Autonomous mobile attenuator system

Aspects of the present disclosure relate to an autonomous mobile attenuator system for mitigating vehicular collisions. The system includes one or more mobile attenuators that receive data indicating a need for deployment from one or more sensors. The one or more mobile attenuators perform a collision risk assessment on the received data to determine a probability of a potential vehicle collision. The one or more mobile attenuators determine the probability of the potential vehicle collision exceeds a predetermined risk threshold value. The one or more mobile attenuators determine a predicted location for the potential vehicle collision. The one or more mobile attenuators proceed to the predicted location to mitigate the potential vehicle collision.

BACKGROUND

The present disclosure relates generally to the field of vehicle collision safety mechanisms, and more specifically, to an autonomous mobile attenuator system for reducing the risk of injury during an automobile accident.

Automobile accidents involving a vehicle striking another object are a frequent occurrence on highways and roads. In many instances, an automobile accident occurring at high speed results in serious injury or even death to the occupants of the vehicle. To reduce injury occurring from automobile accidents, specifically those involving collisions with barriers, stationary construction vehicles, and the like, vehicle impact attenuators are typically used. Vehicle impact attenuators, also known as fitch barriers, often consist of sand filled barrels placed in a linear or triangular arrangement at the end of a guard rail between a highway and an exit lane along the most probable line of impact. The impact attenuators in the front of the arrangement typically contain the least amount of sand, with each successive barrel containing more, so that when a vehicle collides with the barrels they shatter, the kinetic energy of the vehicle is dissipated by scattering the sand, and the vehicle decelerates smoothly instead of violently striking a solid obstruction. This method of dissipating the kinetic energy of the vehicle using impact attenuators significantly reduces risk of injury to the occupants.

SUMMARY

Embodiments of the present disclosure include an autonomous mobile attenuator system for mitigating potential vehicular collisions. The system may include one or more mobile attenuators, wherein each of the one or mobile attenuators includes a processor. The processor is configured to execute steps of a method. The processor may receive, from one or more sensors, data indicating an estimation of need for deployment of the one or more mobile attenuators. The processor performs a collision risk assessment on the data to determine a probability of a potential vehicle collision. If the probability of the potential vehicle collision exceeds a predetermined risk threshold value, the processor will deploy the one or more mobile attenuators to a predicted location to mitigate the potential vehicle collision.

Embodiments of the present disclosure may be directed toward a method for deploying one or more mobile attenuators of an autonomous mobile attenuator system to mitigate potential vehicular collisions. One or more mobile attenuators may receive data indicative of an errant vehicle from one or more sensors. The one or more mobile attenuators may perform a collision risk assessment on the data to determine a probability of a potential vehicle collision. The one or more mobile attenuators may determine the probability of the potential vehicle collision exceeds a predetermined risk threshold value. The one or more mobile attenuators may deploy and proceed to a predicted location to mitigate the potential vehicle collision.

Embodiments of the present disclosure may be directed toward a computer program product for mitigating a vehicular collision using an autonomous mobile attenuator system. The computer program product includes a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to receive from one or more sensors, data indicating an estimation of need for deployment of one or more mobile attenuators. The processor may perform a collision risk assessment on the data to determine a probability of a potential vehicle collision. If the probability of a potential vehicle collision exceeds a predetermined risk threshold value, the processor will deploy the one or more mobile attenuators to a predicted location to mitigate the potential vehicle collision.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to the field of vehicle collision safety mechanisms, and more particularly to an autonomous mobile attenuator system for reducing the risk of injury during vehicle collisions. While the present disclosure is not necessarily limited to such applications, various aspects of the disclosure may be appreciated through a discussion of various examples using this context.

Impact attenuators or barriers are often placed in predicted positions along a road to reduce the amount of automobile accidents specifically involving collisions with stationary barriers, construction vehicles, and the like. Impact attenuators may utilize different means to intercept and stop a vehicle in motion. Vehicle impact attenuators, also known as fitch barriers, often consist of weighted barrels placed in a linear or triangular arrangement at the end of a guard rail between a highway and an exit lane along the most probable line of impact.

The impact attenuator is typically filled with a dispersible material, such as sand, gravel, or liquid. Impact attenuators may be categorized by the method used to dissipate kinetic energy. Early models use momentum transfer to stop a vehicle. The impact attenuators would be placed in successive rows where momentum of an errant vehicle is transferred to the dispersible material when the impact attenuator is struck during a collision, reducing the speed of the impacting vehicle such that the vehicle eventually stops.

Newer versions of impact attenuators use alternative methods to slow and/or stop a vehicle, such as material deformation or friction. For example, some impact attenuators use crushable materials that create a crumple zone for absorbing energy. Other types of attenuators use a corrugated steel guard rail section or split a steel box beam. However, these various examples of impact attenuators fail to be mobile or autonomous, such that the attenuator can move to various locations without the need of human intervention.

Embodiments of the present disclosure provide an autonomous mobile attenuator system that deploys one or more mobile attenuators to an area determined to have a high risk of a vehicle collision. The system includes one or more sensors that send data to the mobile attenuators to be analyzed for risk of a potential vehicle collision. If a predetermined risk threshold is exceeded, the system deploys the mobile attenuators to a predicted location (e.g., GPS coordinates) associated with the potential vehicle collision. When at the predicted location, the mobile attenuators may arrange in a pattern to best mitigate the potential vehicle collision. This pattern may be any suitable pattern (e.g., triangular, linear, etc.) that efficiently dissipates the kinetic energy (e.g., disperses the impact force over time) of an errant vehicle when the one or more mobile attenuators are struck.

In some embodiments, the autonomous mobile attenuator system may be deployed to redirect an errant driver to prevent a potential vehicle collision. In this way, one or more mobile attenuators may intercept (e.g., stop, slow, or redirect) a vehicle at any point along the path leading to the predicated location of the potential vehicle collision. In other words, the attenuator system may be deployed to create a mobile barrier that causes the driver to reduce his speed or turn onto a safer road. For example, if a driver is heading the wrong way on a one-way street, the mobile attenuator system may deploy a plurality of impact attenuators that line up to gradually direct the driver into a lane from which the driver can be forced onto another street.

Referring now toFIG. 1, shown is a block diagram of an autonomous mobile attenuator system100, in accordance with embodiments of the present disclosure. In the illustrative embodiment, the autonomous mobile attenuator system100includes one or more mobile attenuators102, one or more sensors120, and one or more host devices130communicatively coupled via a network150. The network150may be any type of computing network, such as a cloud computing network.

The network150may be substantially similar to, or the same as, network50described inFIG. 5andFIG. 6. In some embodiments, the network can be implemented using any number of any suitable communications media. For example, the network may be a wide area network (WAN), a local area network (LAN), an internet, or an intranet. In certain embodiments, the various systems may be local to each other, and communicate via any appropriate local communication medium. For example, the mobile attenuator102may communicate with sensor120and the host device130using a local area network (LAN), one or more hardwire connections, a wireless link or router, or an intranet. In some embodiments, the various systems may be communicatively coupled using a combination of one or more networks and/or one or more local connections. For example, the mobile attenuator102may communicate with a docking station (not shown) using a wireless network (e.g., a router), and the docking station may be hardwired (e.g., connected with an Ethernet cable) to the host device130.

The host device130may be any type of computer system and may be substantially similar to computer system1101ofFIG. 4. In some embodiments, the host device130may allow for the manual activation of the system100when deployment is determined necessary by a user. For example, the deployment may be initiated from a remote facility (e.g., a police car, a 911 call center, an authorized helicopter, an authorized drone, or combination of modalities that are in “agreement” that an action may be taken, etc.) by a user utilizing the host device130. Once deployment is initiated by a user, the autonomous nature of the autonomous mobile attenuator system can ensue. The deployment may be based on the need to intercept or redirect a vehicle (e.g. a vehicle travelling the wrong way), e.g., to prevent a head on collision.

The host device130may provide updates to the mobile attenuator102. For example, the host device130may send updated statistical information generated from a history of collisions associated with a specific location. This statistical information may be used to adjust algorithms used for collision risk assessments and criteria for deployment of the mobile attenuator102. The host device130may send the mobile attenuator102current traffic information and other geographic data obtained from an internet database or website to aide in predicting a location of a potential vehicle collision.

Sensor120may be any type of sensor configured to generate data to be analyzed for risk indicative of a potential vehicle collision. For example, data may be generated from motion sensors placed along a highway, such that the sensors120can determine a vehicle going in the wrong direction, the location of an accident, or any type of road hazard, etc.

In some embodiments, sensor120may be an optical or biometric sensor that can detect cognitive function of a driver of a vehicle. For example, sensor120may be a dashboard camera located within a vehicle and may detect facial expressions indicating a drowsy driver. The dashboard camera may be linked to the autonomous mobile attenuator system100, such that data can be transmitted to the system100to be analyzed for risk of a potential accident. If the combination of all data received from each sensor120results in a cumulative risk that is determined to be above a threshold risk value, the system100may deploy one or more mobile attenuators102to mitigate a potential vehicle collision.

Sensor120may include various smart devices. For example, data generated from sensor120may include biometric data obtained from a communicatively coupled wearable unit, such as a smartwatch. Data from a driver's smartwatch may be used to track biometric data such as pulse rate or blood sugar level, which may be additionally used to indicate the current state of the driver. In some embodiments, other devices such as a smartphone may further provide data to be used for risk assessment of a potential accident. For example, data taken from a driver's smartphone calendar may indicate they have been awake for a long period of time based on scheduling, appointments, events, etc. Further, drug prescription data may be obtained via an internet database, indicating a driver may be under the influence of prescription drugs. If known, the estimated cognitive state (e.g., drunk, drowsy, distracted, under the influence of prescription medicine) of the errant driver (e.g. driver travelling the wrong way on a highway) may be used to change characteristics of the deployment of the mobile attenuator102(e.g., how, where, and when the deployment takes place). In some embodiments, the cognitive state of a driver that may potentially become an errant driver may be further predicted through social network monitoring. For example, if a user (prior to driving) writes on a message board or social networking site that they are planning a long trip, tired, or upset, the system100may utilize this information when determining the cognitive state of the driver.

In some embodiments, sensor120may comprise impact sensors to detect various vehicle activity. For example, an impact sensor may detect if the vehicle has brushed a sidewall on a highway. In another embodiment, sensor120may comprise gyroscope or inertial sensors to detect sporadic driving by a driver.

Mobile attenuator102may be any type of mobile vehicle configured to transport an impact attenuator104. In one embodiment, the mobile attenuator102is a dedicated self-driving vehicle, such as a truck, car, or drone. The impact attenuator104may be any type of impact barrier. For example, the impact attenuator104may be shaped as a barrel, wherein barrel may be filled with sand, gravel, or liquid in order to dissipate energy when the impact attenuator104is struck by a vehicle. The impact attenuator104may include curved rigid tubular structural members that are designed to buckle upon application of forces resulting from vehicular impact.

In alternative embodiments, the impact attenuator104may contain a bladder, wherein the bladder deflates and absorbs the force of a collision when struck by a vehicle. The impact attenuator104may include a frame adapted to be mounted on the mobile attenuator102. For example, a sliding mechanism mounted on the frame may be configured to allow the impact attenuator104to slide in response to a vehicle impact. The sliding mechanism may include a steel cable or strap disposed in an angled slot or tube, such that kinetic energy created when the impact attenuator104is struck is converted into heat energy by utilizing friction, thereby slowing an errant vehicle. In an alternative embodiment, the frame disposed on the mobile attenuator102may include collapsible telescopic energy absorbing members (e.g., springs, hydraulic shock absorbers, etc.) positioned between the slider and the frame to absorb energy as the slider telescopes relative to the frame.

In one embodiment, the association between the mobile attenuator102and the impact attenuator104may be temporary. In this arrangement, the impact attenuator104is deployed by the mobile attenuator102, and the mobile attenuator102is configured to move away from the impact attenuator104to prevent risks associated with collision of the mobile attenuator's102mechanical components upon impact with an errant vehicle. For example, mobile attenuator102may include multiple impact attenuators104that may be placed by the mobile attenuator in stationary positions along a highway. Once placed in an appropriate position to mitigate a potential collision, the mobile attenuator102may move away from the impact attenuators104, allowing for only the impact attenuators104to be struck by a vehicle.

In some embodiments, the impact attenuator104is permanently secured to the mobile attenuator102. For example, the impact attenuator104itself is mobile and may include computer system106, a motor and a set of retractable wheels, such that impact attenuator104may position itself autonomously. Once positioned, the wheels on the impact attenuator104may retract allowing the impact attenuator to remain in a stationary position. It is contemplated that the computer system, motor and wheels are disposed in an area where the least damage will be incurred when the impact attenuator104is struck by a vehicle.

In another embodiment, the impact attenuator104is adjustable in height, such that the center of gravity of the dispersible mass (sand, water, etc.) within the impact attenuator may be placed at the same height of the center of gravity of an errant vehicle. In this way, the impact attenuator104can be positioned to the height in which the impact attenuator will most efficiently stop or slow the errant vehicle. This may be done by utilizing a mechanical device disposed on the impact attenuator, such as a hydraulic lifting mechanism which is communicatively coupled to the mobile attenuator102. For example, the type of errant vehicle may be determined during the risk assessment performed by computer system106(or by host device130). Once determined, the mobile attenuator102may adjust the height of a coupled impact attenuator104to match the predicted center of gravity of the determined type of errant vehicle. In some embodiments, the height of a particular impact attenuator104may be based on its deployment position within a group of impact attenuators104and the shape of the deployment. For example, if multiple impact attenuators104are deployed in a linear fashion, each row of impact attenuators104may have a different deployment height (e.g., the front row may be the highest, followed by the second row, and so on).

Computer system106may be substantially similar to, or the same as, computer system1101described inFIG. 4. The computer system106includes a processor108and may include a global positioning system (GPS)110. The processor is configured to perform risk computation analysis on data received from sensor120and host device130. The risk computation may include a collision risk assessment to determine the probability of a potential vehicular collision. The risk computation may be based on the current traffic data, data indicating the type and location of an errant vehicle as determined by one or more sensors120, geographic data, weather data, road surface conditions (e.g., icy, rain soaked, flooded, etc.) the time of the day, etc. The processor108may determine how much risk an errant vehicle would create to lead to a potential vehicle collision (e.g., the likelihood of the vehicle causing an accident and the potential damage of the resulting accident). Based on that risk, the processor108dynamically determines the amelioration method to use or the extent to which a method is used to stop, or slow, the errant vehicle from moving further.

Further, it is contemplated that computer system106may include an artificial intelligence system that can learn based on a history of accidents, success in ameliorating accidents (including deaths and damages), and various uses during ambient conditions of weather, road conditions, driver state, etc. For example, the artificial intelligence system can include one or more artificial neural networks. As more data is learned by the system, such as data related to accident history, successful deployments of the attenuators, and unsuccessful deployments, the weights of the neural network can be adjusted, automatically, by the processor. Over time, the system can become more accurate in determining when the deploy the impact attenuators, where to deploy the impact attenuators, and what pattern to use based on, among other things, the type and size of errant vehicle, the type of driver condition (e.g., drowsy, drunk, etc.), the weather, traffic patterns, and road surface conditions (e.g., icy, rain soaked, flooded, etc.).

GPS110may be included to monitor the location of each of the one or more mobile attenuators102linked to the system100. In this way, if the determined risk of a potential vehicle collision is high, multiple mobile attenuators may be used to mitigate the collision. By utilizing the GPS location of each mobile attenuator102, the attenuators in the closest position to the potential vehicle collision location can be deployed (e.g., from a docking station on the side of a highway) to quickly mitigate the potential collision. Further, GPS110may be utilized to position each mobile attenuator in a specific pattern during placement. Although the phrase GPS location is used herein, other means of location may be employed, including the integration of car-to-car distance information, visual information based on computer vision systems, etc., to improve positional accuracy. Other examples of positional augmentation systems include the European Geostationary Navigation Overlay Service (EGNOS), Differential GPS (DGPS), inertial navigation systems (INS), vehicular LIDAR systems, and the like.

In some embodiments, mobile attenuator102may further include an immobilization unit112. The immobilization unit112is configured to immobilize, or partially immobilize, an errant vehicle. In one embodiment, the immobilization unit112is a tack strip configured to pierce the errant vehicle's tires. In this way, the mobile attenuator may deploy the immobilization unit112to aide in slowing or stopping a vehicle. In another embodiment, the immobilization unit112is configured as a chemical liquid that is dispersed and immediately solidifies when contacting the tires of the errant vehicle. In this way, the immobilization unit112may jam the wheels of the errant vehicle thereby slowing the vehicle prior to impacting the mobile attenuator102.

Referring now toFIG. 2, shown is a flow diagram of an example process200for deployment of one or more mobile attenuators, in accordance with embodiments of the present disclosure. The process200may be performed by processing logic that comprises hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processor to perform hardware simulation), firmware, or a combination thereof. In some embodiments, the process200is a computer-implemented process. The process200may be performed by a processor108exemplified inFIG. 1. In some embodiments, a processor of a host device (e.g., host device130inFIG. 1) may perform the process200.

The process200begins by the processor receiving data indicating an estimation of need for deploying one or more mobile attenuators to a location. This is illustrated by step205. The data indicating an estimation of need is received by the processor from one or more sensors communicatively coupled to the one or more mobile attenuators. For example, a vehicle traveling in the wrong direction on a highway will activate the one or more sensors to collect data on the vehicle. The data received by the processor will be assessed to determine a need to stop or redirect the vehicle.

Once the processor receives the data from the one or more sensors, the processor analyzes the estimation of need for a potential collision risk. This is illustrated in step210. In one embodiment, the received data may be analyzed by the processor by performing a risk-based computation. For example, the processor may utilize current traffic patterns obtained from the sensors or via information obtained from the host device.

The process200continues by determining if the collision risk exceeds a predetermined threshold. This is illustrated in step215. If the collision risk is determined to be below the threshold, the one or more mobile attenuators will not be deployed (e.g., remain docked in a docking station). This is illustrated in step220. For example, if a sensor detects movement from an animal or another small object, the risk of a vehicle collision may be minimal and fall under the predetermined risk threshold. Thus, the processor would determine that there is no need to deploy any mobile attenuators. Alternatively, if the processor determines that the collision risk exceeds the predetermined risk threshold, then the processor will deploy one or more mobile attenuators to a predicted location associated with the potential vehicle collision determination. This is illustrated in step225. In some embodiments, the deployed mobile attenuators may intercept (e.g., stop, slow, or redirect) an errant vehicle at any point along a path traveled to the predicted location of the potential vehicle collision. For example, the mobile attenuators may be deployed to redirect an errant vehicle prior to the predicted location of the vehicle collision, such that no collision occurs. In some embodiments, the predicated location may be determined through the use of GPS where the mobile attenuator further refines the position/placement of the impact attenuators via optical sensors or other positioning systems (e.g., trilateration, RSSI positioning, etc.)

Based on the collision risk, the processor dynamically determines the amelioration method to use or the extent to which a method is used to stop, slow, or redirect the errant vehicle from moving further. For example, the determined speed and size of the errant vehicle will factor into how many mobile attenuators are deployed and what pattern they should be deployed in. If sensors indicate that the errant vehicle is traveling at a high speed, more mobile attenuators will be needed to stop or slow the errant vehicle. In some embodiments, the source of the data may further dictate the type of response the mobile attenuator system may use. For example, if a police department indicated an errant vehicle is a threat, the immobilization unit (e.g., tack strip) may be used.

Additionally, in some embodiments the processor may determine what types of mobile attenuators to deploy based on the collected data. For example, if the size of the errant vehicle is determined to be large based on the collected data, a specific type of mobile attenuator (e.g., a mobile attenuator having a heavier sand filled impact attenuator) may be deployed to intercept the larger vehicle. Further, the number of mobile attenuators needed for deployment and the number of mobile attenuators available may be factored in when determining the appropriate deployment plan. For example, if the processor determines that only mobile attenuators with lighter impact attenuators are available, more mobile attenuators may be deployed to stop the errant vehicle.

The process200continues by instructing the mobile attenuators to arrive at the predicted location of the potential vehicle collision, as determined by the risk computation. This is illustrated in step230. In some embodiments, each mobile attenuator may be equipped with an immobilization unit to further mitigate a potential collision. The immobilization unit may be used to slow the errant vehicle prior to coming in contact with the one or more mobile attenuator. In such an embodiment, the process200continues by determining if further immobilization is needed to stop an errant vehicle. This is illustrated in step235. If the processor determines that the collision risk is significant, the mobile attenuator will deploy the immobilization unit. This is illustrated in step240. For example, a vehicle traveling at a high rate of speed may be slowed by the immobilization unit (e.g., utilizing a chemical liquid or a tack strip) that is deployed in front of the one or more mobile attenuators. However, the immobilization unit may not fully decelerate the vehicle prior to contacting the mobile attenuators, thus the attenuators may still be needed to mitigate a collision.

If further immobilization is determined not to be needed by the processor, then the process200continues by determining a swarm pattern to best mitigate the collision risk. This is illustrated in step245. For example, depending on the number of mobile attenuators deployed relative to the size and speed of the errant vehicle, the processor will determine an appropriate pattern (e.g., a linear or triangular pattern) for placement of the mobile attenuators at the predicated location. Once the swarm pattern is determined, the process200continues by arranging the one or more mobile attenuators in the determined pattern. This is illustrated in step250. The placement of each mobile attenuator may be determined by inter-attenuator communication between each of the mobile attenuators. For example, some mobile attenuators may be equipped with lighter impact attenuators, which will be placed in front of the determined pattern, whereas mobile attenuators containing heavier impact attenuators will be placed near the rear of the pattern in order to properly dissipate the kinetic energy of an errant vehicle. Once the mobile attenuators are in place, they will remain in position until the potential vehicle collision has been mitigated.

The process200continues by the mobile attenuators receiving data indicating the potential vehicle collision has been mitigated. This is illustrated in step255. This data may indicate that the errant vehicle struck the one or more mobile attenuators. If no vehicle collision occurs and the potential vehicle collision is no longer a risk based on received updated data, the process200continues by instructing the one or more attenuators to return to a docking station. This is illustrated in step260. In some embodiments, the docking station may be used to charge an electric battery of the mobile attenuators and further update the system with data gathered from the mobile attenuator during deployment.

In some embodiments, the order of operations for process200may vary and additional or fewer steps may be included. For example, in some embodiments, the mobile attenuators may be in motion on a highway prior to determining a need to intercept a vehicle. In such an instance, the mobile attenuators would not be deployed from a docking station. Further, in some embodiments, the mobile attenuators may remain in motion on a highway once a potential collision has been mitigated. Thus, the mobile attenuators may not need to return to a docking station.

Referring now toFIG. 3A, shown is an example triangular swarm pattern configuration for multiple mobile attenuators, in accordance with embodiments of the present disclosure. In the illustrative embodiment, swarm pattern340includes a set of mobile attenuators302A-302F (collectively referred to as mobile attenuator302) arranged in a triangular pattern. Each mobile attenuator302receives data from sensor320disposed along road300, indicating that an errant vehicle310needs to be intercepted or diverted to mitigate or prevent a vehicle collision. The data received from sensor320may include variables of the errant vehicle, such as size, speed, cognition of the driver. Once received, this information is processed by each mobile attenuator302. If deploying each mobile attenuator302is determined to be necessary to mitigate a potential collision risk, each mobile attenuator302deploys from docking station330to arrive at a determined location where the probability of intercepting the vehicle is the highest. Based on the determined variables received from the sensor320, each mobile attenuator302arranges itself in a determined swarm pattern. For example, mobile attenuators302A-302F are arranged in a triangular swarm pattern340to divert a smaller vehicle310. It is contemplated that a triangular pattern may be used to divert the errant vehicle to a preferred direction (e.g., right or left) to mitigate damages. In some embodiments, each mobile attenuator302may include sensors (e.g., impact sensors) to measure the force when impacted by an errant vehicle. In this way, future swarm patterns could be refined based on what was required (e.g., if the swarm340utilized too many or too little attenuators) to stop or slow a vehicle having a certain momentum.

Referring now toFIG. 3B, shown is an example linear swarm pattern configuration for multiple mobile attenuators, in accordance with embodiments of the present disclosure. In the illustrative embodiment, swarm pattern341includes a set of mobile attenuators302A-302J (collectively referred to as mobile attenuator302) arranged in a linear pattern. Sensor320sends data to each mobile attenuator302indicating a large errant vehicle315is traveling in a wrong direction on road300. Because the vehicle data indicates a larger errant vehicle, the mobile attenuator302deploys from docking station330and arranges into a linear swarm pattern341. In the illustrative embodiment, the arrangement of mobile attenuators302are such that the first impact attenuator may contain the least amount of dispersible material, with each successive attenuator containing more, so that when the larger errant vehicle315collides with the impact attenuators they shatter, and the kinetic energy is dissipated by scattering the dispersible material and the vehicle315decelerates smoothly instead of violently striking a solid obstruction, reducing the risk of injury to the occupants.

Referring now toFIG. 4, shown is a high-level block diagram of an example computer system1101(e.g., host device130, computer system106, sensor120, sensor320, etc.) that may be used in implementing one or more of the methods, tools, and modules, and any related functions, described herein (e.g., using one or more processor circuits or computer processors of the computer), in accordance with embodiments of the present disclosure. In some embodiments, the major components of the computer system1101may comprise one or more CPUs1102, a memory subsystem1104, a terminal interface1112, a storage interface1116, an I/O (Input/Output) device interface1114, and a network interface1118, all of which may be communicatively coupled, directly or indirectly, for inter-component communication via a memory bus1103, an I/O bus1108, and an I/O bus interface unit1110.

The computer system1101may contain one or more general-purpose programmable central processing units (CPUs)1102A,1102B,1102C, and1102D, herein generically referred to as the CPU1102. In some embodiments, the computer system1101may contain multiple processors typical of a relatively large system; however, in other embodiments the computer system1101may alternatively be a single CPU system. Each CPU1102may execute instructions stored in the memory subsystem1104and may include one or more levels of on-board cache. In some embodiments, a processor can include at least one or more of, a memory controller, and/or storage controller. In some embodiments, the CPU can execute the process included herein (e.g., process200).

Although the memory bus1103is shown inFIG. 4as a single bus structure providing a direct communication path among the CPUs1102, the memory subsystem1104, and the I/O bus interface1110, the memory bus1103may, in some embodiments, include multiple different buses or communication paths, which may be arranged in any of various forms, such as point-to-point links in hierarchical, star or web configurations, multiple hierarchical buses, parallel and redundant paths, or any other appropriate type of configuration. Furthermore, while the I/O bus interface1110and the I/O bus1108are shown as single units, the computer system1101may, in some embodiments, contain multiple I/O bus interface units1110, multiple I/O buses1108, or both. Further, while multiple I/O interface units are shown, which separate the I/O bus1108from various communications paths running to the various I/O devices, in other embodiments some or all of the I/O devices may be connected directly to one or more system I/O buses.

It is noted thatFIG. 4is intended to depict the representative major components of an exemplary computer system1101. In some embodiments, however, individual components may have greater or lesser complexity than as represented inFIG. 4, components other than or in addition to those shown inFIG. 4may be present, and the number, type, and configuration of such components may vary.

One or more programs/utilities1128, each having at least one set of program modules1130may be stored in memory1104. The programs/utilities1128may include a hypervisor (also referred to as a virtual machine monitor), one or more operating systems, one or more application programs, other program modules, and program data. Each of the operating systems, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Programs1128and/or program modules1130generally perform the functions or methodologies of various embodiments.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Workloads layer90provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; risk assessment93; data analytics processing94; transaction processing95; and mobile desktops96.