Patent Publication Number: US-10783586-B1

Title: Determining a property of an insurance policy based on the density of vehicles

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of pending U.S. patent application Ser. No. 14/184,272 entitled “Insurance System for Analysis of Autonomous Driving” and filed on Feb. 19, 2014, which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     Aspects of the disclosure generally relate to the analysis of vehicle driving data of vehicles that have engaged autonomous driving features for the purposes of determining aspects of vehicle insurance, and for teaching driving skills to drivers of autonomous vehicles. 
     BACKGROUND 
     Many vehicles include sensors and internal computer systems designed to monitor and control vehicle operations, driving conditions, and driving functions. Advanced vehicles systems can perform such tasks as detecting and correcting a loss of traction on an icy road, self-parking, or detecting an imminent collision or unsafe driving condition and automatically making evasive maneuvers. Additionally, vehicles can include autonomous driving systems that assume all or part of real-time driving functions to operate the vehicle without real-time input from a human operator. 
     Many vehicles also include communication systems designed to send and receive information from inside or outside the vehicle. Such information can include, for example, vehicle operational data, driving conditions, and communications from other vehicles or systems. For example, a Bluetooth system may enable communication between the vehicle and the driver&#39;s mobile phone. Telematics systems may be configured to access vehicle computers and sensor data, including on-board diagnostics systems (OBD), and transmit the data to a display within the vehicle, a personal computer or mobile device, or to a centralized data processing system. Additionally, vehicle-to-vehicle (V2V) communication systems can be used to send and receive information from other nearby vehicles. Data obtained from vehicle sensors, Telematics systems, OBD systems, and V2V systems, have been used for a variety of purposes, including maintenance, diagnosis, and analysis. 
     SUMMARY 
     The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosure. The summary is not an extensive overview of the disclosure. It is neither intended to identify key or critical elements of the disclosure nor to delineate the scope of the disclosure. The following summary merely presents some concepts of the disclosure in a simplified form as a prelude to the description below. 
     Aspects of the disclosure relate to systems, methods, and computing devices for determining, by a computing device, a property of an insurance policy for a semi-autonomous vehicle. The property of the insurance policy may comprise at least one of a premium, a deductible, a coverage term, and a coverage amount. The property of the insurance policy may be based at least on a driver-independent variable of the semi-autonomous vehicle and a driver-dependent variable. The driver-independent variable of the semi-autonomous vehicle may comprise a Vehicle Identification Number (VIN). The driver-dependent variable may comprise an age of a driver of the semi-autonomous vehicle or a location of residence of the driver of the semi-autonomous vehicle. In some aspects, the property of the insurance policy may be based on a plurality of driver-independent variables including the driver-independent variable and a plurality of driver-dependent variables including the driver-dependent variable. 
     In response to a determination that a level of autonomy of the semi-autonomous vehicle has changed, a value of the property of the insurance policy may be changed. Changing the value of the property of the insurance policy may comprise determining that the level of autonomy of the semi-autonomous vehicle has increased, and increasing a weight for the driver-independent variable of the semi-autonomous vehicle or decreasing a weight for the driver-dependent variable. Changing the value of the property may also comprise determining the value of the property of the insurance policy based on at least one of the increased weight for the driver-independent variable of the semi-autonomous vehicle and the decreased weight for the driver-dependent variable. 
     Changing the value of the property of the insurance policy may comprise determining that the level of autonomy of the semi-autonomous vehicle has decreased, and decreasing a weight for the driver-independent variable of the semi-autonomous vehicle or increasing a weight for the driver-dependent variable. Changing the value of the property may also comprise determining the value of the property of the insurance policy based on at least one of the decreased weight for the driver-independent variable of the semi-autonomous vehicle and the increased weight for the driver-dependent variable. 
     In some aspects, systems, methods, and computing devices may determine that a classification of the vehicle has changed from semi-autonomous to completely autonomous. In response to determining that the classification of the vehicle has changed from semi-autonomous to completely autonomous, the value of the property of the insurance policy may be changed based on the driver-independent variable and not the driver-dependent variable. 
     Systems, methods, and computing devices described herein may also retrieve, from a database, a vehicle identifier of a semi-autonomous or completely autonomous vehicle, and determine a property of an insurance policy for the semi-autonomous or completely autonomous vehicle. As previously discussed, the property of the insurance policy may comprise at least one of a premium, a deductible, a coverage term, and a coverage amount. The property of the insurance policy may be based on the vehicle identifier of the semi-autonomous or completely autonomous vehicle. The property of the insurance policy might not be based on any driver-dependent variables. As previously discussed, the vehicle identifier may comprise a VIN. Additionally or alternatively, the property of the insurance policy may be based on a plurality of driver-independent variables including the vehicle identifier. 
     Aspects of the disclosure relate to systems, methods, and computing devices for determining, by a computing device, data identifying vehicles within a predetermined distance of a first vehicle. The predetermined distance of the first vehicle may comprise a predetermined distance of the first vehicle along a path of the first vehicle. 
     The systems, methods, and computing devices may determine a density of vehicles within the predetermined distance of the first vehicle based on the determined data identifying the vehicles. Determining the density of vehicles within the predetermined distance of the first vehicle may comprise determining a density of completely autonomous vehicles within the predetermined distance of the first vehicle. A property of an insurance policy for the first vehicle may be generated based on the determined density of vehicles. Additionally or alternatively, determining the density of vehicles within the predetermined distance of the first vehicle may comprise determining a density of completely autonomous vehicles within the predetermined distance of the first vehicle and a density of semi-autonomous vehicles within the predetermined distance of the first vehicle. Additionally or alternatively, determining the density of vehicles within the predetermined distance of the first vehicle may comprise determining a density of completely autonomous vehicles within the predetermined distance of the first vehicle, a density of semi-autonomous vehicles within the predetermined distance of the first vehicle, and a density of non-autonomous vehicles within the predetermined distance of the first vehicle. 
     The systems, methods, and computing devices may sense, using a plurality of sensors on the first vehicle, a number of the vehicles within the predetermined distance of the first vehicle. The plurality of sensors may comprise at least two of the following: a camera, a proximity sensor, a vehicle-to-vehicle communication device, and a vehicle-to-infrastructure communication device. In some aspects, determining the data identifying vehicles within the predetermined distance of the first vehicle may comprise generating the data based on the sensed number of vehicles. 
     The systems, methods, and computing devices may send, by the computing device, the data identifying vehicles within the predetermined distance of the first vehicle to a remote driving analysis computing device. The remote driving analysis computing device may be configured to generate the property of the insurance policy for the first vehicle. 
     The systems described herein may comprise a first vehicle having a vehicle computing device. The first vehicle computing device may include a first processor and first memory storing computer-executable instructions that, when executed by the first processor, cause the vehicle computing device to generate data identifying vehicles within a predetermined distance of the first vehicle. The systems may also comprise a driving analysis computing device having a second processor and second memory storing computer-executable instructions that, when executed by the second processor, cause the driving analysis computing device to determine a density of vehicles within the predetermined distance of the first vehicle based on the generated data and generate a property of an insurance policy for the first vehicle based on the determined density of vehicles. 
     The first memory described herein may store computer-executable instructions that, when executed by the first processor, cause the vehicle computing device to receive, from a plurality of sensors on the first vehicle, a sensed number of vehicles within the predetermined distance of the first vehicle. Generating the data identifying vehicles within the predetermined distance of the first vehicle may comprise generating the data based on the sensed number of vehicles. The plurality of sensors may comprise at least two of the following: a camera, a proximity sensor, a vehicle-to-vehicle communication device, and a vehicle-to-infrastructure communication device. The first memory described herein may also store computer-executable instructions that, when executed by the first processor, cause the vehicle computing device to send the data identifying vehicles within the predetermined distance of the first vehicle to the driving analysis computing device. 
     Aspects of the disclosure relate to systems, methods, and computing devices for sending an instruction to a vehicle to switch off an autonomous driving feature. A computing device may determine operational data of the vehicle after the autonomous driving feature is switched off. Furthermore, a value of a property of an insurance policy for the vehicle may be determined based on the operational data of the vehicle after the autonomous feature is switched off. As previously discussed, the property of the insurance policy may comprise at least one of a premium, a deductible, a coverage term, and a coverage amount. 
     In some aspects, the property of the insurance policy may comprise an insurance quote for the vehicle, and determining the value of the property of the insurance policy may comprise determining the value of the insurance quote for the vehicle based on the operational data of the vehicle after the autonomous feature is switched off. The operational data may indicate a driver of the vehicle&#39;s response to the autonomous feature being switched off. 
     Determining the value of the property of the insurance policy for the vehicle may be based on the operational data of the vehicle after the autonomous feature is switched off and a maintenance history for the vehicle. Furthermore, the maintenance history may comprise at least one of a software upgrade to a computing device of the vehicle and a response to an illegal access to software of the computing device. 
     Determining the value of the property may be based on other information. For example, determining the value of the property of the insurance policy for the vehicle may be based on the operational data of the vehicle after the autonomous feature is switched off and use of an autonomous vehicle lane. Determining the value of the property of the insurance policy for the vehicle may additionally or alternatively be based on the operational data of the vehicle after the autonomous feature is switched off and use of an autonomous parallel parking feature of the vehicle. Determining the operational data of the vehicle after the autonomous driving feature is switched off may comprise determining a history of a driver of the vehicle&#39;s response to the autonomous driving feature being switched off over a period of time. 
     A system for performing the above-described steps is described herein. For example, a system may comprise a vehicle computing device and a driving analysis computing device. The driving analysis computing device may include a processor and memory storing computer-executable instructions that, when executed by the processor, cause the driving analysis computing device to send an instruction to the vehicle computing device of a vehicle to switch off an autonomous driving feature. The driving analysis computing device may also determine operational data of the vehicle after the autonomous driving feature is switched off, and determine a value of a property of an insurance policy for the vehicle based on the operational data of the vehicle after the autonomous feature is switched off. The system may perform additional steps as previously discussed and as described herein. 
     Aspects of the disclosure relate to systems, methods, and computing devices for switching off an autonomous feature of a vehicle in response to a determination that an environmental condition of the vehicle is satisfied. The determination that the environmental condition of the vehicle is satisfied may comprise determining that a density of vehicles within a predetermined distance of the vehicle is below a threshold. In some examples, prior to switching off the autonomous feature of the vehicle, the method may include determining that the driver of the vehicle has enabled a teaching feature of the vehicle. The determination that the environmental condition of the vehicle is satisfied may be performed at a first time, and the method may further comprise switching on the autonomous feature of the vehicle in response to a determination that the environmental condition of the vehicle is not satisfied at a second time after the first time. 
     A computing device may determine operational data of the vehicle after the autonomous feature of the vehicle is switched off, and the operational data of the vehicle may indicate a driver of the vehicle&#39;s response to the autonomous feature of the vehicle being switched off. A score based on the operational data of the vehicle after the autonomous feature is switched off may be generated. If the score exceeds a threshold, a reward may be provided to the driver of the vehicle. If the score does not exceed a threshold, a recommended response for the driver of the vehicle may be generated. The recommended response may be provided to the driver of the vehicle while the driver is in the vehicle. In some aspects, a value of a property of an insurance policy for the vehicle may be determined based on the generated score. 
     A system for performing the above-described steps is described herein. For example, the system may comprise a vehicle having a vehicle computing device, the vehicle computing device including a first processor and first memory storing computer-executable instructions that, when executed by the first processor, cause the vehicle computing device to switch off an autonomous feature of a vehicle in response to a determination that an environmental condition of the vehicle is satisfied. The vehicle computing device may also determine operational data of the vehicle after the autonomous feature of the vehicle is switched off, the operational data of the vehicle indicating a driver of the vehicle&#39;s response to the autonomous feature of the vehicle being switched off. The system may also comprise a driving analysis computing device having a second processor and second memory storing computer-executable instructions that, when executed by the second processor, cause the driving analysis computing device to generate a score based on the operational data of the vehicle after the autonomous feature is switched off. The system may perform additional steps as previously discussed and as described herein. 
     The systems described herein may include a vehicle computing device and a driving analysis computing device. The driving analysis computing device may comprise a processor and memory storing computer-executable instructions that, when executed by the processor, cause the driving analysis computing device to perform the steps described herein. Additionally or alternatively, a non-transitory computer readable medium storing instructions that, when read by a computing device, may cause the computing device to perform the steps described herein. 
     Aspects of the present disclosure improve determination of properties of insurance policies for vehicles that engage in autonomous driving and improve drivers&#39; skills by teaching them driving techniques. Other features and advantages of the disclosure will be apparent from the additional description provided herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A more complete understanding of the present invention and the advantages thereof may be acquired by referring to the following description in consideration of the accompanying drawings, in which like reference numbers indicate like features, and wherein: 
         FIG. 1  illustrates a network environment and computing systems that may be used to implement aspects of the disclosure. 
         FIG. 2  is a diagram illustrating various example components of a driving analysis computing device according to one or more aspects of the disclosure. 
         FIG. 3  is a flow diagram illustrating an example method of analyzing vehicle driving data according to one or more aspects of the disclosure. 
         FIG. 4  is a flow diagram illustrating an example method of analyzing vehicle driving data according to one or more aspects of the disclosure. 
         FIG. 5  is a diagram illustrating one example of an autonomous driving insurance rating factor calculator according to one or more aspects of the disclosure. 
         FIG. 6  is a flow diagram illustrating an example method of generating vehicle insurance rates based on driver-independent variables and/or driver-dependent variables according to one or more aspects of the disclosure. 
         FIG. 7  is a flow diagram illustrating an example method of generating vehicle insurance rates based on changes in the level of autonomy of vehicles according to one or more aspects of the disclosure. 
         FIG. 8  is a flow diagram illustrating an example method of tracking vehicle density and/or generating vehicle insurance rates based on vehicle density according to one or more aspects of the disclosure. 
         FIG. 9  is a flow diagram illustrating an example method of analyzing use of autonomous vehicle features and/or maintenance of autonomous vehicles according to one or more aspects of the disclosure. 
         FIG. 10  is a flow diagram illustrating an example method of enabling vehicle teaching features and/or monitoring the driver&#39;s response to teaching features according to one or more aspects of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description of the various embodiments, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration, various embodiments of the disclosure that may be practiced. It is to be understood that other embodiments may be utilized. 
     As will be appreciated by one of skill in the art upon reading the following disclosure, various aspects described herein may be embodied as a method, a computer system, or a computer program product. Accordingly, those aspects may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. In addition, aspects may take the form of a computing device configured to perform specified actions. Furthermore, such aspects may take the form of a computer program product stored by one or more computer-readable storage media having computer-readable program code, or instructions, embodied in or on the storage media. Any suitable computer readable storage media may be utilized, including hard disks, CD-ROMs, optical storage devices, magnetic storage devices, and/or any combination thereof. In addition, various signals representing data or events as described herein may be transferred between a source and a destination in the form of electromagnetic waves traveling through signal-conducting media such as metal wires, optical fibers, and/or wireless transmission media (e.g., air and/or space). 
       FIG. 1  illustrates a block diagram of a computing device  101  in driving analysis communication system  100  that may be used according to one or more illustrative embodiments of the disclosure. The driving analysis computing device  101  may have a processor  103  for controlling overall operation of the computing device  101  and its associated components, including RAM  105 , ROM  107 , input/output module  109 , and memory unit  115 . The computing device  101 , along with one or more additional devices (e.g., terminals  141 ,  151 ) may correspond to any of multiple systems or devices, such as a driving analysis computing devices or systems, configured as described herein for transmitting and receiving vehicle operational data, analyzing vehicle operational data, determining aspects related to vehicle insurance rating factors, including distance-based autonomous driving insurance rating factors, and determining properties of vehicle insurance policies. Vehicle operational data can include data collected from vehicle sensors and OBD systems. Vehicle operations can also include data pertaining to the driver of a vehicle. Vehicle operational data can also include data pertaining to other nearby vehicles collected via, for example, V2V communications. As used herein, vehicle operation data is used interchangeably with driving data. 
     The computing device  101  may additionally or alternatively be configured to generate vehicle insurance rates based on driver-independent variables and/or driver-dependent variables, generate vehicle insurance rates based on changes in the level of autonomy of vehicles according to one or more aspects of the disclosure, track vehicle density and/or generate vehicle insurance rates based on vehicle density, analyze use of autonomous vehicle features and/or maintenance of autonomous vehicles, and enable vehicle teaching features and/or monitor the driver&#39;s response to teaching features, as will be described in further detail in the examples below. 
     Input/Output (I/O)  109  may include a microphone, keypad, touch screen, and/or stylus through which a user of the computing device  101  may provide input, and may also include one or more of a speaker for providing audio input/output and a video display device for providing textual, audiovisual and/or graphical output. Software may be stored within memory unit  115  and/or other storage to provide instructions to processor  103  for enabling device  101  to perform various functions. For example, memory unit  115  may store software used by the device  101 , such as an operating system  117 , application programs  119 , and an associated internal database  121 . The memory unit  115  includes one or more of volatile and/or non-volatile computer memory to store computer-executable instructions, data, and/or other information. Processor  103  and its associated components may allow the driving analysis system  101  to execute a series of computer-readable instructions to transmit or receive vehicle driving data, analyze driving data, determine driving characteristics from the driving data, and determine properties of, for example, vehicle insurance policies using the driving data. 
     The driving analysis computing device  101  may operate in a networked environment  100  supporting connections to one or more remote computers, such as terminals/devices  141  and  151 . Driving analysis computing device  101 , and related terminals/devices  141  and  151 , may include devices installed in vehicles, mobile devices that may travel within vehicles, or devices outside of vehicles that are configured to receive and process vehicle and driving data. Thus, the driving analysis computing device  101  and terminals/devices  141  and  151  may each include personal computers (e.g., laptop, desktop, or tablet computers), servers (e.g., web servers, database servers), vehicle-based devices (e.g., on-board vehicle computers, short-range vehicle communication systems, telematics devices), or mobile communication devices (e.g., mobile phones, portable computing devices, and the like), and may include some or all of the elements described above with respect to the driving analysis computing device  101 . The network connections depicted in  FIG. 1  include a local area network (LAN)  125  and a wide area network (WAN)  129 , and a wireless telecommunications network  133 , but may also include other networks. When used in a LAN networking environment, the driving analysis computing device  101  may be connected to the LAN  125  through a network interface or adapter  123 . When used in a WAN networking environment, the device  101  may include a modem  127  or other means for establishing communications over the WAN  129 , such as network  131  (e.g., the Internet). When used in a wireless telecommunications network  133 , the device  101  may include one or more transceivers, digital signal processors, and additional circuitry and software for communicating with wireless computing devices  141  (e.g., mobile phones, short-range vehicle communication systems, vehicle telematics devices) via one or more network devices  135  (e.g., base transceiver stations) in the wireless network  133 . 
     It will be appreciated that the network connections shown are illustrative and other means of establishing a communications link between the computers may be used. The existence of any of various network protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, and of various wireless communication technologies such as GSM, CDMA, WiFi, and WiMAX, is presumed, and the various computing devices and driving analysis system components described herein may be configured to communicate using any of these network protocols or technologies. 
     Additionally, one or more application programs  119  used by the driving analysis computing device  101  may include computer executable instructions (e.g., driving data analysis programs, driving characteristic algorithms, driving and insurance policy properties algorithms, vehicle insurance rating factor algorithms, driver reward algorithms, and driver teaching algorithms) for transmitting and receiving vehicle driving data, determining mileage units indicating distances traveled by the vehicle while the vehicle was engaged in autonomous driving, determining distance-based autonomous driving insurance rating factors, determining various properties associated with one or more vehicle insurance policies, and performing other related functions as described herein. 
     Vehicle operational data may refer to information pertaining to one or more actions or events performed by a vehicle and can include aspects of information identified or determined from data collected from a vehicle. Vehicle operational data can include, for example, a vehicle speed and/or gas mileage. In addition, for example, vehicle operational data may include an indication that the vehicle is engaged in autonomous or manual driving, a road condition, a road-type and other operational data collected from the vehicle. 
     Vehicle operational data may also include data describing the environment surrounding the vehicle, such as a vehicle density and type of vehicles near the tracked vehicle (e.g., whether the vehicles surrounding the tracked vehicle are autonomous or not). Vehicle operational data may also include the driver&#39;s use of autonomous vehicle features, the driver&#39;s maintenance of the vehicle, and/or the driver&#39;s use of and response to autonomous driving (e.g., teaching) features. 
     As discussed below, a mileage unit indicating a distance traveled by the vehicle when the vehicle has engaged in autonomous driving can be determined from driving data collected by a vehicle sensors and telematics device, and/or additional data received from other nearby vehicles using vehicle-to-vehicle (V2V) communications. It should be understood that vehicle operational data may be associated with a vehicle, a driver, or a group of vehicles or drivers engaged in social interaction, such as an autonomous droning relationship. 
     A vehicle insurance rating factor may refer to a factor which reflects a relative level of risk associated with aspects of vehicle insurance. The vehicle insurance rating factor can be based on or more data points and be used to determine a property of a vehicle insurance policy. A property of a vehicle insurance policy can include, for example, a premium, a deductible, a coverage term, and a coverage amount. An example vehicle insurance rating factor of the instant disclosure includes a distance-based autonomous driving insurance rating factor. As used herein, a distance-based autonomous driving insurance rating factor is used synonymously with distance-based autonomous driving insurance factor. Other exemplary rating factors include, but are not limited to, the driver&#39;s response when switching between autonomous and manual driving features (or mode), the vehicle&#39;s maintenance history, the use of manual or automated vehicle lanes, and the use of autonomous parallel parking. 
       FIG. 2  is a diagram of an illustrative driving analysis system  200  including two vehicles  210  and  220 , a driving analysis server  250 , and additional related components. Each component shown in  FIG. 2  may be implemented in hardware, software, or a combination of the two. Additionally, each component of the driving analysis system  200  may include a computing device (or system) having some or all of the structural components described above for computing device  101 . 
     Vehicles  210  and  220  in the driving analysis system  200  may be, for example, automobiles, motorcycles, scooters, buses, recreational vehicles, boats, or other vehicles for which a vehicle driving data may be collected and analyzed. The vehicles  210  and  220  each include vehicle operation sensors  211  and  221  capable of detecting and recording various conditions at the vehicle and operational parameters of the vehicle. For example, sensors  211  and  221  may detect and store data corresponding to the vehicle&#39;s location (e.g., GPS coordinates), time, travel time, speed and direction, rates of acceleration or braking, gas mileage, and specific instances of sudden acceleration, braking, swerving, and distance traveled. Sensors  211  and  221  also may detect and store data received from the vehicle&#39;s  210  internal systems, such as impact to the body of the vehicle, air bag deployment, headlights usage, brake light operation, door opening and closing, door locking and unlocking, cruise control usage, hazard lights usage, windshield wiper usage, horn usage, turn signal usage, seat belt usage, phone and radio usage within the vehicle, autonomous driving system usage, maintenance performed on the vehicle, and other data collected by the vehicle&#39;s computer systems, including the vehicle OBD. 
     Additional sensors  211  and  221  may detect and store the external driving conditions, for example, external temperature, rain, snow, light levels, and sun position for driver visibility. For example, external cameras and proximity sensors  211  and  221  may detect other nearby vehicles, vehicle spacing, traffic levels, road conditions, traffic obstructions, animals, cyclists, pedestrians, and other conditions that may factor into a driving data analysis. Sensors  211  and  221  also may detect and store data relating to moving violations and the observance of traffic signals and signs by the vehicles  210  and  220 . Additional sensors  211  and  221  may detect and store data relating to the maintenance of the vehicles  210  and  220 , such as the engine status, oil level, engine coolant temperature, odometer reading, the level of fuel in the fuel tank, engine revolutions per minute (RPMs), software upgrades, and/or tire pressure. 
     Vehicles sensors  211  and  221  also may include cameras and/or proximity sensors capable of recording additional conditions inside or outside of the vehicles  210  and  220 . For example, internal cameras may detect conditions such as the number of the passengers and the types of passengers (e.g. adults, children, teenagers, pets, etc.) in the vehicles, and potential sources of driver distraction within the vehicle (e.g., pets, phone usage, and unsecured objects in the vehicle). Sensors  211  and  221  also may be configured to collect data a driver&#39;s movements or the condition of a driver. For example, vehicles  210  and  220  may include sensors that monitor a driver&#39;s movements, such as the driver&#39;s eye position and/or head position, etc. Additional sensors  211  and  221  may collect data regarding the physical or mental state of the driver, such as fatigue or intoxication. The condition of the driver may be determined through the movements of the driver or through other sensors, for example, sensors that detect the content of alcohol in the air or blood alcohol content of the driver, such as a breathalyzer. 
     Certain vehicle sensors  211  and  221  also may collect information regarding the driver&#39;s route choice, whether the driver follows a given route, and to classify the type of trip (e.g. commute, errand, new route, etc.). In certain embodiments, sensors and/or cameras  211  and  221  may determine when and how often the vehicles  210  and  220  stay in a single lane or stray into other lanes. A Global Positioning System (GPS), locational sensors positioned inside the vehicles  210  and  220 , and/or locational sensors or devices external to the vehicles  210  and  220  may be used to determine the route, lane position, road-type (e.g. highway, entrance/exit ramp, residential area, etc.) and other vehicle position/location data. 
     The data collected by vehicle sensors  211  and  221  may be stored and/or analyzed within the respective vehicles  210  and  220 , such as for example a driving analysis computer  214 ,  224  integrated into the vehicle, and/or may be transmitted to one or more external devices. For example, as shown in  FIG. 2 , sensor data may be transmitted via short-range communication systems  212  and  222  to other nearby vehicles. Additionally, the sensor data may be transmitted via telematics devices  213  and  223  to one or more remote computing devices, such as driving analysis server  250 . 
     Short-range communication systems  212  and  222  are vehicle-based data transmission systems configured to transmit vehicle operational data to other nearby vehicles, and to receive vehicle operational data from other nearby vehicles. In some examples, communication systems  212  and  222  may use the dedicated short-range communications (DSRC) protocols and standards to perform wireless communications between vehicles. In the United States, 75 MHz in the 5.850-5.925 GHz band have been allocated for DSRC systems and applications, and various other DSRC allocations have been defined in other countries and jurisdictions. However, short-range communication systems  212  and  222  need not use DSRC, and may be implemented using other short-range wireless protocols in other examples, such as WLAN communication protocols (e.g., IEEE 802.11), Bluetooth (e.g., IEEE 802.15.1), or one or more of the Communication Access for Land Mobiles (CALM) wireless communication protocols and air interfaces. The vehicle-to-vehicle (V2V) transmissions between the short-range communication systems  212  and  222  may be sent via DSRC, Bluetooth, satellite, GSM infrared, IEEE 802.11, WiMAX, RFID, and/or any suitable wireless communication media, standards, and protocols. In certain systems, short-range communication systems  212  and  222  may include specialized hardware installed in vehicles  210  and  220  (e.g., transceivers, antennas, etc.), while in other examples the communication systems  212  and  222  may be implemented using existing vehicle hardware components (e.g., radio and satellite equipment, navigation computers) or may be implemented by software running on the mobile devices  215  and  225  of drivers and passengers within the vehicles  210  and  220 . 
     The range of V2V communications between vehicle communication systems  212  and  222  may depend on the wireless communication standards and protocols used, the transmission/reception hardware (e.g., transceivers, power sources, antennas), and other factors. Short-range V2V communications may range from just a few feet to many miles, and different types of driving behaviors may be determined depending on the range of the V2V communications. For example, V2V communications ranging only a few feet may be sufficient for a driving analysis computing device  101  in one vehicle to determine that another vehicle is tailgating or cut-off the vehicle, whereas longer communications may allow the device  101  to determine additional types of driving behaviors (e.g., vehicle spacing, yielding, defensive avoidance, proper response to a safety hazard, etc.) and driving conditions (e.g., congestion). 
     V2V communications also may include vehicle-to-infrastructure (V2I) communications, such as transmissions from vehicles to non-vehicle receiving devices, for example, toll booths, rail road crossings, and road-side traffic monitoring devices. Certain V2V communication systems may periodically broadcast data from a vehicle  210  to any other vehicle, or other infrastructure device capable of receiving the communication, within the range of the vehicle&#39;s transmission capabilities. For example, a vehicle  210  may periodically broadcast (e.g., every 0.1 second, every 0.5 seconds, every second, every 5 seconds, etc.) certain vehicle operation data via its short-range communication system  212 , regardless of whether or not any other vehicles or reception devices are in range. In other examples, a vehicle communication system  212  may first detect nearby vehicles and receiving devices, and may initialize communication with each by performing a handshaking transaction before beginning to transmit its vehicle operation data to the other vehicles and/or devices. 
     The types of vehicle operational data, or vehicle driving data, transmitted by vehicles  210  and  220  may depend on the protocols and standards used for the V2V communication, the range of communications, the autonomous driving system, and other factors. In certain examples, vehicles  210  and  220  may periodically broadcast corresponding sets of similar vehicle driving data, such as the location (which may include an absolute location in GPS coordinates or other coordinate systems, and/or a relative location with respect to another vehicle or a fixed point), speed, and direction of travel. In certain examples, the nodes in a V2V communication system (e.g., vehicles and other reception devices) may use internal clocks with synchronized time signals, and may send transmission times within V2V communications, so that the receiver may calculate its distance from the transmitting node based on the difference between the transmission time and the reception time. The state or usage of the vehicle&#39;s  210  controls and instruments may also be transmitted, for example, whether the vehicle is accelerating, braking, turning, and by how much, and/or which of the vehicle&#39;s instruments are currently activated by the driver (e.g., head lights, turn signals, hazard lights, cruise control, 4-wheel drive, traction control, etc.). Vehicle warnings such as detection by the vehicle&#39;s  210  internal systems that the vehicle is skidding, that an impact has occurred, or that the vehicle&#39;s airbags have been deployed, also may be transmitted in V2V communications. In various other examples, any data collected by any vehicle sensors  211  and  221  potentially may be transmitted via V2V communication to other nearby vehicles or infrastructure devices receiving V2V communications from communication systems  212  and  222 . Further, additional vehicle driving data not from the vehicle&#39;s sensors (e.g., vehicle make/model/year information, driver insurance information, driver scores, etc.) may be collected from other data sources, such as a driver&#39;s or passenger&#39;s mobile device  215  or  225 , driving analysis server  250 , and/or another external computer system  230 , and transmitted using V2V communications to nearby vehicles and other transmitting and receiving devices using communication systems  212  and  222 . 
     As shown in  FIG. 2 , the data collected by vehicle sensors  211  and  221  also may be transmitted to a driving analysis server  250 , and one or more additional external servers and devices via telematics devices  213  and  223 . Telematics devices  213  and  223  may be computing devices containing many or all of the hardware/software components as the computing device  101  depicted in  FIG. 1 . As discussed above, the telematics devices  213  and  223  may receive vehicle operation data and driving data from vehicle sensors  211  and  221 , and may transmit the data to one or more external computer systems (e.g., driving analysis server  250  of an insurance company, financial institution, or other entity) over a wireless transmission network. Telematics devices  213  and  223  also may be configured to detect or determine additional types of data relating to real-time driving and the condition of the vehicles  210  and  220 . In certain embodiments, the telematics devices  213  and  223  may contain or may be integral with one or more of the vehicle sensors  211  and  221  or system, such as an autonomous driving system. The telematics devices  213  and  223  also may store the type of their respective vehicles  210  and  220 , for example, the make, model, trim (or sub-model), year, and/or engine specifications, autonomous driving system specifications, as well as other information such as vehicle owner or driver information, insurance information, and financing information for the vehicles  210  and  220 . 
     In the example shown in  FIG. 2 , telematics devices  213  and  223  may receive vehicle driving data from vehicle sensors  211  and  221 , and may transmit the data to a driving analysis server  250 . However, in other examples, one or more of the vehicle sensors  211  and  221  or systems, including autonomous driving systems, may be configured to receive and transmit data directly from or to a driving analysis server  250  without using a telematics device. For instance, telematics devices  213  and  223  may be configured to receive and transmit data from certain vehicle sensors  211  and  221  or systems, while other sensors or systems may be configured to directly receive and/or transmit data to a driving analysis server  250  without using the telematics devices  213  and  223 . Thus, telematics devices  213  and  223  may be optional in certain embodiments. 
     In certain embodiments, vehicle sensors, vehicle OBD, autonomous driving systems, and/or vehicle communication systems, may collect and/or transmit data pertaining to autonomous driving of the vehicles. In autonomous driving, the vehicle fulfills all or part of the driving without being piloted by a human. An autonomous car can be also referred to as a driverless car, self-driving car, or robot car. For example, in autonomous driving, a vehicle control computer  217 ,  227  may be configured to operate all or some aspects of the vehicle driving, including but not limited to acceleration, braking, steering, and/or route navigation. A vehicle with an autonomous driving capability may sense its surroundings using the vehicle sensors  221 ,  221  and/or receive inputs regarding control of the vehicle from the vehicle communications systems, including but not limited to short range communication systems  212 ,  222 , Telematics  213 ,  223 , or other vehicle communication systems. 
     In certain embodiments, mobile computing devices  215  and  225  within the vehicles  210  and  220  may be used to collect vehicle driving data and/or to receive vehicle driving data from vehicle communication systems and then to transmit the vehicle driving data to the driving analysis server  250  and other external computing devices. Mobile computing devices  215  and  225  may be, for example, mobile phones, personal digital assistants (PDAs), or tablet computers of the drivers or passengers of vehicles  210 ,  220 . Software applications executing on mobile devices  215 ,  225  may be configured to detect certain driving data independently and/or may communicate with vehicle sensors  211 ,  221 , Telematics  213 ,  223 , autonomous driving systems, or other vehicle communication systems to receive additional driving data. For example, mobile devices  215 ,  225  equipped with GPS functionality may determine vehicle location, speed, direction and other basic driving data without needing to communicate with the vehicle sensors  211  or  221 , or any vehicle system. In other examples, software on the mobile devices  215 ,  225  may be configured to receive some or all of the driving data collected by vehicle sensors  211 ,  221 . Mobile computing devices  215  and  225  may also be involved with aspects of autonomous driving, including receiving, collecting, and transmitting vehicle operational data regarding autonomous driving and autonomous driving relationships between multiple vehicles. 
     When mobile computing devices  215  and  225  within the vehicles  210  and  220  are used to detect vehicle driving data and/or to receive vehicle driving data from vehicles  211  and  221 , the mobile computing devices  215  and  225  may store, analyze, and/or transmit the vehicle driving data to one or more other devices. For example, mobile computing devices  215  and  225  may transmit vehicle driving data directly to one or more driving analysis servers  250 , and thus may be used in conjunction with or instead of telematics devices  213  and  223 . Additionally, mobile computing devices  215  and  225  may be configured to perform the V2V communications described above, by establishing connections and transmitting/receiving vehicle driving data to and from other nearby vehicles. Thus, mobile computing devices  215  and  225  may be used in conjunction with, or instead of, short-range communication systems  212  and  222  in some examples. In addition, mobile computing devices  215  and  225  may be used in conjunction with the vehicle control computers  217  and  227  for purposes of autonomous driving. Moreover, the processing components of the mobile computing devices  215  and  225  may be used to analyze vehicle driving data, determine a distance-based autonomous driving insurance factor, determine properties related to aspects of a vehicle insurance policy, and perform other related functions. Therefore, in certain embodiments, mobile computing devices  215  and  225  may be used in conjunction with, or in place of, the driving analysis computers  214  and  224 . 
     Vehicles  210  and  220  may include driving analysis computers  214  and  224 , which may be separate computing devices or may be integrated into one or more other components within the vehicles  210  and  220 , such as the short-range communication systems  212  and  222 , telematics devices  213  and  223 , autonomous driving systems, or the internal computing systems of vehicles  210  and  220 . As discussed above, driving analysis computers  214  and  224  also may be implemented by computing devices independent from the vehicles  210  and  220 , such as mobile computing devices  215  and  225  of the drivers or passengers, or one or more separate computer systems  230  (e.g., a user&#39;s home or office computer). In any of these examples, the driving analysis computers  214  and  224  may contain some or all of the hardware/software components as the computing device  101  depicted in  FIG. 1 . Further, in certain implementations, the functionality of the driving analysis computers, such as storing and analyzing vehicle driving data, determining a distance-based autonomous driving insurance factor, and determining aspects of insurance policies, may be performed in a central driving analysis server  250  rather than by individual vehicles  210  and  220 . In such implementations, the vehicles  210  and  220  might only collect and transmit vehicle driving data to a driving analysis server  250 , and thus the vehicle-based driving analysis computers  214  and  224  may be optional. 
     Driving analysis computers  214  and  224  may be implemented in hardware and/or software configured to receive vehicle driving data from vehicle sensors  211  and  221 , short-range communication systems  212  and  222 , telematics devices  213  and  223 , vehicle control computer  217  and  227 , autonomous driving systems, and/or other driving data sources. Vehicle sensors/OBDs  211  and  221 , short-range communication systems  212  and  222 , telematics devices  213  and  223 , vehicle control computer  217  and  227 , autonomous driving systems, and/or other driving data sources can be referred to herein individually or collectively as a vehicle data acquiring component. The driving analysis computer  214 ,  224  may comprise an electronic receiver to interface with the vehicle data acquiring components to receive the collected data. After receiving, via the electronic receiver, the vehicle driving data from, for example, a vehicle data acquiring component, the driving analysis computers  214  and  224  may perform a set of functions to analyze the driving data and determine properties related to vehicle insurance. 
     For example, the driving analysis computers  214  and  224  may include one or more distance-based autonomous driving insurance factor algorithms, which may be executed by software running on generic or specialized hardware within the driving analysis computers. The driving analysis computer  214  in a first vehicle  210  may use the vehicle driving data received from that vehicle&#39;s sensors  211 , along with vehicle driving data for other nearby vehicles received via the short-range communication system  212 , to determine a distance-based autonomous driving insurance factor and determine properties related to vehicle insurance applicable to the first vehicle  210  and the other nearby vehicles. Within the driving analysis computer  214 , a vehicle insurance property function may use the results of the driving analysis performed by the computer  214  to determine/adjust a property of an insurance policy associated with the vehicle  210  and/or a driver of a vehicle  210 . Further descriptions and examples of the algorithms, functions, and analyses that may be executed by the driving analysis computers  214  and  224  are described below, including in reference to  FIGS. 3, 4, and 5 . 
     As another example, the driving analysis computers  214  and  224  may include one or more algorithms configured to generate vehicle insurance rates based on driver-independent variables and/or driver-dependent variables, generate vehicle insurance rates based on changes in the level of autonomy of vehicles according to one or more aspects of the disclosure, track vehicle density and/or generate vehicle insurance rates based on vehicle density, analyze use of autonomous vehicle features and/or maintenance of autonomous vehicles, and enable vehicle teaching features and/or monitor the driver&#39;s response to teaching features, as will be described in further detail in the examples below. 
     The system  200  also may include a driving analysis server  250 , containing some or all of the hardware/software components as the computing device  101  depicted in  FIG. 1 . The driving analysis server  250  may include hardware, software, and network components to receive vehicle operational data/driving data from one or more vehicles  210  and  220 , and other data sources. The driving analysis server  250  may include a driving data and driver data database  252  and driving analysis computer  251  to respectively store and analyze driving data received from vehicles and other data sources. The driving analysis server  250  may initiate communication with and/or retrieve driving data from vehicles  210  and  220  wirelessly via telematics devices  213  and  223 , mobile devices  215  and  225 , or by way of separate computing systems (e.g., computer  230 ) over one or more computer networks (e.g., the Internet). Additionally, the driving analysis server  250  may receive additional data from other non-vehicle data sources, such as external traffic databases containing traffic data (e.g., amounts of traffic, average driving speed, traffic speed distribution, and numbers and types of accidents, etc.) at various times and locations, external weather databases containing weather data (e.g., rain, snow, sleet, and hail amounts, temperatures, wind, road conditions, visibility, etc.) at various times and locations, and other external data sources containing driving hazard data (e.g., road hazards, traffic accidents, downed trees, power outages, road construction zones, school zones, and natural disasters, etc.), route and navigation information, and insurance company databases containing insurance data (e.g., driver score, coverage amount, deductible amount, premium amount, insured status) for the vehicle, driver, and/or other nearby vehicles and drivers. 
     Data stored in the driving data database  252  may be organized in any of several different manners. For example, a table in database  252  may contain all of the vehicle operation data for a specific vehicle  210 , similar to a vehicle event log. Other tables in the database  252  may store certain types of data for multiple vehicles. For instance, tables may store specific data sets, including data types discussed above (e.g. road-type information, insurance data, etc.). 
     The driving analysis computer  251  within the driving analysis server  250  may be configured to retrieve data from the database  252 , or may receive driving data directly from vehicles  210  and  220  or other data sources, and may perform driving data analyses, determine distance-based autonomous driving insurance factor, and/or vehicle insurance determinations, and other related functions. The functions performed by the driving analysis computer  251  may be similar to those of driving analysis computers  214  and  224 , and further descriptions and examples of the algorithms, functions, and analyses that may be executed by the driving analysis computer  251  are described below, including in reference to  FIGS. 3 through 10 . 
     In various examples, the driving data analyses, mileage unit determinations, and/or insurance property determinations may be performed entirely in the driving analysis computer  251  of the driving analysis server  250  (in which case driving analysis computers  214  and  224  need not be implemented in vehicles  210  and  220 ), or may be performed entirely in the vehicle-based driving analysis computers  214  and  224  (in which case the driving analysis computer  251  and/or the driving analysis server  250  need not be implemented). In other examples, certain driving data analyses may be performed by vehicle-based driving analysis computers  214  and  224 , while other driving data analyses are performed by the driving analysis computer  251  at the driving analysis server  250 . For example, a vehicle-based driving analysis computer  214  may continuously receive and analyze driving data from nearby vehicles to determine certain driving characteristics (e.g., mileage units of distance traveled by the vehicle when the vehicle is engaged in autonomous driving or other data as described herein) so that large amounts of driving data need not be transmitted to the driving analysis server  250 . However, for example, after a mileage unit is determined by the vehicle-based driving analysis computer  214 , the information may be transmitted to the server  250 , and the driving analysis computer  251  may determine if a property of the insurance policy should be updated. 
       FIG. 3  and  FIG. 4  are flow diagrams illustrating example methods of determining a property of an insurance policy based on analysis of vehicle operational data of vehicles engaged in autonomous driving.  FIG. 3  includes an example step of determining a distance-based autonomous driving insurance rating factor. The examples of  FIG. 3  and  FIG. 4  may be performed by one or more computing devices in a driving analysis system, such as vehicle-based driving analysis computers  214  and  224 , a driving analysis computer  251  of a driving analysis server  250 , user mobile computing devices  215  and  225 , and/or other computer systems. 
     The steps shown in  FIG. 3  describe performing an analysis of vehicle operational data to determine a distance-based autonomous driving insurance rating factor of vehicles engaged in an autonomous driving and determining a property of an insurance policy based on the factor. In step  301 , vehicle operational data may be received from a first vehicle  210 . As described above, a driving analysis computer  214  may receive and store vehicle driving data from a vehicle data acquiring component, including but not limited to the vehicle&#39;s internal computer systems and any combination of the vehicle&#39;s sensors/OBD  211  and/or communication systems. The data received in step  301  may include, for example, an identifier that the vehicle is engaged in autonomous driving. The data received in step  301  may include, for example, the location, speed, direction of travel, distance traveled, distance traveled while engaged in autonomous driving, object proximity data from the vehicle&#39;s external cameras and proximity sensors, and data from the vehicle&#39;s various systems used to determine if the vehicle  210  is braking, accelerating, or turning, etc., and status of the vehicle&#39;s user-operated controls (e.g., head lights, turn signals, hazard lights, radio, phone, etc.), along with any other data collected by vehicle sensors/OBD  211  or data received from a nearby vehicle. 
     In step  302 , the vehicle operational data is analyzed to determine one or more mileage units. As used herein, a mileage unit indicates a distance traveled by the vehicle when the vehicle was engaged in autonomous driving for at least a portion of the distance traveled. For example, in an embodiment, a first mileage unit can indicate a total distance traveled by the vehicle when the vehicle was engaged in autonomous driving over a period of time. In addition, for example, a second mileage unit can indicate a total distance traveled by the vehicle over the same period of time as the first mileage unit. In such examples, the first mileage unit would be equal to the second mileage unit if the vehicle was engaged in autonomous driving over the entire distance driven over period of time. Or, the first mileage unit would be less than the second mileage unit if the vehicle was engaged in autonomous driving for only a portion of the distance driven over the period of time. 
     The driving analysis computer may determine a mileage unit from the vehicle operational data in multiple ways. The manner in which a mileage unit is determined may depend on the type of information included in the driving data. For example, the driving data may include an identifier which indicates that the vehicle is engaged in autonomous driving and information indicating distance traveled by the vehicle. Information indicating distance traveled may be obtained from, for example, the vehicle odometer, trip meter, and/or other distance measuring device of the vehicle. In addition, distance traveled information can be determined from other driving data including, for example, time and speed information and/or location information, such as GPS. Example algorithms using time marked driving data are included in US Publications Number 2013/0073112 which is hereby incorporated by reference herein in its entirety. In addition, mileage units can be determined to indicate a distance traveled by the vehicle over a single trip, multiple trips, a period of time, and/or in an ongoing tally. Mileage units can also be determined from, for example, contiguous or non-contiguous distances traveled by the vehicle. Mileage units can also be determined from, for example, distances traveled when at least one other condition is satisfied during travel, including a distance traveled over a certain road-type, driving during a certain weather condition, and/or driving in a certain location. A period of time can be, for example, a six-month term of an insurance policy associated with the vehicle. In addition, for example, a period of time can be a month, a week, a day, a hour, a second, and/or multiples or combinations of the same. 
     In an example, the driving analysis computer can determine a mileage unit using an autonomous driving identifier to determine when the vehicle was engaged in autonomous driving and the distance traveled information collected from a distance measuring device of the vehicle to determine any number of various distances traveled by the vehicle when the vehicle was engaged in autonomous driving for at least a portion of the distance traveled. In an example, a mileage unit indicating a total distance traveled by the vehicle when the vehicle was engaged in autonomous driving over a sixth month period of time can be determined by adding all the distance segments traveled within the six-month time period when the autonomous driving indicator indicates that the vehicle was engaged in autonomous driving. 
     In certain embodiments, a mileage unit can be determined based on driving data additional to a distance traveled by the vehicle when the vehicle was engaged in autonomous driving for at least a portion of the distance traveled. For example, such additional driving data can include, for example, period of time, a road-type (e.g. highway, side road, etc.), road condition, speed, driver data, weather condition, time-of-day, driving event or action, congestion level, location, etc. For example, a first mileage unit can be determined to indicate a total distance traveled by the vehicle over a first road-type when the vehicle was engaged in autonomous driving for at least a portion the distance and a second mileage unit can be determined to indicate a total distance traveled by the vehicle over the first road-type when the vehicle was engaged in autonomous driving. In such example, the first mileage unit indicates total distance traveled over the road-type, and the second mileage unit indicates the amount of such total for which the vehicle was engaged in autonomous driving. 
     In step  303 , the mileage units determined in step  302  may be used to determine a distance-based autonomous driving insurance rating factor. In addition, in an example, in step  303 , the mileage units determined in step  302  and additional driving data may be used to determine a distance-based autonomous driving insurance factor. In an embodiment, a mileage unit determined in step  302  and additional driving data are input variables used to determine a distance-based autonomous driving insurance factor. For example, in step  303 , the mileage units determined in step  302  and an autonomous driving system quality rating may be used to determine a distance-based autonomous driving insurance factor. In another example, in step  303 , the mileage units determined in step  302  and an autonomous driving characteristic or event determined from the driving data can be used to determine a distance-based autonomous driving insurance factor. An autonomous driving characteristic or autonomous driving event can include, for example, actions performed or events undertaken by the vehicle or nearby vehicles—such as that the vehicle was a lead vehicle in an autonomous droning relationship or that the vehicle engaged in self-parking. 
     In step  304 , a property of an insurance policy may be determined using the distance-based autonomous driving insurance rating factor. The property of an insurance policy can include any of a number of aspects of a vehicle insurance policy. For example, a property of an insurance policy can include a premium, a deductible, a coverage term, a coverage amount, or other attribute of an insurance policy. In various embodiments, the property can be determined in accordance with rules set forth by the insurance provider. For example, the property of the vehicle insurance policy may change depending upon any number of driving data points, driver information, and other information. For example, in step  304 , a distance-based autonomous driving insurance factor may be determined using one or more mileage units determined in step  303 . For example, in step  304 , a distance-based autonomous driving insurance factor may be determined using a comparison between one or more mileage units. In an example, in step  304  a comparison between two or mileage units can be a ratio. For example, a ratio can be between a first mileage unit that indicates a total distance traveled by the vehicle when the vehicle was engaged in autonomous driving over the first period of time and a second mileage unit that indicates a total distance traveled by the vehicle over the first period of time. In such example, the ratio can indicate a percentage of the total distance traveled by the vehicle over the first period of time where the vehicle was engaged in autonomous driving. The ratio can be used to determine a property of a vehicle insurance policy. In an example, in step  304 , where the property of the vehicle insurance policy is a premium, a first premium rate can be applied when the ratio is above a threshold value and a second premium rate can be applied when the ratio is below the threshold value. In an example, one or more premium rates can be applied on a per-mile basis. 
     In step  305 , the driving analysis computer can adjust or cause to adjust the insurance policy based on the determined property. In various embodiments, the adjustment can occur during the coverage term and/or prior to an initial or subsequent coverage term. In addition, the policy holder may be notified of the adjustment. Alternatively, the adjustment can come in the form of a reward. Examples of using driving data to determine rewards, including driver rewards related to vehicle insurance, are disclosed in U.S. application Ser. No. 14/163,741 which is hereby incorporated by reference herein in its entirety. 
     Referring to  FIG. 4 , the steps shown in  FIG. 4  describe an example of performing an analysis of vehicle operational data to determine mileage units indicating distances traveled by a vehicle over a period of time when the vehicle was engaged in autonomous driving over at least a portion of the distance traveled. In step  401 , vehicle operational data may be received from a first vehicle  201 . In step  402 , the vehicle operational data is analyzed and a first mileage unit is determined to indicate a total distance traveled by the vehicle when the vehicle was engaged in autonomous driving over a period of time. In step  403 , the vehicle operational data is analyzed and a second mileage unit is determined to indicate a total distance traveled by the vehicle over the same period of time as for the first mileage unit. In step  404 , a premium of an insurance policy associated with the vehicle is determined using a ratio of the first mileage unit and the second mileage unit. In step  405 , a first premium rate is applied when the ratio is above a threshold value and a second premium rate is applied when the ratio is below the threshold value. 
     Referring to  FIG. 5 , an autonomous driving insurance rating factor calculator  502  may calculate the distance-based autonomous driving insurance rating factor using at least one input variable. In various embodiments, an input variable can include at least one mileage unit or at least one mileage unit and additional driving data. The additional driving data may include, but is not limited to, at least one of: vehicle speed, location, road-type, weather condition, driver score, vehicle&#39;s characteristics (e.g., vehicle type-SUV, sports car, sedan, convertible, etc., vehicle&#39;s turning radius, vehicle&#39;s maximum speed, vehicle time to accelerate from 0-60 mph, and other characteristics tied to the specific vehicle), driving risk characteristics/profile of the driver/operator, and other characteristics. 
     In an embodiment, an input variable can include an autonomous driving system quality rating. For example, assuming numerous systems exist for autonomous driving, “System A” may use hardware and/or software algorithms different from that of competing “System B.” As a result, each of the systems may react differently when used in the real world, and as such, will earn a driving risk characteristic/profile commensurate with the amount of risk associated with the particular system. In an embodiment, an autonomous driving system quality rating may indicate a rating of the likelihood of an autonomous driving system of the vehicle to avoid accidents involving the vehicle. Therefore, an autonomous driving insurance rating factor calculator  402  may take into account different quality rating/level of risk for “System A” than for “System B,” in some examples. In another example, the autonomous driving system quality rating may take into account factors such as number of accidents, moving violations, number of submitted insurance claims, and other factors known for a particular autonomous driving system. 
     In addition, referring to  FIG. 4 , other information may also be inputted into the autonomous driving rating factor calculator  402  for consideration in calculating a distance-based autonomous driving insurance rating factor or other autonomous driving insurance rating factor. For example, the congestion level (e.g., traffic) on a roadway, the weather conditions the roadway, historical occurrences of incidents (e.g., vehicular accidents) on the roadway, and other factors related to the environment/surroundings in which the vehicle is operated. For example, the autonomous driving insurance rating factor calculator  402  may adjust the factor based on the congestion level on the roadway being high. In one example, the autonomous driving insurance rating factor calculator  402  may determine a factor value which indicates elevated risk during rush hour traffic to encourage vehicles  402  equipped with an autonomous driving system to engage in autonomous driving. Congestion levels may be divided, in one example, into categories of high, medium, and low based on the whether the travel time through a particular roadway falls into the upper ⅓, middle ⅓, or lower ⅓ of possible travels times historically logged on that roadway. Likewise, weather conditions may play a role in determining risk level. For example, in a fog situation, the risk may be relatively higher for manual driving versus autonomous driving. In order to encourage the driver to engage in autonomous driving, the calculator may determine a factor which indicates elevated risk for manual driving and lower risk for autonomous driving. The driving analysis computing device can, for example, determine a deductible amount which is higher for manual driving in the fog than autonomous driving in the fog. The driving analysis computing device can notify the driver of the vehicle of the deductible amount and/or difference in deductible amount to encourage the driver to engage in autonomous driving. The notice can be delivered in real-time to, for example, a display system of the vehicle or user device, such as mobile phone of the driver. 
     The various data from the preceding examples may be stored at and retrieved from various data sources, such as an external traffic databases containing traffic data (e.g., amounts of traffic, average driving speed, traffic speed distribution, and numbers and types of accidents, etc.) about various times and locations, external weather databases containing weather data (e.g., rain, snow, sleet, and hail amounts, temperatures, wind, road conditions, visibility, etc.) at various times and locations, and other external data sources containing driving hazard data (e.g., road hazards, traffic accidents, downed trees, power outages, road construction zones, school zones, and natural disasters, etc.), and insurance company databases containing insurance data (e.g., driver score, coverage amount, deductible amount, premium amount, insured status) for the vehicle, driver, and/or other nearby vehicles and drivers. The data may, in some examples, be wirelessly transmitted from a remote server and/or database to the vehicle  220  for consideration by the autonomous driving insurance rating factor calculator  402 . As explained earlier, vehicles  210  may leverage additional hardware and/or software capabilities of another vehicle  220  or vehicles to gain access to the driving data and other information, when desired. For example, a vehicle  220  may receive, through its long-range communications circuitry  222  (or mobile phone  225 ), driving data/information and forward it to vehicles  210  via their short-range communications  212  systems. As such, the vehicles  210 ,  220  may input the information into their autonomous driving insurance rating factor calculator  402  for consideration. 
       FIG. 5  shows the autonomous driving insurance rating factor calculator  502  receiving numerous inputs and outputting a distance-based autonomous driving insurance rating factor. In some examples, the autonomous driving insurance rating factor calculator  502  may be an application-specific integrated circuit (ASIC) designed to perform the functionality described herein. In other examples, the autonomous driving insurance rating factor calculator  502  may use a processing unit (e.g., comprising a computer processor, such as an Intel™ x86 microprocessor or other special-purpose processors) and computer-executable instructions stored in a memory to cause a driving analysis computer  214  to perform the steps described herein. 
     As shown in  FIG. 2 , a single vehicle-based driving analysis computer  214  may receive driving data for a first vehicle  210  (steps  301 ,  401 ), including driving data received from V2V communications including driving data for one or more other vehicles, may determine from the data whether the vehicle is engaged in an autonomous driving, and may determine a characteristic of the autonomous driving (step  302 ,  402 , and  403 ), determine a property of an insurance policy based on the characteristic (step  304 ,  404 ), and adjust the insurance policy based on the determined property (step  305 ). However, other driving analysis computers and/or other computing devices may be used to some or all of the steps and functionality described above in reference to  FIGS. 3, 4, and 5 . For example, any of steps  301 - 305 ,  401 - 405  may be performed by a user&#39;s mobile device  215  or  225  within the vehicles  210  or  220 . These mobile devices  215  or  225 , or another computing device  230 , may execute software configured to perform similar functionality in place of the driving analysis computers  214  and  224 . Additionally, some or all of the driving analysis functionality described in reference to  FIGS. 3, 4, and 5  may be performed by a driving analysis computer  251  at a non-vehicle based driving analysis server  250 . For example, vehicles  210  and  220  may be configured to transmit their own vehicle sensor data, and/or the V2V communications data received from other nearby vehicles, to a central driving analysis server  250  via telematics devices  213  and  223 . 
     While systems already exist for autonomous vehicles, such as the self-driving car by GOOGLE™, the spirit of this disclosure is not limited to just autonomous self-driving cars. For example, the vehicle  220  may be a completely autonomous vehicle, semi-autonomous vehicle, or a manual human-driven vehicle. As used herein, the term autonomous (e.g., autonomous vehicle) may refer to either semi-autonomous (e.g., semi-autonomous vehicle) or completely autonomous (e.g., completely autonomous vehicle). Depending on the capabilities of the vehicle  220 , the vehicle may be equipped with the appropriate sensors  221  and other electronic components to enable the automation/semi-automation, as is already known in the relevant art of autonomous/semi-autonomous vehicles. Similarly, an autonomous drone vehicle may be equipped with the appropriate hardware and software to operate as an autonomous vehicle, semi-autonomous vehicle, or a manually-driven vehicle. In contrast, however, in some examples, an autonomous drone vehicle may be equipped with less hardware and/or software than a vehicle with complete autonomous capability because to some extent, the a drone vehicle may rely upon the lead vehicle to provide guidance and commands for controlling the speed, acceleration, braking, cornering, route, and other operation of the following vehicle. For example, a following drone vehicle may transmit data to the lead vehicle using its short-range wireless communications system, and rely upon long-range wireless communication capabilities of the lead vehicle to forward the data to the appropriate final destination. At least one benefit of such an arrangement is that the cost/price of a following drone vehicle may be less than that of other vehicles (e.g., lead vehicle) due to reduced complexity and reduce hardware and/or software requirements. In an embodiment, an autonomous driving system quality rating takes into account whether a vehicle is equipped for autonomous droning. 
     In addition, the integrity of collected vehicle driving data may be validated by comparing, e.g., by a driving analysis computer, the driving data (e.g., location, speed, direction) from one vehicle&#39;s sensors  211  with corresponding driving data from a nearby vehicle  220 . In one example, driving data of the nearby vehicle can be collected by a data acquiring component of a following/drone vehicle  210  via, for example, vehicle V2V. In one example, the driving data of the nearby vehicle may be directly received from the nearby vehicle. 
       FIG. 6  is a flow diagram illustrating an example method of generating vehicle insurance rates based on driver-independent variables and/or driver-dependent variables according to one or more aspects of the disclosure. The steps illustrated in  FIG. 6  may be performed by one or more computing device  101 . For example, a vehicle computing device (e.g., driving analysis computer  214  or vehicle control computer  217 ) and/or a driving analysis computing device (e.g., driving analysis computer  251 ) may perform one or more of the steps illustrated in  FIG. 6 . 
     In step  606 , a computing device may determine whether a request to determine a property of an insurance policy, such as an insurance quote for a vehicle, has been received. If a request has not been received (step  606 : N), the computing device may wait until a request has been received. Alternatively, the computing device might not wait for a request before determining the property of the insurance policy. 
     In step  608 , the computing device may determine whether the vehicle to be quoted is completely autonomous. If the vehicle is not completely autonomous, the computing device may determine whether the vehicle is semi-autonomous in step  614 . The computing device may make each of these determinations by comparing the features of the vehicle with the features included in an exemplary (or standard) completely autonomous vehicle or an exemplary (or standard) semi-autonomous vehicle. The features for exemplary vehicles may be stored in, for example, a database, such as database  252 . 
     Completely autonomous vehicles may have more autonomous features than semi-autonomous vehicles. For example, the standard completely autonomous vehicle may have a first group of features, such as autonomous speed control, autonomous steering, autonomous braking, and autonomous parallel parking. The standard semi-autonomous vehicle may have a second group of features, which may be a subset of the first group of features. For example, the standard semi-autonomous vehicle may have autonomous braking and autonomous parallel parking. These classifications may be stored in a database. 
     If the vehicle to be quoted has all of the features of the standard completely autonomous vehicle (e.g., based on a comparison of the features of the vehicle to be quoted and the features of the standard completely autonomous vehicle), the computing device may determine that the vehicle to be quoted is a completely autonomous vehicle (step  608 : Y). If the vehicle to be quoted has all of the features of the standard semi-autonomous vehicle, the computing device may determine that the vehicle is a semi-autonomous vehicle (step  614 : Y). If the vehicle does not have all the features of the standard semi-autonomous vehicle (step  614 : N), the computing device may determine that the vehicle is a non-autonomous or standard vehicle. 
     While three different classifications have been described (e.g., completely autonomous, semi-autonomous, and non-autonomous), any number of classifications may exist. For example, five different classifications may be used: (1) assisted, (2) partial automation, (3) conditional automation, (4) high automation, and (5) full automation. In assisted mode, the vehicle&#39;s computer-operated systems may assist in emergency situations. The system takes over either steering or acceleration in specific modes using information about the driving environment. The driver may do everything else. Exemplary autonomous features in the assisted mode may include lane keeping automation, cruise control, electronic stability control, and automatic braking. 
     In partial automation mode, the automation system may take control of steering and acceleration in specific driving modes using information about the driving environment. The driver may do everything else. This mode may be beneficial in low speed environments, if there are no (or very few) intersections, and the driver is alert. Exemplary autonomous features in the partial automation mode may include traffic jam assist and adaptive cruise control, in addition to or instead of the autonomous features in the assisted mode. 
     In conditional automation, the system may perform all (or most) aspects of the dynamic driving task in specific driving modes. The driver may be available to respond to a request by the autonomous system to intervene. For example, the driver may be present in driver&#39;s seat but would not have to stay alert to the driving environment. Exemplary autonomous features in the conditional automation mode may include a traffic-jam autopilot system, in addition to of instead of the autonomous features in the partial automation mode. 
     In high automation mode, the system may perform all aspects of the dynamic driving task in specific driving modes, even if the human driver does not respond appropriately to a request to intervene. For example, a full freeway autopilot system may be used. The driver, in some circumstances, may input a desired destination but might not be expected to take an active role in driving the vehicle. Exemplary autonomous features in the high automation mode may include a full freeway autopilot system, in addition to or instead of the autonomous features in the partial automation mode. 
     In full automation mode, the system may perform all aspects of the dynamic driving task in all driving modes under all roadway conditions. Vehicles running in this mode may include truly driverless cars, such as robotic taxis. Exemplary autonomous features in the high automation mode may include autonomous features in addition to or instead of the autonomous features in the high automation mode. 
     Any number of levels of vehicle automation may be used, including those defined by standards-setting organizations, such as the National Highway Traffic Safety Administration (NHTSA) and SAE International. For example, the definitions available in the NHTSA&#39;s “Preliminary Statement of Policy Concerning Automated Vehicles” may be used, the entirety of which is hereby incorporated by reference. “Preliminary Statement of Policy Concerning Automated Vehicles,” NHTSA, May 30, 2013, available at http://www.nhtsa.gov/staticfiles/rulemaking/pdf/Automated_Vehicles_Policy.pdf. As another example, the definitions available in SAE International&#39;s “Summary of Levels of Driving Automation for On-road Vehicles,” may be used, the entirety of which is hereby incorporated by reference. Bryant Walker Smith, “Summary of Levels of Driving Automation for On-road Vehicles,” Center for Internet and Society, Stanford Law School, Dec. 18, 2013, available at http://cyberlaw.stanford.edu/blog/2013/12/sae-levels-driving-automation. As will be described in further detail in the examples below, the classification of the vehicle may be used to determine the types of variables (e.g., driver-independent variables or driver-dependent variables) considered in generating an insurance quote or other property of an insurance policy for the vehicle. 
     The vehicle classification may change between completely autonomous, semi-autonomous, and non-autonomous over time. In some aspects, a change in the classification system may prompt a change in the vehicle classification. For example, at a first time, a vehicle may be classified as completely autonomous if it has feature A, feature B, feature C, and feature D. At a second time later than the first time, the classification system may change the definition of a completely autonomous vehicle to a vehicle that has features A-D along with feature E. If the quoted vehicle has features A-D, but not feature E, it may be classified as a completely autonomous vehicle at the first time. However, the classification may change from completely autonomous to semi-autonomous in the future (e.g., at the second time) based on the change in the classification system. 
     The vehicle&#39;s classification may also change based on a change in the vehicle&#39;s autonomous features. For example, assume that a vehicle is classified as completely autonomous if it has features A-D. The vehicle may have been classified as a semi-autonomous if, in the past, the vehicle had features A-C. However, the vehicle may be updated with feature D (e.g., an automated steering feature) at a later time. The computing device may re-classify the vehicle as completely autonomous at the later time. 
     If the vehicle is completely autonomous (step  608 : Y), the computing device, in step  610 , may determine one or more driver-independent variable used to generate an insurance quote for the vehicle. The driver-independent variable may comprise a vehicle identifier, such as a Vehicle Identification Number (VIN). The computing device may obtain the vehicle identifier from the driver or retrieve it from a database, such as a driver motor vehicle (DMV) database, a database of the insurance provider if the driver previously provided the vehicle identifier, or any other database. Using the vehicle identifier, the computing device may determine additional data associated with the vehicle identifier, such as the make, year, and model of the vehicle, the autonomous or safety features of the vehicle, or any other information for the vehicle. The computing device may provide the vehicle identifier to a database or search engine, such as a VIN system, and the database or search engine may provide the computing device with the additional information. 
     In some aspects, the vehicle information in a VIN system might not be up to date. For example, a newer vehicle, such as a vehicle released within the last year, might have new safety and/or autonomous features that are not currently associated with the VIN for the vehicle. A user, such as an agent or administrator, may update the VIN system by manually adding new vehicle information to the VIN system or updating existing information in the system. For example, the user may input, through a computing device, the autonomous features for a particular type of vehicle or for a particular VIN into the VIN system. Accordingly, the autonomous features for each VIN in the VIN system may be kept up to date. 
     In step  612 , the computing device may generate an insurance quote or other property of an insurance policy based on one or more of the driver-independent variables determined in step  610 . As previously discussed, the property of the insurance policy may comprise a premium, a deductible, a coverage term, a coverage amount, or any other insurance property. In some aspects, the computing device may generate a quote based on the identifier of the vehicle, and not based on any driver-dependent variables. In other words, the computing device may generate a quote and/or bind based on the vehicle identifier alone and not based on personal information of the driver (e.g., age, credit score, driving history, etc.) because the driver&#39;s characteristics might not matter if the vehicle is completely autonomous. 
     The vehicle&#39;s classification may change from semi-autonomous to completely autonomous, as previously discussed. Correspondingly, the computing device may change the insurance quote to be based on driver-independent variables and not based on driver-dependent variables. As vehicles become more and more autonomous, fewer driver-dependent rating variables may be introduced and a number of them may be eliminated or rolled off. Greater reliance may be placed on driver-independent variables, such as the VIN for quotes, binds, and/or discounts. 
     If the vehicle to be quoted is not completely autonomous (step  608 : N) but is semi-autonomous (step  614 : Y), the computing device, in step  616 , may determine one or more driver-independent variable for generating a quote, as previously discussed with reference to step  610 . Because the vehicle is semi-autonomous, characteristics of the driver may be relevant, and the computing device may also rely on driver-dependent variables to generate the quote. Weights may be used for the driver-independent variables and/or driver-dependent variables to emphasize some variables over other variables. 
     In step  618 , the computing device may determine one or more weights for the driver-independent variables determined in step  616 . In step  620 , the computing device may determine one or more driver-dependent variable used to generate the quote. Exemplary driver-dependent variables include, but are not limited to, the age of the driver, a geographic location of the driver (e.g., the location of the driver&#39;s residence), driving record, credit score, whether the driver owns a home, and the like. In step  622 , the computing device may determine one or more weights for the driver-dependent variables. 
     In step  624 , the computing device may generate an insurance quote (or other property of an insurance policy) based on the variables and weights. Several non-limiting examples of this determination will now be provided. For these examples, the following exemplary driver-independent variables will be used: vehicle model and vehicle year. The following exemplary driver-dependent variables will also be used: driver&#39;s age and driver&#39;s residence. 
     The computing device may determine a value for each of the variables considered. In some aspects, the values may be on the same scale, such as a 1-10 scale. The computing device may determine the values by comparing a characteristic of the driver or of the vehicle with a database that correlates characteristics with values. For example, the computing device may assign a value of 4 for the vehicle model variable if the vehicle is a small 2-door automobile, and the database correlates small 2-door automobiles with the value of 4. Similarly, the computing device may assign a value of 9 for the vehicle year variable if the vehicle was manufactured in 2013, and the database correlates 2013 vehicles with the value of 9. The computing device may assign a value of 3 for the driver&#39;s age variable if the driver is 20 years old, and the database correlates drivers between the ages of 18 and 24 with the value of 3. Finally, the computing device may assign the value of 8 for the driver&#39;s residence if the driver lives in Iowa, and the database correlates drivers residing in Iowa with the value of 8. 
     The computing device may also determine a weight for each variable (vehicle model, vehicle year, driver&#39;s age, and driver&#39;s residence) or for each type of variable (driver-independent or driver-dependent). In some aspects, the weights may be used to emphasize one or more variables over one or more other variables. For example, a weight of 0.20 may be applied to the vehicle model variable, a weight of 0.40 may be applied to the vehicle year, a weight of 0.10 may be applied to the driver&#39;s age, and a weight of 0.30 may be applied to the driver&#39;s residence. In this example, the vehicle may be given a score of 7.10 (e.g., 0.20*4+0.40*9+0.10*3+0.30*8=7.10). The score and/or the weights and variables determined in steps  616 ,  618 ,  620 , and  622  may be used by the computing device to generate the quote in step  624 . 
     In some aspects, weights for the same types of variables (e.g., driver-independent or driver-dependent) may be the same. For example, driver-independent variables may each be assigned the weight of 0.20. Driver-dependent variables may each be assigned the weight of 0.30. In this example, the vehicle may be given a score of 5.90 (e.g., 0.20*4+0.20*9+0.30*3+0.30*8=5.90). 
     The weights may depend on the level of autonomy of the vehicle. The more autonomous the vehicle, the greater the weights for the driver-independent variables and the smaller the weights for the driver-dependent variables. If the vehicle changes from semi-autonomous to completely autonomous, the weights for the driver-dependent variables may drop to 0 (or drop to near zero) so that driver-dependent variables are not considered (or are at least weighted less) when determining an insurance quote for the vehicle. Determining the level of autonomy will be described in further detail below in reference to  FIG. 7 . 
     In step  626 , the computing device may generate a standard quote if the vehicle is a non-autonomous vehicle or otherwise does not qualify as completely autonomous or semi-autonomous. 
       FIG. 7  is a flow diagram illustrating an example method of generating vehicle insurance rates based on changes in the level of autonomy of vehicles according to one or more aspects of the disclosure. The steps illustrated in  FIG. 7  may be performed by one or more computing device  101 . For example, a vehicle computing device (e.g., driving analysis computer  214  or vehicle control computer  217 ) and/or a driving analysis computing device (e.g., driving analysis computer  251 ) may perform one or more of the steps illustrated in  FIG. 7 . 
     In step  702 , the computing device may optionally determine whether an event has occurred, such as receiving a request for a vehicle re-quote and/or determining that the level of autonomy of the insured vehicle has changed since the time of the original quote or bind. For example, the computing device may automatically detect a change in the level of autonomy of the vehicle, and generate a quote in response to the detection. The vehicle may send data identifying its level of autonomy to the computing device in response to a change in the level of autonomy or periodically (e.g., weekly, monthly, etc.). Additionally or alternatively, the computing device may retrieve information identifying a vehicle&#39;s level of autonomy from a database, such as a third party database. The database may be updated with level of autonomy information for a plurality vehicles or vehicle types (e.g., make, model, year, etc.). The computing device may retrieve the correct information by providing the database with the VIN, model and year, or any other information identifying the vehicle. If an event has not occurred (step  702 : N), the computing device may optionally wait for an event to occur. 
     In step  704 , the computing device may determine the level of autonomy of the vehicle to quote. The level of autonomy may be based on the overall classification of the vehicle (e.g., completely autonomous, semi-autonomous, or non-autonomous), as previously discussed. The level of autonomy may additionally or alternatively be based on each autonomous feature of the vehicle (e.g., automatic speed control, automatic steering, automatic braking, automatic parallel parking, and the like). For example, the computing device may count the number of autonomous features to determine the level of autonomy. The computing device may also apply weights to each of the autonomous features to determine the level of autonomy. For example, automatic steering may be emphasized more than automatic braking, and automatic braking may be emphasized more than automatic parallel parking. 
     The computing device may determine whether a level of autonomy of the vehicle has changed, such as from the last time a quote or bind was generated for the vehicle. For example, in step  706 , the computing device may determine whether the level of autonomy increased. If the level of autonomy has not increased (step  706 : N), the computing device may determine whether the level of autonomy has decreased in step  714 . If the level of autonomy has not changed, the computing device may maintain the original quote or bind in step  720 . 
     The computing device may determine that the level of autonomy of the vehicle has changed if the classification of the vehicle has changed, which (as previously discussed) may have been caused by a change in the classification system or a change in the autonomous features of the vehicle. The computing device may additionally or alternatively detect a change in the level of autonomy based on a modification, addition, or removal of an automated feature. For example, if the software or hardware for an autonomous feature, such as autonomous braking, has been upgraded, the computing device may determine that the level of autonomy increased (step  706 : Y). Alternatively, the computing device may determine that the level of autonomy decreased if an autonomous feature is removed and/or has been disabled (step  714 : Y). 
     In response to a determination that the level of autonomy of the vehicle has changed, the computing device may adjust (e.g., increase or decrease) the weights for one or more of the variables. For example, the computing device may increase a weight for a driver-independent variable in step  708  and/or decrease a weight for a driver-dependent variable in step  710  if the level of autonomy has increased (step  706 : Y). On the other hand, the computing device may decrease a weight for the driver-independent variable in step  716  and/or increase a weight for the driver-dependent variable in step  718  if the level of autonomy has decreased (step  714 : Y). 
     In step  712 , the computing device may generate a quote (or other property of the insurance policy) using the new weights. For example, if the vehicle is now more autonomous, the computing device may determine the quote based on at least one of an increased weight for a driver-independent variable of the vehicle and a decreased weight for a driver-dependent variable of the vehicle. The weights for the driver-dependent variables may eventually drop to 0, and a quote may be based on one or more driver-independent variables, but not based on driver-dependent variables. This may occur, for example, if the vehicle becomes classified as completely autonomous. If, on the other hand, the vehicle is now less autonomous, the computing device may determine the quote based on at least one of a decreased weight for a driver-independent variable of the vehicle and an increased weight for a driver-dependent variable of the vehicle. 
     In some aspects, the level of autonomy may change multiple times, such as during one or more of the driver&#39;s trips with the vehicle. The computing device may determine an average or median level of autonomy and generate a value for a property of the insurance policy based on the average or median level of autonomy. Additionally or alternatively, the value for the property of the insurance policy may vary over time. For example, the computing device may determine that the vehicle was used at a first level of autonomy over a first period of time (e.g., April). During a second period of time after the first (e.g., May), the computing device may determine that the vehicle was used at a second level of autonomy greater than the first level of autonomy. Accordingly, the computing device may vary, such as decrease, the value for the property of the insurance policy (e.g., deductible, premium, and the like) for the second period of time relative to the value for the first period of time. Thus, variable rate insurance may be generated based on the level of autonomy of the vehicle over predetermined periods of time (e.g., each month, every 3 months, and the like). The vehicle, driver, or owner may be notified of the decrease or increase in the value of the insurance policy, which may encourage the driver or owner of the vehicle to use more autonomous features to decrease the amount of the deductible or premium. 
       FIG. 8  is a flow diagram illustrating an example method of tracking vehicle density and/or generating vehicle insurance rates based on vehicle density according to one or more aspects of the disclosure. The steps illustrated in  FIG. 8  may be performed by one or more computing device  101 . For example, a vehicle computing device (e.g., driving analysis computer  214  or vehicle control computer  217 ) and/or a driving analysis computing device (e.g., driving analysis computer  251 ) may perform one or more of the steps illustrated in  FIG. 8 . 
     In step  802 , a computing device may monitor the density (e.g., congestion) of vehicles around an insured vehicle or vehicle to be quoted (also referred to as a target vehicle). The computing device may generate and store the density data for use in determining an insurance quote for the target vehicle. In some aspects, the computing device may be a computing device within the target vehicle. The target vehicle may have one or more sensors used to determine (e.g., sense) the number of vehicles near the target vehicle. Exemplary sensors were previously discussed with reference to  FIG. 2 , and may include one or more of the following types of sensors: a camera, a proximity sensor, a vehicle-to-vehicle (V2V) communication device, and a vehicle-to-infrastructure (V2I) communication device. 
     In some aspects, the computing device may determine the number of vehicles based on a number of interactions, such as communications, between the target vehicle and other vehicles (e.g., via V2V communication devices) over a predetermined period of time. The target vehicle may also determine the number of completely autonomous vehicles, semi-autonomous vehicles, and/or non-autonomous vehicles based on the V2V communications (or lack thereof). For example, other vehicles may communicate vehicle identifying information to the target vehicle, such as make, model, year, automated features it has, VIN, etc. Based on the received information, the target vehicle may determine whether the other vehicle is completely autonomous, semi-autonomous, and/or non-autonomous. In some aspects, if another vehicle does not communicate with the target vehicle, the target vehicle may determine that the other vehicle is a non-autonomous vehicle. This information may be used to generate the insurance quote for the vehicle, as will be described in further detail in the examples that follow. 
     The computing device may also determine the number of vehicles from data stored by road infrastructure. For example, the road infrastructure may store a count of the number of vehicles that have passed a particular point on the road. When the target vehicle drives within a predetermined distance from the road infrastructure, the target vehicle may communicate with the road infrastructure (e.g., via V2I communication devices) and receive the vehicle count from the road infrastructure. The computing device may also determine the number of vehicles based on other devices capable of being used to track location, such as mobile phones and/or other Global Positioning Satellite (GPS) devices. Accordingly, the computing device may determine vehicle count and/or vehicle density independent from an absolute geographical location of the vehicle. In other words, geography might not be a factor in determining the insurance quote. 
     The computing device may additionally or alternatively determine the number of vehicles near the target vehicle and/or otherwise generate vehicle density data based on one or more databases, such as third party databases. For example, the computing device may retrieve data from a navigation tool database, such as a GOOGLE MAPS database, and determine the number of vehicles or vehicle density near the target vehicle based on the retrieved data. In some aspects, data from the vehicle&#39;s sensors and data retrieved from a database may be combined to determine the number of vehicles and/or density data. 
     Vehicle density may be based on the number of vehicles within a predetermined distance from the target vehicle. For example, the target vehicle may use its sensors to monitor the number of vehicles within a predetermined radius (e.g., 30 feet) of the target vehicle. Additionally or alternatively, the predetermined distance may be a predetermined distance of the target vehicle along a path of the target vehicle. For example, if the target vehicle is on a highway, the density measured may be from the vehicle&#39;s location to a predetermined distance in front of the vehicle along the highway (e.g., 1 mile in front of the vehicle) and/or a predetermined distance behind the vehicle along the highway (e.g., 0.5 miles behind the vehicle). 
     In step  804 , the vehicle computing device may determine whether new data is available, and in step  806 , the vehicle computing device may send the vehicle count or density data to a driving analysis computing device if new data is available. The data may be transmitted periodically (e.g., every 5 days), occasionally, and/or in real time (e.g., while the vehicle is on the road and new data is detected). In step  808 , the driving analysis computing device may receive the vehicle count or density data from the vehicle computing device. Steps  804 ,  806 , and  808  may be optional if, for example, the driving analysis computing device receives the data from other sources, such as directly from the road infrastructure or from another database. In step  810 , the driving analysis computing device may optionally determine whether to generate an insurance quote (or other property of an insurance policy) for the target vehicle. 
     The computing device may determine the density of vehicles near the target vehicle based on the vehicle number data received from the vehicle, road infrastructure, and/or various databases. In some aspects, the computing device may determine vehicle density based on vehicle type. In step  812 , the computing device may determine the density of completely autonomous vehicles near (e.g., within a predetermined distance from) the target vehicle. In step  814 , the computing device may determine the density of semi-autonomous vehicles near the target vehicle. In step  816 , the computing device may determine the density of non-autonomous vehicles near the target vehicle. 
     In step  818 , the computing device may generate an insurance quote (which may comprise a premium amount) for the target vehicle based on the determined density of vehicles. In some aspects, the computing device may determine the average and/or median density of vehicles over a particular period of time (e.g., 1 month, 3 months, etc.). The quote may be higher if the target vehicle typically travels in a denser area, such as New York City, and lower if the vehicle typically travels in a less dense area, such as Perry, Iowa. The computing device may make adjustments to the quote based on a comparison of the density of completely autonomous vehicles, the density of semi-autonomous vehicles, and/or the density of non-autonomous vehicles. For example, the quote may be higher if the density of non-autonomous vehicles is higher (e.g., above a threshold amount). Alternatively, the quote may be lower if the density of completely autonomous or semi-autonomous vehicles is higher (e.g., above a threshold amount). The computing device may also apply different weights to each of the three vehicle density numbers. The use of weights was previously discussed with reference to  FIGS. 6 and 7 . In some aspects, the quote may also be based at least in part on the level of autonomy of the target vehicle, as also discussed with reference to  FIGS. 6 and 7 . For example, the quote may be increased if the target vehicle is non-autonomous or decreased if the target vehicle is semi-autonomous or completely autonomous. 
     In step  820 , the computing device may send the vehicle insurance quote to the driver or owner of the target vehicle and/or an agent of the insurance provider. The computing device may return to step  808  to wait for updated vehicle density or count data. 
       FIG. 9  is a flow diagram illustrating an example method of analyzing use of autonomous vehicle features and/or maintenance of autonomous vehicles according to one or more aspects of the disclosure. The steps illustrated in  FIG. 9  may be performed by one or more computing device  101 . For example, a vehicle computing device (e.g., driving analysis computer  214  or vehicle control computer  217 ) and/or a driving analysis computing device (e.g., driving analysis computer  251 ) may perform one or more of the steps illustrated in  FIG. 9 . As will be described in further detail in the examples below, the computing device may generate an insurance quote or reward based on one or more factors, including the driver&#39;s response to switching between an autonomous driving feature (e.g., autonomous steering) and a manual driving feature (e.g., manual steering). 
     In step  902 , the computing device may track a driver&#39;s reaction to a vehicle switching between its autonomous driving features (or autonomous driving modes) and its manual driving features (or manual driving modes). A vehicle may have several different gradations of driving modes, between fully autonomous and fully manual. Each driving mode may be determined based on the autonomous features that are available or active. For example, a manual driving mode may have no autonomous features. A first autonomous driving mode may have autonomous features A and B active. A second autonomous driving mode more autonomous than the first autonomous driving mode may have autonomous features A, B, and C active. In a fully autonomous driving mode, the vehicle may have all (or a substantial number) of its autonomous features active, such as autonomous features A, B, C, D, E, and F. Any number of gradations may be available, and the computing device may determine the mode based on a detection of which autonomous features of the vehicle are active (or available). 
     Each driving mode may additionally or alternatively be a specific-purpose driving mode. For example, on autonomous mode may be an autonomous parking mode. In this mode, the vehicle&#39;s autonomous parking feature (e.g., parallel parking or other type of parking) may be activated. In a highway driving autonomous mode, the vehicle&#39;s autonomous steering control and autonomous speed control features may be activated. In a collision avoidance autonomous mode, the vehicles&#39; autonomous braking and autonomous steering features may be activated. Any number of driving modes with different autonomous features activated may be available. 
     Switching between the two features or modes may be beneficial for a variety of reasons. For example, the driver might not have experience with one or more of the autonomous driving features of the vehicle. The autonomous driving features may gradually be activated over time as the driver becomes accustomed to autonomous driving. A vehicle may also switch between autonomous and manual features based on present road conditions. For example, autonomous steering and speed control may be more beneficial if the vehicle density is very low where the driver is prone to dozing off. Alternatively, manual steering and speed control may be more beneficial if the vehicle density is very high, and the driver can quickly react to changing traffic conditions. Furthermore, one or more autonomous feature may be turned off in order to teach and maintain the driver&#39;s driving skills. 
     In some aspects, one or more autonomous driving feature may be switched off. For example, a computing device, which may be within the vehicle, may send an instruction to the vehicle to switch off an autonomous driving feature. The driver may be notified that the autonomous driving feature has been switched off by, for example, an audio, visual, and/or tactile notification inside the vehicle. The computing device may determine the driver&#39;s response to the autonomous driving feature being switched off. Additionally or alternatively, the computing device may determine a history of the driver&#39;s response to the autonomous driving feature being switched off over a period of time. For example, the feature may be switched off multiple times, and the driver&#39;s response to each of those instances may be tracked. 
     The driver&#39;s reaction time may be monitored. For example, if autonomous speed control is turned off, the amount of time it takes for the driver to maintain or get back up to the speed that the vehicle was traveling at with autonomous speed control active may be tracked. As another example, the vehicle may switch off its autonomous parallel parking feature. The amount of time it takes for the driver to parallel park the vehicle may be monitored. 
     In step  904 , the computing device may track the maintenance history of the vehicle. For example, the driver&#39;s response time to maintenance notifications may be monitored. Exemplary, non-limiting maintenance notifications include a check engine notification, an oil level notification, a low fuel notification, a software upgrade availability notification, a vehicle hardware or software hack notification, and the like. For example, if the driver is notified that a software upgrade for the vehicle (or a component in the vehicle) becomes available, the computing device may track how long it takes for the driver to begin downloading and/or installing the software upgrade. Similarly, if the vehicle learns that it (or a particular vehicle component) has been hacked, the computing device may track how long it takes for the driver to remediate the hack by, for example, bringing the vehicle to a service location, downloading a patch to prevent future hacks, downloading anti-hacking or anti-virus software, and/or replacing the hacked component. In some aspects, the insurance provider may also provide updates to the vehicle&#39;s security software. These updates may be automatically pushed to the vehicle. The insurance provider may charge an additional premium for the updates. 
     In step  906 , the computing device may track the vehicle&#39;s use of autonomous vehicle lanes and/or manual vehicle lanes. Certain roads may include lanes dedicated to autonomous vehicles, lanes dedicated to non-autonomous vehicles, and/or lanes available to both autonomous vehicles and non-autonomous vehicles. As will be described in further detail in the examples below, the driver&#39;s insurance quote may be decreased and/or the driver may be given discounts or other rewards the more often the driver uses the autonomous vehicle lanes. This may be beneficial to both the insurance provider and the driver because the autonomous vehicle lane may be safer than the non-autonomous vehicle lane in some environments. 
     In step  908 , the computing device may track the driver&#39;s use of other autonomous features, such as autonomous parallel parking. The more often the driver uses an autonomous feature, the greater the driver&#39;s discount may be. For example, if the driver uses autonomous parallel parking at least twice per week, a $10.00 discount on the insurance premium may be provided. If the driver uses autonomous parallel parking at least once per week, a $5.00 discount may be provided. 
     In step  910 , the computing device may optionally determine whether there is any new data from the vehicle&#39;s computing device. If not (step  910 : N), the vehicle&#39;s computing device may continue to track and store the vehicle operational data previously discussed. In step  912 , a driving analysis computing device (which may be located remotely from the vehicle&#39;s computing device) may receive the monitored data from the vehicle&#39;s computing device. 
     In step  914 , the computing device may optionally determine whether to generate a quote for the driver based on the monitored data. For example, the computing device may receive a request to generate a quote. In step  922 , the computing device may generate an insurance quote (or other property of an insurance policy) for the vehicle. The insurance quote may be based on one or more of the driver&#39;s response to an autonomous feature being switched on or off, the driver&#39;s response to maintenance notifications, the driver&#39;s use of autonomous or non-autonomous vehicle lanes, and/or the driver&#39;s use of other autonomous features (e.g., autonomous parallel parking). The data used to generate the quotes may be based on the information tracked and stored in steps  902 ,  904 ,  906 , and/or  908 . As previously discussed, the quote may be based on a score determined for the vehicle or driver. The score may be based on one or more values for each piece of data considered as well as one or more weights assigned to those pieces of data. In step  924 , the computing device may send the quote to the driver or to an agent of the insurance provider. 
     Other factors may be considered to determine the insurance quote. For example, the computing device may determine whether and how often the vehicle is driven off peak. Furthermore, the quote may be based on a record of incidents tracked by the vehicle&#39;s sensors or computing device, such as the driver momentarily veering out of a lane, the distance between the target vehicle and a vehicle in front of the target vehicle, how hard the driver brakes, or any other factors. The quote may be reduced if the driver does not have any incidents or has a limited number of incidents during a predetermined period of time that the driver&#39;s habits are tracked. 
     The computing device may determine a reward, such as a discount on a premium or deductible, alternatively to or additionally to a quote in a similar manner as it determines the quote (e.g., based on one or more of the factors previously discussed). In particular, in step  916 , the computing device may determine whether to generate a reward for the driver based on the monitored data, and in step  918 , the computing device may generate the reward based on the monitored data. A reward may be generated in lieu of the quote if the driver is, for example, already a customer of the insurance provider. In step  920 , the computing device may send the reward to the driver or to an agent of the insurance provider. 
       FIG. 10  is a flow diagram illustrating an example method of enabling vehicle teaching features and/or monitoring the driver&#39;s response to teaching features according to one or more aspects of the disclosure. The steps illustrated in  FIG. 10  may be performed by one or more computing device  101 . For example, a vehicle computing device (e.g., driving analysis computer  214  or vehicle control computer  217 ) and/or a driving analysis computing device (e.g., driving analysis computer  251 ) may perform one or more of the steps illustrated in  FIG. 10 . If a driver uses an autonomous vehicle, the driver may lose some of his or her driving skills over time. As will be described in further detail in the examples below, a vehicle may include a teaching feature that disables one or more autonomous driving features for a predetermined period of time. During this period of time, the driver may manually drive the vehicle, and the vehicle may provide instructions to the driver to teach and/or help the driver maintain his or her driving skills. 
     In step  1002 , the computing device may determine whether to enable the teaching feature of the autonomous vehicle. The teaching feature may be enabled by the vehicle&#39;s computing device and/or the insurance provider&#39;s computing device. In some aspects, the driver may enable the teaching feature in step  1002  by providing an input to the vehicle&#39;s computing device. In step  1004 , the vehicle&#39;s computing device notifies the driver that the teaching feature has been enabled, such as by providing feedback or confirmation to the driver that the teaching feature has been enabled. For example, the vehicle computing device may generate an audio, visual, and/or tactile prompt to the driver that the teaching feature has been enabled. In some aspects, a teaching feature light or icon on the car&#39;s graphical user interface may display an indication that the teaching feature is enabled. The teaching feature may also be disabled at any time, such as if the driver instructs the vehicle to disable the teaching feature. 
     In step  1006 , the computing device may monitor current conditions of the vehicle or the vehicle&#39;s environment, and in step  1008 , the computing device may determine whether to initiate the teaching feature based on current conditions (e.g., if a criterion is satisfied). If the computing device determines to initiate the teaching feature (step  1008 : Y), the computing device in step  1010  may notify the driver of the vehicle that the teaching feature is or will be initiated. In step  1012 , the teaching feature may be initiated, such as by switching off an autonomous vehicle feature. Non-limiting examples of steps  1006 ,  1008 ,  1010 , and/or  1012  will now be provided. 
     In step  1006 , the vehicle computing device may monitor the density of vehicles within a predetermined distance from the vehicle, as previously discussed with reference to  FIG. 8 . If the vehicle density drops below a threshold, the computing device may determine to initiate the teaching feature in step  1008 , and notify the driver, in step  1010 , that the teaching feature is or will be initiated. If the driver is notified in step  1010 , the computing device may delay initiating the teaching feature for a predetermined amount of time, such as 5 seconds, to give the driver time to prepare. The driver may also be notified by visual, audio, and/or tactile information that the teaching feature will be initiated in 5 seconds. For example, the vehicle may display (or audibly speak) a countdown to initiation of the teaching feature. In step  1012 , the computing device may initiate the teaching feature. For example, the computing device may switch off autonomous steering or autonomous speed control for a predetermined period of time (e.g., 10 minutes). 
     The vehicle&#39;s teaching feature may also be used to teach the driver how to react in particular conditions or environments. For example, the driver&#39;s merging skills may be kept up to date by switching off autonomous driving before the vehicle enters onto a ramp. The computing device may determine the vehicle&#39;s location and/or route, such as by tracking the driver&#39;s location using his or her mobile phone or any other location tracking mechanism. If the driver&#39;s current route will take the driver onto a ramp, the computing device may notify the driver that one or more autonomous driving feature will be switched off prior to the vehicle entering the ramp. The driver&#39;s ability to handle the vehicle in any other driving situation (e.g., switching lanes, bumper-to-bumper traffic, turning, etc.) may similarly be tested or refreshed by the computing device in a similar manner. 
     In step  1014 , the computing device may monitor the driver&#39;s reaction (e.g., response) to the teaching feature. For example, the computing device may determine and/or store the driver&#39;s reaction to the autonomous steering or autonomous speed control being switched off. The computing device may determine the amount of time that the driver is below the speed limit and the amount of time that the driver is above the speed limit during the teaching period. The computing device may also track the number of times that the driver erroneously crosses a driving lane marking or divider (e.g., if the driver veers across a lane marker without intending to switch lanes or drives onto a shoulder of the road). If the driver is being taught how to merge, the computing device may determine, for example, the number of times that the driver brakes during his or her merge. 
     In step  1016 , the computing device may determine whether the driver&#39;s reaction to the teaching feature is unsafe at any point during the teaching period. If the driver&#39;s reaction is unsafe (step  1016 : Y), the computing device may disable the teaching feature by, for example, reactivating the automated driving feature in step  1020 . The driver&#39;s reaction may be unsafe if the driver&#39;s reaction exceeds a threshold. For example, if the driver&#39;s speed exceeds the posted speed limit by a predetermined amount, such as 20 miles per hour over the speed limit, the computing device may reactivate autonomous speed control if it was deactivated for teaching. As another example, if the driver swerves into another lane or onto the shoulder by a predetermined distance, such as two feet, the computing device may reactivate autonomous steering if it was deactivated for teaching. As yet another example, if the driver brakes too hard (which may be measured by the amount of force applied to the brakes and/or the amount of deceleration of the vehicle), the computing device may reactivate autonomous driving if it was deactivated to refresh the driver&#39;s merging skills. 
     In step  1018 , the computing device may determine whether the condition of the vehicle or the vehicle&#39;s environment is no longer safe for teaching (e.g., that the condition originally satisfied in step  1008  is no longer satisfied). If the environment is unsafe for teaching, the computing device may disable the teaching feature in step  1020 . For example, the current conditions might not be safe for teaching if the vehicle density exceeds a predetermined threshold. The threshold may be the same as the threshold used to initiate the teaching feature in step  1008 , or the thresholds may be different. 
     In step  1022 , the computing device may determine whether the teaching period has ended. For example, the teaching period may last a predetermined amount of time after initiation of the teaching feature, such as 10 minutes. Alternatively, the teaching period may end when the driving action taught to the driver has ended, such as when the driver has successfully merged onto a highway. If the teaching period has not ended (step  1022 : N), the computing device may continue to monitor for an unsafe response in step  1016  and/or unsafe conditions in step  1018 . 
     If the teaching period has ended (step  1022 : Y), the computing device, in step  1024 , may generate a score based on the driver&#39;s reaction(s) during the teaching period. As previously discussed, the score may be based on one or more values and/or weights. For example, the score may be on a scale of 0 to 100, with 100 being the best or preferred response. The computing device may generate a score of 100 if the vehicle does not detect any mistakes during the teaching period. For example, the driver may receive a score of 100 if the vehicle does not exceed the speed limit (or a predetermined amount above the speed limit, such as 5 miles per hour above the speed limit) with the autonomous speed control switched off. Deductions may be made each time the driver exceeds the speed limit and/or based on a comparison of the amount of time that the driver spends above the speed limit and an amount of time that the driver spends at or below the speed limit. For example, if the teaching period is 10 minutes, and the vehicle spends 7 minutes at or below the speed limit, the computing device may generate a score of 70% (e.g., 7/10). 
     As another example, the driver may receive a score of 100 if the vehicle does not cross onto the shoulder of the road with autonomous steering switched off. Deductions may be made for each instance of the vehicle crossing onto the shoulder and/or based on a comparison of the amount of time the vehicle spends in driving lanes and the amount of time the vehicle spends at least partially in the shoulder lane. As yet another example, the driver may receive a score of 100 if the vehicle does not brake at all when the driver&#39;s merging skills are tested. Deductions may be made each time the driver applies the brakes. 
     In step  1026 , the computing device may determine whether the score generated in step  1024  exceeds one or more score threshold. In some examples, the threshold may determine whether the driver&#39;s response was sufficient or not. In step  1028 , the computing device may provide a reward to the driver if the driver&#39;s score exceeds the threshold (step  1026 : Y). Exemplary rewards were previously described, and may include, for example, a reduction in an insurance premium, a gift, such as a free movie rental or free gas, or any other reward. 
     On the other hand, if the driver&#39;s score does not exceed the threshold (step  1026 : N), the computing device, in step  1030 , may provide one or more recommendations to the driver to help the driver improve or refresh his or her driving skills In some aspects, the recommendation may be given to the driver while the driver is still in the vehicle. For example, a voice prompt may recommend that the driver slow down next time if the driver spent too much time above the speed limit with autonomous speed control deactivated. Other recommendations may be provided based on which autonomous feature was deactivated. The recommendations may additionally or alternatively be provided to the driver visually, such as on a display of the vehicle (e.g., an LCD screen, a heads-up-display (HUD), and the like). 
     As previously discussed with reference to  FIG. 9 , the computing device may also determine an amount of a property of an insurance policy for the vehicle, such as an insurance premium amount, based on the score generated in step  1024 . 
     A new insurance policy may be created for semi-autonomous or completely autonomous vehicles. In particular, traffic accidents may be less and less likely caused by the driver as vehicles utilize more and more autonomous features. As such, liability may be allocated amount multiple parties, including (1) the driver, (2) manufacturers (or servicers) of malfunctioning autonomous features (e.g., malfunctioning autonomous steering, braking, and/or speed control), (3) manufacturers (or servicers) of malfunctioning roadway infrastructure or malfunctioning V2I or I2V communication systems, and/or (4) third parties that illegally accesses (e.g., hack) the vehicle&#39;s autonomous driving system. 
     In some aspects, the driver may be primarily liable if the driver overrides the autonomous system or automated system warnings and causes an accident by initiating manual driving features. The driver might not be primarily liable in certain situations, such as if the teaching feature previously described is automatically enabled and initiated (e.g., without the driver&#39;s input). The manufacturer of an autonomous feature (e.g., autonomous steering) may be liable if the autonomous feature malfunctions and causes an accident. No-fault coverage may be used if an “Act of God,” such as severe weather or other interference, confuses the sensors in the vehicle, causing an accident during autonomous driving mode. 
     While the aspects described herein have been discussed with respect to specific examples including various modes of carrying out aspects of the disclosure, those skilled in the art will appreciate that there are numerous variations and permutations of the above described systems and techniques that fall within the spirit and scope of the invention.