Dynamically adjusting an infrastructure item

Disclosure herein are systems and methods for dynamically adjusting infrastructure items, such as street lights, construction signage, and/or other lighting elements. The systems and methods may include receiving environmental data for a sector containing the infrastructure items. A quality of infrastructure effectors located within the sector may be determined. A deviation from a standard infrastructure quality associated with the infrastructure effectors may be determined. A setting of the infrastructure items located in the sector may be changed to minimize the deviation from the standard infrastructure quality.

FIELD OF THE DISCLOSURE

The present subject matter relates to infrastructure and autonomous systems. Specifically, the present disclosure relates to dynamically adjusting infrastructure used by autonomous vehicles.

BACKGROUND

Autonomous vehicles have been developed to automate, adapt, or enhance vehicle systems to increase safety and provide better driving. In such systems, safety features are designed to avoid collisions and accidents by offering technologies that alert the driver to potential problems, or to avoid collisions by implementing safeguards and allowing drivers to take over control of the vehicle. Autonomous vehicles rely on various sensors that are able to detect objects and other aspects of their operating environment.

DETAILED DESCRIPTION

Autonomous vehicles may confront several challenges when deployed. The various algorithms that control autonomous vehicles may use visual data from onboard cameras and/or other sensors to localize and/or to detect objects on the road, such as other vehicles and/or pedestrians, and their surroundings, such as guard rails, curbs, signs, etc. The algorithms may make attempts to compensate for changes in the appearance of the environment, such as, for example, compensating for shadows caused by other vehicles and/or objects. Some challenges, for example, variations in illumination at night and/or changes in weather conditions, such as, for example, changes from sunny to cloudy conditions, may cause system failures and/or other problems. Algorithms may attempt to mitigate these sudden changes in appearance. As disclosed herein, sky blackening algorithms or other algorithm types, may use image analysis of current images in conjunction with past images and/or known images to compensate for atmospheric conditions such as cloudy skies.

As disclosed herein, changes in lighting conditions may be tackled by dynamically adjusting the infrastructure surrounding roads during hours of poor natural illumination. The systems and methods disclosed herein may provide a range of illumination that may optimize the performance of autonomous vehicles. For example, the light intensity (e.g., lumens per square meter, or lux) produced by infrastructure, such as streetlights, may be dynamically adjusted to provide optimized lighting conditions for autonomous vehicles. Other non-limiting examples include dynamically adjusting the focus and/or direction of lighting elements to better illuminate roads, paths, etc.

The systems and methods disclosed herein also allow for overall improvement of infrastructure systems by allowing for better control of energy usage, minimize light pollution, while at the same time delivering optimized conditions for autonomous vehicles. The optimized conditions improve the overall system by minimizing risks associated with potential collisions during fully autonomous operations. During driver assisted autonomous operations system improvements may be recognized by reducing driver fatigue and errors caused by suboptimal lighting conditions.

The dynamic adjustment of infrastructure as disclosed herein represents an improvement to previous solutions that focused on improving the performance of autonomous vehicles by innovating on their hardware or perception algorithms. Stated another way, instead of optimizing environmental conditions, previous solutions attempted to improve the sensors and algorithms used in autonomous vehicles to detect the changes in environmental conditions. The systems and methods disclosed herein may be utilized in conjunction with or without the improved onboard sensors and algorithms of autonomous vehicles to achieve greater efficiency and overall system improvements resulting in lower accident rates caused by changes in environmental conditions.

The systems and methods disclosed herein may utilize smart infrastructure. The smart infrastructure may change and adapt dynamically to improve the performance of autonomous vehicles. For instance, the systems and methods disclosed herein may allow sensors in cities to collect environmental data that may be used to dynamically adjust lighting conditions to improve autonomous vehicle navigation on streets. For example, lighting improvements in locations, sometimes called sectors, may result in improvements in autonomous vehicle navigation and/or object detection capabilities. An additional improvement may be reduction in required redundancy/performance requirements on vehicles at the expense of infrastructure.

The above discussion is intended to provide an overview of subject matter of the present patent application. It is not intended to provide an exclusive or exhaustive explanation of the disclosure. The description below is included to provide further information about the present disclosure.

Turning now to the figures,FIG.1shows an example environment100for dynamically adjusting infrastructure in accordance with at least one example of this disclosure. As shown inFIG.1, environment100may include area102, an autonomous vehicle104, an infrastructure items106, a controller108, and sensors110.

Area102may be a city, county, state/province, or country. Area102may be divided into one or more sectors112(labeled individually as sectors112A,112B,112C, and112D). Examples of sectors112can include states/provinces of a country, counties and/or cities of a state/province, blocks within a city, particular zip/postal codes within an area, etc.

Sectors112can include infrastructure items106. A single infrastructure item106is shown in each of sectors112for clarity, but sectors112may have a plurality of infrastructure items106. The various infrastructure items106may be of differing types as well. Infrastructure items may be any hardware or software element that is disposed within any environment Which may or may comprise sensors. For example, infrastructure items106may be streetlights as shown inFIG.1or any type of illumination device that may be located within sectors112. For example, infrastructure items106may be an overhead streetlight as shown inFIG.1, inground lighting such as curb lighting, traffic lights, construction lighting, lighted signage, etc.

Sectors112may include any number of sensors110, Two sensors110are shown in each of sectors112for clarity. Sensors110may be a component of infrastructure items106. For example, sensors110may be photocells that are components of streetlights. Sensors110may also be separate from infrastructure items106. For instance, sensors110may be photocells and/or moisture sensors embedded in a road and/or in a guardrail positioned proximate the road.

Sensors110may be associated with a single infrastructure item, such as a streetlight, or with multiple infrastructure items. For example, a single sensor or a collection of sensors may be associated with a single streetlight or a plurality of streetlights to measure a general illumination of an area. For instance, a first set of sensors may be used to measure a general illumination of a first block of a city illuminated by a first set of streetlights. A second set of sensors may be used to measure a general illumination of a second block of a city illuminated by a second set of streetlights.

As shown inFIG.1, both infrastructure items106and sensors110may be in electrical communication with controller108. This connection may be wired or wireless network114. Autonomous vehicle104may also be in electrical communication with controller108via a wireless connection that utilizes network114.

The sensors110can transmit one or more signals to controller108that correspond to environmental data. Controller108can use the signals to determine a quality of one or more infrastructure effectors. Infrastructure effectors effectuates the infrastructure items. Infrastructure effectors may include a control system and/or other feature of an infrastructure item that may be changed by an outside command and/or entity. The various infrastructure effectors may allow for infrastructure effector measurements to be made from the infrastructure items. As an example, infrastructure effectors measurements may include lighting values, moisture levels, etc. For example, controller108may continuously monitor and receive signals from sensors110. Using the signals, controller108may determine an illumination value for streetlights in sector112A. For instance, controller108may convert the signals to lumens and/or a lux value using calibration formulas and/or lookup tables. Further using the signals, controller108may actuate switches, relays, rheostats, etc. of infrastructure item106to increase or decrease illumination as disclosed herein.

Controller108may store in a memory an infrastructure quality associated with autonomous vehicle104. The infrastructure quality standard may be initially received from autonomous vehicle104. The infrastructure quality standard may also be a regulatory standard set by a governing body. For example, a regulatory body and/or the manufacture of autonomous vehicle104may publish a minimum illumination level in which autonomous vehicle104is permitted to operate in a fully autonomous mode. The infrastructure quality standard may also include tiers for different levels of autonomous operations. For instance, for fully autonomous operations, an illumination of X lux may be specified. For driver assisted autonomous operations an illumination of Y lux may be specified, where Y is less than X. For illumination values below Y, autonomous operations may be prohibited.

The infrastructure quality standard may be an average of infrastructure qualities received by controller108from a plurality of autonomous vehicles. For example, different autonomous vehicles may have different sensors and/or generational algorithms used to control the vehicles. As a result, each of the autonomous vehicles may require a different illumination level in order to operate in various states of autonomous operations. For instance, a newer model vehicle may require less illumination for fully autonomous operations due to being equipped with newer sensors and/or algorithms than an older model vehicle that may have less sensitive sensors and/or older algorithms.

Each of the autonomous vehicles104may transmit infrastructure qualities as illumination levels it requires for various degrees of autonomous operations. Controller108may combine the various infrastructure qualities to arrive at a standard infrastructure quality. The combination of the various infrastructure qualities may be an average of the illumination values for each of the vehicle. The standard infrastructure quality may be a weighted average of the various illumination values. For example, lower illumination values received may be given a greater weight when determining the standard infrastructure quality to allow controller108to dynamically adjust infrastructure items106to accommodate more vehicles.

Once the infrastructure quality and standard infrastructure quality are determined, controller108may determine a deviation of the infrastructure quality from the standard infrastructure quality. For example, if the infrastructure quality as determined using environmental data is equal to W lux and the standard infrastructure quality as determined from data received from the autonomous vehicles, a regulatory body, etc. is Z lux the deviations may be equal to W minus Z.

After determining the deviation, controller108may change a setting of infrastructure item106to minimize the deviation from the standard infrastructure quality. For example, if the illumination of sector112A is below the standard infrastructure quality (i.e., W−Z<0), then controller108may increase an intensity of light emitted by infrastructure item106to decrease the deviation. If the illumination of sector112A is too bright (i.e., W−Z>0), then controller108may decrease the intensity of light emitted by infrastructure item106to conserve energy, decrease light pollution, and minimize a risk of light oversaturating onboard sensors of autonomous vehicle104.

As disclosed herein, controller108, or a collection of controllers assigned to various sectors, may continuously monitor environmental conditions and dynamically adjust infrastructure elements106based on traffic flow patterns. For example, as autonomous vehicle104travels within a first sector (e.g., a first block within a city) a first set of sensors may transmit environmental data to controller108and controller108may dynamically adjust infrastructure items106within the first sector. As autonomous vehicle104travels to a second sector (e.g., a second block within the city), a second set of sensors may transmit environmental data to controller108and controller108may dynamically adjust infrastructure items106within the second sector (e.g., sector112B). When there is no traffic within a sector controller108may dim lights to a preset level to save energy and minimize light pollution while still allowing some light.

The environmental data may also be transmitted to controller108from autonomous vehicle104. For example, onboard photocell sensors may detect illumination levels and transmit signals to controller108along with standard infrastructure quality data. Controller108may use the environmental data in determining the infrastructure quality as disclosed above.

As disclosed herein, controller108may also transmit a standard infrastructure quality to autonomous vehicles. For example, after determining the infrastructure quality using the environmental data, controller108may transmit the standard infrastructure quality to the autonomous vehicles. The autonomous vehicles may use the standard infrastructure quality to control a level of automation authorized. For example, based on the standard infrastructure quality, the autonomous vehicles may allow full automation, driver assisted automation, or no automation at all. For instance, autonomous vehicle104may require X lux for full automation, Y lux for assisted automation, and below Z lux autonomous vehicle104may not allow any automation. If controller108determines the standard infrastructure quality is greater than X, the autonomous vehicle may allow fully autonomous operations.

Using the sensors110, controller108may determine when autonomous vehicle104enters and leaves sector112A. This can be accomplished using global positioning system (GPS) data transmitted by autonomous vehicle104to controller108and/or by monitoring illumination patterns of autonomous vehicle104. For example, the autonomous vehicle104can transmit a notification to controller108to notify it that autonomous vehicle104will enter sector112A and provide an estimated time of arrival (ETA). Autonomous vehicle104may also transmit a notification to controller108upon leaving sector112A, which may be defined by a geofence.

FIGS.2A and2Bshow an example of infrastructure item106in accordance with at least one example of this disclosure. As shown inFIG.2A, infrastructure item106may be in a default configuration. The default configuration may include having lighting elements202illuminate a road204proximate infrastructure item106as indicated by lines206. Infrastructure item106may include additional lighting element208.

Upon determining that the quality of infrastructure effectors results in an infrastructure quality that has deviation greater than a tolerance allowed for a standard infrastructure quality, controller108may change a setting on lighting elements202and/or additional lighting element208. For example, as shown inFIG.2B, additional lighting element208may be activated to direct light over road204as indicated by lines210. In addition, controller108may rotate a reflecting element in lighting elements202and/or activate additional bulbs in lighting elements202to cast light onto road204as indicated by lines212. Once autonomous vehicle104is no longer proximate infrastructure element106, infrastructure item106may return to a default configuration as indicated inFIG.2A.

FIG.3shows an example method300in accordance with at least one example of this disclosure. Method300may begin at starting block302and proceed to stage.304where a controller, such as controller108, may receive environmental data. As disclosed herein, the controller may receive the environmental data from sensors located proximate infrastructure items, such as infrastructure items106, and located throughout one or more sectors, such as sectors112. In addition, the controller may receive environmental data from one or more autonomous vehicles, such as autonomous vehicle104.

At stage306, the controller may determine a quality of infrastructure effectors. For example, and as disclosed herein, the controller may use the environmental data to determine an illumination level. The controller may also determine a moisture level indicating a road may be slippery or otherwise have a reduced coefficient of static friction. Using the illumination and moisture levels the controller may also determine that the road is reflecting light increasing illumination of the infrastructure items.

At stage308, the controller may determine a standard infrastructure quality. As disclosed herein, the controller may retrieve the standard infrastructure quality from a memory when the standard infrastructure quality is a standard set by a regulatory body.

The controller may also determine a standard infrastructure quality using data obtained from autonomous vehicles located within the sector. For example, the controller may receive as input requirements of the autonomous vehicles as disclosed herein. For example, the autonomous vehicles may transmit requirements when transmitting environmental data to the controller. The autonomous vehicles may transmit the requirements without transmitting environmental data as well. Upon receiving the illumination requirements from the autonomous vehicles, the controller may determine the ideal lumens value for sensor i using a weighted average lumens per square meter value required by autonomous vehicles in the sector using Equation 1:

l⁢⁢mi*=1n⁢∑k=1n⁢wk×req⁡(A⁢Vk,i)Equation⁢⁢1where lmi* is the optimal value of an arbitrary light meter, i, such as sensors110, in a surface or façade in the sector of the smart city, and req(AVk, i) is the requirement of autonomous vehicle (AVk) of n autonomous vehicles currently in the sector for that sensor i.

A weight, w, may be applied to the estimate since some autonomous vehicles might have priority over others. For example, an autonomous vehicle having a medical emergency, such as an ambulance, may have priority over non-emergency vehicles. The weighted average may put more emphasis on configurations with greater light intensity requirements.

The standard may be determined such that lmi* is the maximum requirement among all the requirements from the autonomous vehicles as shown in Equation 2:

l⁢⁢mi*=maxa∈V⁢(r⁢e⁢q⁡(a,i))Equation⁢⁢2where V is the set of all autonomous vehicles currently in the sector. In other words, the controller may set the standard as the maximum illumination required for all of the autonomous vehicles in the sector to accommodate the autonomous vehicle requiring the brightest light.

Using the quality of effectors and the standard infrastructure quality, the controller may determine a deviation from the standard infrastructure quality at stage310. As disclosed herein, the deviation may be determined by subtracting the quality of the effectors from the standard infrastructure quality. If the deviation is within a predetermined tolerance (312), method300may return to stage304to continuously monitor the quality of effectors for the sector.

If the deviation is outside the predetermined tolerance (312), method300may proceed to stage314where a setting of one or more infrastructure items located in the sector may be changed. For example, and as disclosed herein, a signal may be transmitted by the controller to the infrastructure items located in the sector to increase or decrease an intensity of light emitted by the infrastructure items. The controller may also transmit signals to activate additional lighting elements, rotate reflecting elements, etc.

After changing the setting, method300may proceed to stage316where the quality of the infrastructure effectors and/or the standard infrastructure quality may be transmitted to the autonomous vehicles in the sector. For example, the quality of the infrastructure effectors may be transmitted to the autonomous vehicles. The autonomous vehicles may use the quality of the infrastructure efforts to adjust a level of automation allowed and/or calibrate: onboard sensors.

The controller may also transmit the standard infrastructure quality that has been calculated to the autonomous vehicles. The standard infrastructure quality may act as a substitute for the quality of the infrastructure effectors. For example, once the controller adjusts the settings on the infrastructure items, the standard infrastructure quality and the quality of the infrastructure effectors may be equal. From stage316, method300may return to stage304to continuously monitor the quality of the effectors for the sector and adjust the settings of the infrastructure items as needed.

While method300has been describe in a particular order, one of ordinary skill in the art will understand, in view of this disclosure, that the various stages of method300may be rearranged and/or omitted. For example, the controller may transmit the quality of the effectors and/or standard infrastructure quality to the autonomous vehicles (316) before changing the setting of the infrastructure items (314). In addition, the transmission of the quality of the effectors and/or standard infrastructure quality to the autonomous vehicles (316) may be omitted.

A processor subsystem may be used to execute the instruction on the readable medium. The processor subsystem may include one or more processors, each with one or more cores. Additionally, the processor subsystem may be disposed on one or more physical devices. The processor subsystem may include one or more specialized processors, such as a graphics processing unit (GPU), a digital signal processor (DSP), a field programmable gate array (FPGA), or a fixed function processor.

Circuitry or circuits, as used in this document, may comprise, for example, singly or in any combination, hardwired circuitry, programmable circuitry such as computer processors comprising one or more individual instruction processing cores, state machine circuitry, and/or firmware that stores instructions executed by programmable circuitry. The circuits, circuitry, or modules may, collectively or individually, be embodied as circuitry that forms part of a larger system, for example, an integrated circuit (IC), system on-chip (SoC), desktop computers, laptop computers, tablet computers, servers, smart phones, etc.

As used in any embodiment herein, the term “logic” may refer to firmware and/or circuitry configured to perform any of the aforementioned operations. Firmware may be embodied as code, instructions or instruction sets and/or data that are hard-coded (e.g., nonvolatile) in memory devices and/or circuitry.

“Circuitry,” as used in any embodiment herein, may comprise, for example, singly or in any combination, hardwired circuitry, programmable circuitry, state machine circuitry, logic and/or firmware that stores instructions executed by programmable circuitry. The circuitry may be embodied as an integrated circuit, such as an integrated circuit chip. In some embodiments, the circuitry may be formed, at least in part, by the processor circuitry executing code and/or instructions sets (e.g., software, firmware, etc.) corresponding to the functionality described herein, thus transforming a general-purpose processor into a specific-purpose processing environment to perform one or more of the operations described herein. In some embodiments, the processor circuitry may be embodied as a stand-alone integrated circuit or may be incorporated as one of several components on an integrated circuit. In some embodiments, the various components and circuitry of the node or other systems may be combined in a system-on-a-chip (SoC) architecture

Example computer system400includes at least one processor402(e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both, processor cores, compute nodes, etc.), a main memory404and a static memory406, which communicate with each other via a link408(e.g., bus). The computer system400may further include a video display unit410, an alphanumeric input device412(e.g., a keyboard), and a user interface (UI) navigation device414(e.g., a mouse). In one embodiment, the video display unit410, input device412and UI navigation device414are incorporated into a touch screen display. The computer system400may additionally include a storage device416(e.g., a drive unit), a signal generation device418(e.g., a speaker), a network interface device420, and one or more sensors (not shown), such as a global positioning system (GPS) sensor, compass; accelerometer, pyrometer, magnetometer, or other sensor.

The storage device416includes a machine-readable medium422on which is stored one or more sets of data structures and instructions424(e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions424may also reside, completely or at least partially, within the main memory404, static memory406, and/or within the processor402during execution thereof by the computer system400, with the main memory404, static memory406, and the processor402also constituting machine-readable media.

ADDITIONAL NOTES