as well as various configurations and protocols including the Internet, World Wide Web, intranets, virtual private networks, wide area networks, local networks, private networks using communication protocols proprietary to one or more companies, Ethernet, WiFi and HTTP, and various combinations of the foregoing.
FIG. 2B illustrates a block diagram 240 of an example trailer. As shown, the system includes an ECU 242 of one or more computing devices, such as computing devices containing one or more processors 244 , memory 246 and other components typically present in general purpose computing devices. The memory 246 stores information accessible by the one or more processors 244 , including instructions 248 and data 250 that may be executed or otherwise used by the processor(s) 244 . The descriptions of the processors, memory, instructions and data from FIG. 2A apply to these elements of FIG. 2B . As illustrated in FIG. 2B , the data 250 may include information about the trailer's tires (e.g., tire pressure data, tire age, rotation history, etc.) and/or the trailer itself (e.g., current loaded weight, trip and/or total mileage, etc.).
The ECU 242 is configured to receive information and control signals from the trailer unit. The on- board processors 244 of the ECU 242 may communicate with various systems of the trailer, including a deceleration system 252 (for controlling braking of the trailer), signaling system 254 (for controlling turn signals), and a positioning system 256 (for determining the position of the trailer). The processors 244 may receive TPMS data from the trailer's tires.
The ECU 242 may also be operatively coupled to a perception system 258 (for detecting objects in the trailer's environment and/or aspects of the trailer itself) and a power system 260 (for example, a battery power supply) to provide power to local components. Some or all of the wheels/ tires 262 of the trailer may be coupled to the deceleration system 252 , and the processors 244 may be able to receive information about tire pressure, balance, wheel speed and other factors that may impact driving in an autonomous mode, and to relay that information to the processing system of the tractor unit. The deceleration system 252 , signaling system 254 , positioning system 256 , perception system 258 , power system 260 and wheels/ tires 262 may operate in a manner such as described above with regard to FIG. 2A . For instance, the perception system 258 , if employed as part of the trailer, may include at least one sensor assembly 264 having one or more lidar sensors, sonar devices, radar units, cameras, inertial sensors, and/or any other detection devices that record data which may be processed by the ECU 242 or by the processors 204 of the tractor unit.
The trailer may also include a set of landing gear 266 , as well as a coupling system 268 . The landing gear 266 provide a support structure for the trailer when decoupled from the tractor unit. The coupling system 268 , which may be a part of coupling system 236 of the tractor unit, provides connectivity between the trailer and the tractor unit. The coupling system 268 may include a connection section 270 to provide backward compatibility with legacy trailer units that may or may not be capable of operating in an autonomous mode. The coupling system includes a kingpin 272 configured for enhanced connectivity with the fifth-wheel of an autonomous-capable tractor unit.
FIG. 3 illustrates a block diagram 300 of various systems of a passenger vehicle. As shown, the system includes one or more computing devices 302 , such as computing devices containing one or more processors 304 , memory 306 and other components typically present in general purpose computing devices. The memory 306 stores information accessible by the one or more processors 304 , including instructions 308 and data 310 that may be executed or otherwise used by the processor(s) 304 . The descriptions of the processors, memory, instructions and data from FIG. 2A apply to these elements of FIG. 3 .
As with the computing devices 202 of FIG. 2A , the computing devices 302 of FIG. 3 may control computing devices of an autonomous driving computing system or incorporated into a passenger vehicle. The autonomous driving computing system may be capable of communicating with various components of the vehicle in order to control the movement of the passenger vehicle according to primary vehicle control code of memory 306 . For example, computing devices 302 may be in communication with various, such as deceleration system 312 , acceleration system</figure-callout
CLAIMS
Claims ( 20 )
1 . A method of performing tire evaluation for an autonomous vehicle, the method comprising:
obtaining, by one or more processors of the autonomous vehicle, baseline information for a set of tires of the autonomous vehicle; receiving, by the one or more processors during driving of the autonomous vehicle, sensor data regarding at least one tire of the set of tires; updating a dynamics model for the set of tires based on the baseline information and the received sensor data; receiving, by the one or more processors, information regarding at least one of (i) a roadway condition for a portion of a roadway, or (ii) an environmental condition; determining by the one or more processors, based on the updated dynamics model and the received information, whether a possibility of a tire failure for the at least one tire in the set exceeds a threshold possibility; and upon determining that the threshold possibility is exceeded, the one or more processors causing the autonomous vehicle to take a corrective action.
2 . The method of claim 1 , wherein the received information includes an indication that the autonomous vehicle will encounter an obstacle on the portion of the roadway.
3 . The method of claim 2 , wherein the obstacle is a pothole.
4 . The method of claim 1 , wherein the received information regarding the environmental condition is an ambient temperature.
5 . The method of claim 1 , wherein the sensor data regarding the at least one tire is pressure data, temperature data or shape data.
6 . The method of claim 5 , wherein the pressure data is obtained from a tire pressure monitoring system.
7 . The method of claim 1 , wherein the sensor data regarding the at least one tire is received from a camera or lidar sensor of the autonomous vehicle.
8 . The method of claim 7 , wherein the sensor data includes a thermal image from an infrared camera of the autonomous vehicle.
9 . The method of claim 1 , wherein the corrective action includes adjusting a position of the autonomous vehicle within a lane to minimize possible impact with an obstacle on the portion of the roadway.
10 . The method of claim 1 , wherein the possibility of a tire failure is one of a blowout, slow leak, tread damage, sidewall damage or rim damage.
11 . The method of claim 1 , further comprising:
detecting a failure of the at least one tire of the set of tires; and in response to detecting the failure, the corrective action is selected from the group consisting of:
adjusting a position of the autonomous vehicle within a lane to minimize possible impact with an obstacle on the portion of the roadway;
pulling over and monitoring tire pressure for a first selected period of time;
continuing driving and performing enhanced monitoring of the at least one tire for a second selected period of time;
evaluating whether the at least one tire is safety critical for a current driving operation;
changing a route of the autonomous vehicle; or
notifying a remote service or another vehicle about either the roadway conditions for the portion of a roadway or the environmental condition.
12 . The method of claim 1 , wherein obtaining the baseline information includes performing one or more driving maneuvers to obtain data about the set of tires.
13 . The method of claim 1 , further comprising training the dynamics model based on tire information received from a plurality of other autonomous vehicles.
14 . A vehicle configured to operate in an autonomous driving mode, the vehicle comprising:
a driving system including a steering subsystem, an acceleration subsystem and a deceleration subsystem to control driving of the vehicle in the autonomous driving mode; a perception system including one or more sensors configured to detect objects in an environment external to the vehicle; a positioning system configured to determine a current position of the vehicle; and a control system including one or more processors, the control system operatively coupled to the driving system, the perception system and the positioning system, the control system being configured to:
obtain baseline information for a set of tires of the vehicle;
receive, during driving of the autonomous vehicle, sensor data from the perception system regarding at least one tire of the set of tires;
update a dynamics model for the set of tires based on the baseline information and the received sensor data;
receive information regarding at least one of (i) a roadway condition for a portion of a roadway, or (ii) an environmental condition;
determine, based on the updated dynamics model and the received information, whether a possibility of a tire failure for the at least one tire in the set exceeds a threshold possibility; and
upon determining that the threshold possibility is exceeded, cause the autonomous vehicle to take a corrective action.
15 . The vehicle of claim 14 , wherein the sensor data regarding the at least one tire is pressure data, temperature data or shape data.
16 . The vehicle of claim 14 , wherein the sensor data regarding the at least one tire is received from a camera or lidar sensor of the perception system.
17 . The vehicle of claim 14 , wherein the control system is configured to obtain the baseline information by causing the driving system to perform one or more driving maneuvers.
18 . The vehicle of claim 15 , wherein the control system is further configured to:
detect a failure of the at least one tire of the set of tires; and in response to detection of the failure, the corrective action is selected from the group consisting of:
adjust a position of the autonomous vehicle within a lane to minimize possible impact with an obstacle on the portion of the roadway;
pull over and monitor tire pressure for a first selected period of time;
continue driving and perform enhanced monitoring of the at least one tire for a second selected period of time;
evaluate whether the at least one tire is safety critical for a current driving operation;
change a route of the autonomous vehicle; or
notify a remote service or another vehicle about either the roadway conditions for the portion of a roadway or the environmental condition.
19 . A control system comprising:
memory storing a dynamics model of a vehicle configured to operate in an autonomous driving mode; and one or more processors operatively coupled to the memory, the one or more processors being configured to:
obtain baseline information for a set of tires of the vehicle;
receive sensor data from a perception system of the vehicle regarding at least one tire of the set of tires;
update the dynamics model for the set of tires based on the baseline information and the received sensor data;
receive information regarding at least one of (i) a roadway condition for a portion of a roadway, or (ii) an environmental condition;
determine, based on the updated dynamics model and the received information, whether a possibility of a tire failure for the at least one tire in the set exceeds a threshold possibility; and
upon determining that the threshold possibility is exceeded, issue a driving instruction for the autonomous vehicle to take a corrective action.
20 . The control system of claim 19 , wherein the possibility of a tire failure is one of a blowout, slow leak, tread damage, sidewall damage or rim damage.
US16/714,967
2019-12-16
2019-12-16
Prevention, detection and handling of the tire blowouts on autonomous trucks
Abandoned
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US20210181737A1
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2019-12-16
2019-12-16
Prevention, detection and handling of the tire blowouts on autonomous trucks
EP20212817.9A
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Prevention, detection and handling of tire blowouts on autonomous trucks
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CN112977437B
( en )
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2020-12-16
Autonomous Truck Tire Blowout Prevention, Detection and Treatment
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Prevention, detection and handling of the tire blowouts on autonomous trucks
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Prevention, detection and handling of the tire blowouts on autonomous trucks
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