Invented by Mark V. Slusar, Allstate Insurance Co
The Allstate Insurance Co invention works as follows
Systems, apparatus and methods for determining a drafting characteristics of a draft relationship through vehicle-to vehicle communication are disclosed. The drafting characteristic can include one or more vehicle spacing between a 1st vehicle and 2nd vehicle, vehicle speed and vehicle type. The amount of an autonomous drone reward can be calculated using vehicle driving data or other information. A vehicle that is involved in a drafting agreement, in addition to or separate from an autonomous drone relationship, may also be financially rewarded. Moreover, aspects of the disclosure related to determining ruminative rewards and/or aspects of vehicle insurance procurement/underwriting.
Background for Reward System related to a Vehicle-to-Vehicle Communication System
Many vehicles have sensors and computer systems that monitor and control the vehicle’s operation, driving conditions and driving functions. Modern vehicles can monitor fuel consumption, optimize engine operation for higher fuel efficiency, detect and correct a loss in traction on icy roads, and detect a collision to automatically contact emergency services. Different vehicle-based communications systems enable vehicles to communicate with devices within or outside the vehicle. Bluetooth, for example, can enable communication between a vehicle and a driver’s phone. Telematics systems can be configured to connect to vehicle computers and sensor information, such as on-board diagnostics (OBD), to transmit data to an internal display, a mobile device or a computer. The data obtained from OBD and vehicle sensors has been used in a number of ways, including for maintenance, diagnosis and analysis. Vehicle-to-vehicle communication systems (V2V), which use data from nearby vehicles, can also be used to alert drivers of safety issues and possible collisions. Vehicles can also include autonomous driving systems, which assume or partially perform real-time driving tasks to operate the vehicle with no real-time input by a human driver.
When driving, drivers and vehicles can engage in a variety of driving behaviors. This includes a wide range of’social interactions’. With other vehicles and drivers. One example of social interaction is vehicle drafting, where one vehicle follows another to reduce drag and improve fuel economy. A social interaction example can be autonomous vehicle droning, where a vehicle is at least partially autonomously driven based on the driving style of a leading or pilot vehicle.
The following is a simplified overview to help you understand some of the key aspects of this disclosure. This summary does not provide a comprehensive overview of the disclosure. The summary is not intended to define the scope of disclosure or to highlight key elements. This summary is a simple presentation of some key concepts in the disclosure as a prelude for the detailed description.
Aspects” of the disclosure are related to computer devices and methods for receiving and transmitting data on driving, analyzing data on driving, and determining if a first vehicle has a drafting relation with at least one second vehicle, as well as allocating a reward for drafting based upon the drafting relation. Vehicle operational data can be used to determine a drafting characteristic for the drafting relation. Examples of drafting characteristics include vehicle spacing, vehicle type, or vehicle speed. The drafting characteristic can be used to determine a drafting property that is associated with the second or first vehicle. Examples of drafting properties include a drafting saving rate, a fuel saving amount, or a percentage increase in mileage-per-gallon. Examples of drafting rewards include cash payments, fuel credits, tax credits, rebates, carbon credits, and a portion or the entire drafting fuel saving amount associated with the vehicle driving analysis computer.
According to other aspects of this disclosure, an insurance policy’s property can be determined by receiving driving data from vehicles involved in an autonomous drone relationship. In such a relationship, a vehicle is able to drive at least partially autonomously based on the information provided by another vehicle. The driving data can be used to determine a characteristic of an autonomous droning relation. Examples of characteristics of an auto-droning relationship are identification of a leading vehicle and a drone, the time that a vehicle spends as the lead vehicle or drone, and the distance driven by a vehicle while it is the drone or lead vehicle. Examples of insurance policies include the premium of the insurance policy for the first policy, the deductible and the coverage amount. Aspects of disclosure include determining an auto-droning insurance factor based on vehicle operational data or a characteristic associated with the auto-droning relationship. The autonomous drone insurance factor can also be used to determine a property of the first policy by using the autonomous drone insurance factor.
The disclosure provides incentives for social interaction between drivers and vehicles. The additional description will reveal other features and benefits of the disclosure.
In the description of various embodiments that follow, references are made to the drawings which are a part of this document and which illustrate various embodiments which may be used. Other embodiments are possible.
As one skilled in the art will understand after reading the disclosure, the various aspects described can be embodied into a method, computer system, or computer program product. Those aspects can be embodied in a completely hardware embodiment, a fully software embodiment, or combining both software and hardware. Aspects may also take the form a computing device configured for performing specified actions. A computer program product may also be stored on one or more computer-readable media with computer-readable instructions or program code embedded in the media. Computer-readable storage media can include hard disks or CD-ROMs as well as optical storage devices and magnetic storage devices. “In addition, the various signals that represent data or events described herein can be transmitted between a source to a destination by electromagnetic waves travelling through signal-conducting medium such as metal cables, optical fibres, and/or Wireless transmission media (e.g. air and/or Space).
FIG. The block diagram 1 shows a computing device 100 in a driving analysis communication system (100) that can be used to illustrate one or more embodiments. The device 101 can have a processor for controlling the overall operation, which includes RAM 105 and ROM 107. It may also include an input/output unit 109 and a memory unit 115. Computing device 101 and one or more other devices (e.g. terminals 141 and 151) can correspond to multiple systems and devices. For example, driving analysis computing systems and devices configured to transmit and receive vehicle operational data and analyze vehicle operational data. The data is then used to determine driving characteristics, as well as various properties related driver rewards or vehicle insurance. Vehicle operational data may include data from sensors or OBD systems. Vehicle operations may also include driver-related data. Vehicle operational data may also include information about other vehicles nearby, collected, for instance, via V2V communications. In this document, driving data and vehicle operation data are used interchangeably.
The “Input/Output” (I/O), 109, may include a keypad, microphone, touch screen and/or stylus, through which the user of computing device 101 can provide input. It may also include a speaker to provide audio output, as well as a video display for textual, audiovisual, and/or graphic output. Software can be stored in memory unit 115 or other storage devices to give instructions to processors 103 to enable device 101 to perform different functions. Memory unit 115, for example, may be used to store software that is used by device 101. This includes an operating system, application programs, and an internal database. Memory unit 115 may include volatile or non-volatile memory for storing computer-executable data, instructions, and/or information. The processor 103, along with its associated components, may enable the driving analysis system to execute a set of computer-readable instruction to transmit or receive driving data from a vehicle, analyze driving information, determine driving characteristics based on driving data and determine the properties of insurance policies or driver rewards based upon the driving data.
The driving analytics computing device 101 can operate in a networked 100 environment supporting connections to remote computers such as terminals/devices 141 and 151. The driving analysis computing device 101 and terminals/devices 141 and 151, may be devices installed within vehicles, mobile devices which may travel inside vehicles, or external devices configured to receive vehicle and driving data. The driving analysis computing system 101, terminals/devices 141 and 151, may each be personal computers (e.g. laptops, desktops, tablet computers), web servers, databases servers, vehicle-based devices (e.g. on-board vehicle computer systems, short-range communication systems, telematics), or mobile communication devices. The network connections shown in FIG. The network connections shown in FIG. 1 include a local network (LAN), a wide-area network (WAN), and a wireless communications network 133. Other networks may also be included. The driving analysis computing device may be connected to LAN 125 when used in a LAN network environment through a network adapter 123. In a WAN network environment, a modem 127, or another means of communication over the WAN 129 (e.g. the Internet), may be included in the device 101. The device 101 can be used to communicate with wireless computing devices (e.g. mobile phones, short range vehicle communication systems, vehicle tracking devices) through one or more network devices (e.g. base transceiver station) on a wireless network 133.
The network connections are only intended to be illustrative. Other methods of creating a communication link between computers can be used. It is assumed that there are various network protocols, such as TCP/IP and Ethernet, FTP and HTTP, as well as wireless communication technologies, such as GSM and CDMA. The various computing devices and components of the driving analysis system described herein can be configured to use any of these protocols or technologies.
Additionally one or more application program 119 may be used by the driving analyze computing device 101 to include computer executable instruction (e.g. driving data analysis programs and driving characteristic algorithms; driving and insurance policy property algorithms and driver reward algorithm) for transmitting or receiving vehicle driving data and determining driving characteristics and determining different properties associated with a one or multiple vehicles or drivers and performing other functions as described in this document.
As used in this document, “a driving characteristic” can refer to one or several actions or events performed or recorded by a car. It may also include information derived or identified from vehicle operational data. For example, a driving characteristic could be a vehicle’s speed or gas mileage as determined from vehicle operational data. A driving characteristic can also include, for instance, a vehicle’s speed or gas mileage based on other operational data from the vehicle. A driving characteristic can be determined by driving data collected from a vehicle’s sensors and telematics devices, or additional data from nearby vehicles via vehicle-to vehicle (V2V), as discussed below. A driving characteristic can be associated with either a vehicle or driver or even a group of drivers or vehicles engaged in social interactions, such as an autonomous drone relationship.
FIG. The diagram in FIG. 2 shows an example of a driving system 200, which includes two vehicles, 210 and 220-, a server for driving analysis 250, and other related components. The components shown in FIG. The components shown in FIG. 2 can be implemented as hardware, software or a combination. Each component of the driving system 200 can include a computing system (or device) that includes some or all the structural components described for computing device 101.
The vehicles 210 and 220 of the driving analysis system may include automobiles, motorbikes, scooters or buses. They may also be recreational vehicles, boats or any other vehicle for which driving data can be collected. Vehicle operation sensors 211, 221 are included in the vehicles 210, 220. These sensors detect and record various vehicle conditions and operational parameters. Sensors 211 and 221, for example, may detect and record data corresponding the vehicle’s position (e.g. GPS coordinates), its speed and direction, its rates of acceleration and braking and gas mileage. They can also store specific instances of sudden braking and acceleration. Sensors 211 & 221 may also detect and store data from the 210 internal system of the car, such as airbag deployment, impact on the body, headlights, brake lights, door opening & closing, locking & unlocking, cruise controls, hazard light usages, windshield wipers, turn signals, seat belt usages, phone / radio usages within the car, maintenance performed, and data collected by vehicle computer systems including the OBD.
Additional sensors 221 and 211 may detect and save external driving conditions such as external temperature, rainfall, snowfall, light levels and sun position to improve driver visibility. External cameras and proximity sensor 211 and 221, for example, can detect nearby vehicles, vehicle spacings, traffic levels and road conditions, obstructions and animals. Sensors 211 & 221 may also detect and store information about moving violations, traffic signs and signals observed by vehicles 210 & 220. Sensors 211 and 221, in addition, may store and detect data related to vehicle maintenance, including the engine status, oil temperature, engine coolant level, odometer, fuel level, RPMs, and tire pressure.
The “vehicle sensors 211 and 221, may also include cameras or proximity sensors that can record additional conditions within and outside the vehicles 210, 220. Internal cameras can detect, for example, the number and type of passengers. adults, children, teenagers, pets, etc.) In the vehicles and possible sources of distraction for drivers (e.g. pets, cell phone use, and unattended objects in the vehicle). Sensors 211, 221 may also be configured to collect information about a driver’s condition or movements. Sensors may be installed in vehicles 210 and 220–for example, to monitor the movements of a driver, including his eye and/or head positions. Additional sensors 211 or 221 can collect data about the physical or psychological state of the drivers, such as fatigue and intoxication. The driver’s condition can be determined by the movements or other sensors. For example, sensors which detect alcohol in the air, or the blood alcohol content, or a breathalyzer.
Certain sensors in vehicles 211 and 221, may also collect information about the driver’s choice of route, whether he follows a route and classify the trip type (e.g. commute, errand, new route, etc.). In certain embodiments sensors and/or camera 211 and 221, may determine how often and when the vehicles 210 & 220 stray from a single lane. The Global Positioning System, locational sensor devices or devices outside the vehicles, as well as locational sensors and/or devices inside the vehicles, can be used to determine the route and lane position.
The data collected from vehicle sensors 211, 221 can be stored, analyzed, or transmitted to external devices. As shown in FIG. Sensor data can be sent via short-range communication systems 212 and 2222 to nearby vehicles. The sensor data can also be sent via telematics 213 and 223, to remote computing devices such as the driving analysis server 250.
Short-range communication systems 212 & 222 are vehicle based data transmission systems that transmit vehicle operation data to nearby vehicles and receive vehicle operations data from nearby vehicles. Communication systems 212 or 222 can, in some cases, use dedicated short-range communication (DSRC), protocols and standards for wireless communications between vehicles. In the United States 75 MHz of the 5.850-5.925 GHz frequency band has been designated for DSRC applications and systems. Other countries and jurisdictions have also defined DSRC allocations. In other cases, the short-range communication systems 212 & 222 may not be using DSRC. They could use WLAN (e.g. IEEE 802.11) or Bluetooth (e.g. IEEE 802.15.1) or any of the Communication Access for Land Mobiles wireless communication protocols & air interfaces. Vehicle-to-vehicle transmissions (V2V) between short-range communication systems 212 & 222 can be done via DSRC or Bluetooth, GSM Infrared, IEEE 802.11, WiMAX RFID and/or other suitable wireless communication standards and protocols. Short-range communication systems 212 & 222 can include special hardware in vehicles 210 & 220, such as transceivers and antennas. In other cases, the communication systems 212 & 222 can be implemented with existing vehicle hardware (e.g. radio and satellite equipment or navigation computers), or by software on mobile devices 215 & 225.
