Model development using parallel driving data collected from multiple computing systems

Abstract

This disclosure relates to model development using parallel driving data collected from multiple computing systems. Implementations include systems, vehicles, and computer-implemented methods for developing models from parallel driving datasets to identify risk levels of events in one of the driving datasets. In an exemplary embodiment, a system includes a vehicle data system operatively coupled to at least one sensor on a vehicle to collect vehicle driving data representing driving behavior. A portable data collection module is configured to cause a portable computing system capable of being delivered on the vehicle to collect portable driving data representing the driving behavior. An evaluation system is configured to receive the portable driving data and the vehicle driving data, assign risk levels to at least one event included in the vehicle driving data, and correlate the vehicle driving data with the portable driving data to identify patterns in the portable driving data that can be associated with the risk levels.

Description

Technical Field

[0001] This disclosure relates to developing models from parallel datasets of vehicle-related accidents to prospectively assess subsequent vehicle-related accidents. Background Technology

[0002] The statements in this section are provided only as background information in connection with this disclosure and do not constitute prior art.

[0003] Modern vehicles may include operator warning systems to help encourage safer driving, for example, alerting the driver when the vehicle deviates from its lane or approaches another object. Some vehicles may also include operator assistance features, as corresponding examples, that help guide the vehicle to avoid lane departure and automatically engage the steering mechanism or brakes to attempt to avoid collisions with other objects. These systems can use data from multiple sensors that monitor the driver and vehicle's operation and / or control. Data from these sensors can also prove helpful in monitoring driver behavior, making it possible to determine whether the driver was at fault in the event of a loss-making accident.

[0004] Currently, insurance companies offer smartphone apps that can be used to monitor certain driving behaviors. For example, these apps can use the GPS and accelerometer integrated into the smartphone to monitor when the vehicle is speeding, braking suddenly, or whether the driver is using their phone while driving. If the driver does not accelerate, avoids sudden braking, and is not holding their smartphone while driving, the insurance company may provide compensation to the driver.

[0005] However, avoiding actions such as sudden braking may not be an indicator of whether a driver is prudent. For example, a driver may be highly attentive, but if a car suddenly and inappropriately enters the driver's path, sudden braking may be the only way to prevent a collision. Therefore, in this example, relying solely on sudden braking data may not reliably indicate the circumstances of a particular event or the level of caution employed by the driver. Summary of the Invention

[0006] The embodiments disclosed in this invention include systems, vehicles, and methods for developing models from parallel driving datasets to identify the risk level of events in one driving dataset.

[0007] In an exemplary embodiment, a system includes a vehicle data system operatively coupled to at least one sensor on the vehicle and configured to collect vehicle driving data representing an operator's driving behavior during at least one trip. A portable data collection module is configured to cause a portable computing system, which can be delivered on the vehicle, to collect portable driving data representing the operator's driving behavior while operating the vehicle during at least one trip. An evaluation system is configured to: receive the portable driving data and the vehicle driving data; assign a risk level to at least one event included in the vehicle driving data; and correlate the vehicle driving data with the portable driving data to identify patterns in the portable driving data that can be associated with the risk levels.

[0008] In another exemplary embodiment, a vehicle includes a cabin configured to accommodate an operator, passengers, and / or cargo. A driving system is configured to enable the vehicle to start, accelerate, decelerate, stop, and steer. An operator control system is configured to allow an operator to guide the operation of the vehicle. An operator assistance system is configured to autonomously control the vehicle without operator assistance and / or assist an operator in controlling the vehicle. A vehicle data system is operatively coupled to at least one sensor on the vehicle and configured to collect vehicle driving data representing the operator's driving behavior during at least one trip. A portable data collection module is configured to cause a portable computing system, which can be delivered on the vehicle, to collect portable driving data representing the operator's driving behavior during at least one trip. An evaluation system is configured to: receive portable driving data and vehicle driving data; assign a risk level to at least one event included in the vehicle driving data; and correlate the vehicle driving data with the portable driving data to identify patterns in the portable driving data that can be associated with the risk levels.

[0009] In another exemplary embodiment, a computer-implemented method includes receiving vehicle driving data collected by a vehicle data system operatively coupled to at least one sensor on the vehicle and configured to collect data representing driving behavior of an operator operating the vehicle during at least one trip. Portable driving data is received from a portable data system capable of being delivered on the vehicle to collect data representing driving behavior of an operator operating the vehicle during at least one trip. The vehicle driving data and the portable driving data are evaluated. The evaluation includes assigning a risk level to at least one event included in the vehicle driving data. The evaluation also includes correlating the vehicle driving data with the portable driving data to identify patterns in the portable driving data that can be associated with the risk levels.

[0010] Other applicable features, advantages, and areas will become apparent from the description provided herein. It should be understood that this specification and specific examples are intended for illustrative purposes only and are not intended to limit the scope of this disclosure. Attached Figure Description

[0011] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of this disclosure in any way. Components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the disclosed embodiments. In the drawings:

[0012] Figure 1 It is a block diagram of a partial schematic form of an exemplary system for collecting and evaluating driving data from multiple computing systems;

[0013] Figure 2 It is a block diagram of a vehicle that includes a vehicle data system for collecting driving data and a portable computing system;

[0014] Figure 3 It is support Figure 1 A perspective view of the vehicle's cabin within the system;

[0015] Figure 4 It is a block diagram of an exemplary computing system that exchanges driving data with one or more remote systems;

[0016] Figure 5 It is used for execution Figure 1 A block diagram illustrating the functions of a computing system;

[0017] Figure 6 yes Figure 1 Block diagram of the operator assistance system sensors;

[0018] Figure 7 It is possible to be Figure 1 A block diagram of the sensor system used in the system;

[0019] Figure 8 It is possible to be Figure 1 A block diagram of the portable computing system and the sensor systems included in the system;

[0020] Figure 9A , Figure 9B , Figure 10 , Figure 11A , Figure 11B , Figure 12A , Figure 12B , Figure 12C , Figure 13A and Figure 13B It is a schematic diagram of driving events that can be represented by a driving dataset; and

[0021] Figure 14 This is a flowchart illustrating an exemplary method for developing a model from a parallel driving dataset. Detailed Implementation

[0022] The following description is illustrative in nature only and is not intended to limit this disclosure, its application, or its uses. It should be noted that the first digit of a three-digit reference numeral and the first two digits of a four-digit reference numeral correspond to the first digit and the first few digits of the image number when the reference element first appears, respectively.

[0023] The following description explains, in an illustrative and not restrictive manner, various implementation schemes of systems, vehicles, and methods for developing models from parallel driving datasets to identify the risk level of events in one of these driving datasets.

[0024] refer to Figure 1 Various embodiments of this disclosure include an analysis system 100 that processes vehicle driving data 101 received from a vehicle data system 111 integrated within a vehicle 105 and portable driving data 102 received from a portable computing system 112 (such as a smartphone) capable of being transmitted on the vehicle 105. As further described below, each of the vehicle driving data 101 and portable driving data 102 may include data representing events occurring during operation of the vehicle 105. For example, portable driving data 102 may include many different types of information that can be monitored by the portable computing system 112, ranging from data that can be received from GPS devices, gyroscopes, accelerometers, cameras, microphones to data from any other type of sensor that can be integrated into or communicate with the portable computing system 112 (including, for example, referenced below). Figure 8 The data comes from the sensors mentioned above. Therefore, portable driving data can include data reflecting events related to vehicle operation, such as acceleration, speed, braking, sharp turns, and other vehicle maneuvers. Vehicle driving data 111 can include the same data included in portable driving data 112, but may also include many other types of data. In various embodiments, vehicle driving data 111 may include camera data to indicate the scene presented to the operator, following distance data to indicate the degree of proximity of the vehicle to other vehicles, brake pedal data to indicate whether the operator has their foot on the brake in preparation for stopping, and many other forms of data.

[0025] In various implementations, the analysis system 100 is configured to extract one or more sets of vehicle driving event data 151 from vehicle driving data 101 and one or more sets of portable driving event data 152 from portable driving data 102. The sets of vehicle driving event data 151 can be identified or selected based on data values ​​exceeding various thresholds (such as instances of sudden braking, speeding, sharp turns, lane departure warnings, or object approach warnings). A risk level 155, indicating the risk presented by the event, can be assigned based on the severity of the markers associated with each set in the vehicle driving data 151.

[0026] Correlator 160 is used to associate vehicle driving data set 151 with portable driving event data set 152. In various embodiments, portable driving event data set 152 can be associated with vehicle driving event data set 151 via its corresponding timestamp. Smartphones and similar communication-enabled portable computing systems used as portable computing systems 112 periodically synchronize their clocks with a centralized system, which can also be used to synchronize the time of vehicle data system 111. Therefore, event data set 151 and event data set 152 can be easily matched based on the time when the data associated with the event was recorded. In various cases, the clocks may not be perfectly synchronized. In these cases, other elements such as speed, GPS, Bluetooth, proximity sensors, etc., can be used to match event data set 151 and event data set 152.

[0027] The output of the analysis system 100 is pattern data 170. Pattern data 170 can be used to evaluate portable driving event data 182 to assess events represented by data collected from vehicle 165, which does not include a vehicle data system (such as the vehicle data system of vehicle 105). By comparing portable driving event data 152 with a set of vehicle driving event data 151 that can be assigned a relatively high-risk level 155, aspects of the high-risk level 155 associated with the indications of portable driving event data 152 can be identified. The comparison of vehicle driving event data 151 with portable driving event data 182 allows for the identification of events that could otherwise not be identified or properly evaluated from portable driving event data 182 alone. The inclusion of specific types of data in vehicle driving event data 151 allows for proper contextualization and understanding of portable driving event data 182, which might otherwise be incomprehensible even if a large amount of portable driving event data 182 were thoroughly evaluated alone. Therefore, when an individual operates vehicle 165, the assessment system 175 using pattern data 170 may be able to assign risk level 185 to the set of portable driving event data 182 extracted from portable driving data 132 generated separately from portable computing system 122.

[0028] refer to Figure 2 Vehicle 105 (which includes a vehicle data system 111) may include an automobile, truck, sport utility vehicle (SUV), or similar vehicle for on-road and / or off-road driving. Vehicle 105 includes a body 210 that supports a cabin 220 for accommodating an operator, one or more passengers, and / or cargo. Vehicle 105 may be a self-driving or autonomous vehicle capable of operating without an operator or passengers. The body 210 of vehicle 105 may also include additional cargo sections 221, such as a trunk or truck bed.

[0029] Vehicle 105 includes a driving system 230 that cooperates with the front wheels 232 and / or the rear wheels 234 to enable vehicle 105 to start, accelerate, decelerate, stop, and steer. In several embodiments, the driving system 230 is guided by an operator control system 240 and / or an operator assistance system 260. The operator control system 240 works in conjunction with an operator display and input system 250 located within the vehicle compartment 220. The operator display and input system 250 includes all operator inputs, including steering control, accelerator and brake control, and all other operator input controls. The operator display and input system 250 also includes data devices that provide information to the operator, including a speedometer, tachometer, fuel gauge, thermometer, and other output devices. When vehicle 105 is equipped with an operator assistance system 260, the operator display and input system 250 also allows the operator to control and interact with the operator assistance system 260.

[0030] Operator assistance system 260 includes available automation, autonomous driving capabilities, or other features that assist the operator, such as a forward collision warning system, an automatic emergency braking system, a lane departure warning system, and other features described below. Therefore, operator assistance system 260 partially or completely controls the operation of vehicle 105 and / or provides the operator with warnings to help the operator avoid accidents.

[0031] In various embodiments, vehicle 105 also includes a vehicle data system 111. Vehicle data system 111 receives and tracks positioning data (such as Global Positioning System (GPS) data) to provide navigation assistance, thereby aiding the operator in navigating when the operator controls vehicle 105 using operator control system 240. Vehicle data system 111 also provides navigation data to operator assistance system 260 to allow operator assistance system 260 to control vehicle 105. Vehicle data system 111 is operable to receive and store map data and track the position of vehicle 105 relative to map data using GPS or other positioning information. Furthermore, vehicle data system 111 can record positioning information about ongoing and completed trips. Additionally, as previously referenced... Figure 1 The vehicle data system 111 captures vehicle driving data 101 that can be associated with portable driving data 102 to ultimately generate pattern data 170.

[0032] In various embodiments, the vehicle data system 111 may collect data from numerous inputs when generating vehicle driving data 101. For example, the vehicle data system 111 monitors inputs from the operator control system 240 to monitor operator occupancy of the pedals and steering wheel. The vehicle data system 111 may receive inputs from the operator assistance system 260, which is used to provide warnings and partially or fully control the operation of the vehicle. The vehicle 105 may also include additional sensors 290 from which the vehicle data system 111 collects data. As further described below, inputs from the operator control system 240, the operator assistance system 260, and the additional sensors 290 can provide data on speed, braking, steering, distance to other vehicles, operator actions, and many other types of information collected by the vehicle data system 111 in the vehicle driving data 101. It should be understood that the vehicle data system 111, the operator control system 240, the operator assistance system 260, and the sensors 290 are interoperable, for example, to enable the operator assistance system 260 to receive and use data from the operator control system 240 and the sensors 290.

[0033] It should be understood that, in order to ensure that the vehicle driving data 101 is attributed to the correct operator, the operator of the vehicle 105 can be appropriately identified. To this end, in various embodiments, the vehicle 105 also includes an operator identification system 270, which communicates with the vehicle data system 111 to identify the operator.

[0034] refer to Figure 3 In various implementation schemes, the vehicle compartment 220 of vehicle 105 ( Figure 1 and Figure 2 )Including operator display and input system 250 ( Figure 2 The operator display and input system 250 may include a display 365 and multiple controls 370-373. It should be understood that the display 365 may include a touchscreen or receive voice commands to enable the operator or passenger to interact with the operator display and input system 250. The cabin 220 may also include multiple devices for identifying the operator. The cabin 220 typically includes a windshield 310 and an operator seat 320, as well as a steering wheel 326 and other controls, such as an accelerator, brake pedal, and switches (not shown) for operating headlights, wipers, etc.

[0035] To identify the operator, the cabin 220 may include an operator identification system 270. Figure 2The operator identification system includes some or all of a plurality of identification devices. A camera or other imaging device 330 is positioned to image the operator, who can be identified using image recognition. The operator can also be identified by moving the operator's seat 320 to an adjustment position 322 preferred by the specific operator. The position can be set by selecting one of a plurality of storage buttons (not shown) that can be assigned to each of the plurality of operators. Additionally, the cabin 220 may include a key card identifier 342 that identifies not only that the key card 344 is authorized to operate the vehicle, but also when the key card 344 was assigned to a specific operator. For example, the key card 344 may include a personalized radio frequency identification (RFID) tag, and the key card 342 may include an RFID reader. Furthermore, the cabin 220 may include a telephone connection system 352 that, in addition to enabling the smartphone 354 to interact with the vehicle's entertainment system or other systems, identifies whether the smartphone 354 is associated with a specific operator of the vehicle.

[0036] In addition to in-vehicle systems, various implementation schemes can also communicate with remote computing systems. For example, it might be desirable to transmit vehicle driving data 101 or portable driving data 102 ( Figure 1 The data is transmitted to a remote computing system that supports analysis system 100 or evaluation system 175.

[0037] refer to Figure 4 The operating environment 400 of vehicles 105 and 165 may include a telecomputing system 450. In various embodiments, the telecomputing system 450 may be configured to communicate with the vehicle data system 111 of vehicle 105 and portable computing systems 112 and 122 of vehicles 105 and 165, respectively. The vehicle data system 111 and portable computing systems 112 and 122 may communicate with the telecomputing system 450 via communication links 411, 412, and 413 through network 410. Since vehicles 105 and 165 are mobile devices, communication links 411, 412, and 413 are generally wireless communication links, such as cellular, satellite, or Wi-Fi communication links. However, when one of vehicles 105 and 165 is stationary, a wired communication link, such as an Ethernet connection, may also be used. The telecomputing system 450 communicates with network 410 via a wired or wireless communication link 414. In various embodiments, the vehicle data system 111 of vehicle 105 transmits vehicle driving data 101 (…) via network 410. Figure 1 The portable driving data 102 and 132 of vehicles 105 and 165 are sent to the remote computing system 450 via network 410. Similarly, the portable computing systems 112 and 122 of vehicles 105 and 165 send portable driving data 102 and 132 to the remote computing system 450 via network 410, respectively.

[0038] The remote computing system 450 may include a server or server farm. The remote computing system 450 can access the data storage device 470 via a high-speed bus 460 to obtain programming and data for performing its functions. Information stored in the data storage device 470 may include driving data 472, which includes vehicle driving data 101 and portable driving data 102 and 132. Vehicle driving event data 151 and portable driving event data 152 and 182 may be stored in the data storage device as driving event data 474. Pattern data 170 generated from vehicle driving event data 151 and portable driving event data 152 may also be stored in the data storage device 470. Furthermore, computer-executable instructions 480 include operating system code, database management code, communication management code, and other instructions may be stored in the data storage device 470. Instruction 480 includes computer-executable instructions for receiving driving data 101, 102, and 132, identifying driving event data 151, 152, and 182, and assigning risk levels 155 and 185 to driving event data 151, 152, and 182. Furthermore, instructions for supporting correlator 160, generating pattern data 170, and supporting evaluator 180 can also be stored as instruction 480 in data storage device 470.

[0039] refer to Figure 5 And given by way of example only and not limitation, a certain form of generalized computing system 500 may be used for vehicle data system 111 of vehicle 105, vehicle 105 and 165 respectively. Figure 1 and Figure 4 Portable computing systems 112 and 122 and remote computing system 450 Figure 4 In various embodiments, computing system 500 typically includes at least one processing unit 520 and system memory 530. Depending on the exact configuration and type of the computing system, system memory 530 may be volatile memory (such as random access memory (“RAM”), non-volatile memory (such as read-only memory (“ROM”), flash memory, etc.), or some combination of volatile and non-volatile memory. System memory 530 typically stores operating system 532, one or more application programs 534, and program data 536. For example, analysis system 100 and evaluation system 175 (including correlator 160 and evaluator 180) Figure 1 This may include applications utilizing artificial intelligence, neural networks, and deep learning systems, suitable for analyzing vehicle driving data 101 and portable driving data 102 and 132, as described herein. Operating system 532 may include any number of operating systems executable on a desktop computer or portable device, including but not limited to Linux, Microsoft... Apple or Or a proprietary operating system.

[0040] The computing system 500 may also have additional features or functions. For example, the computing system 500 may also include (removable and / or non-removable) additional data storage devices, such as, for example, disks, optical discs, magnetic tapes, or flash memory. Such additional storage devices... Figure 5 The image shows removable storage device 540 and non-removable storage device 550. Computer storage media can include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules or other data. System memory 530, removable storage device 540 and non-removable storage device 550 are examples of computer storage media. Available types of computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory (in both removable and non-removable forms) or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage devices, magnetic tape cassettes, disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible by computing system 500. Any such computer storage medium can be part of computing system 500.

[0041] The computing system 500 may also include input devices 560, such as a keyboard, mouse, stylus, voice input device, touchscreen input device, etc. It may also include output devices 570, such as a display, speaker, printer, short-range transceiver (such as a Bluetooth transceiver), etc. The computing system 500 may also include one or more communication systems 580, which allow, for example, communication between the vehicle data system 111 on vehicle 105 and portable computing systems 112 and 122 (…). Figure 1 ) and remote computing system 450 ( Figure 4 When communicating, computing system 500 communicates with other computing systems 590, and vice versa. As previously mentioned, communication system 580 may include systems for wired or wireless communication. Communication media in various forms typically carry computer-readable instructions, data structures, program modules, or other data in modulated data signals (such as carrier waves or other transmission mechanisms), and include any information delivery medium. The term "modulated data signal" may include a signal whose characteristics are set or altered to encode information in a certain way. By way of illustrative example only and not limitation, communication media may include wired media (such as wired networks or direct wired connections) and wireless media (such as acoustic media, radio frequency (RF) media, infrared media, and other wireless media). As used herein, the term computer-readable medium includes both storage media and communication media.

[0042] Further reference Figure 5 The computing system 500 may include a Global Positioning System (“GPS”) circuit 585 that can automatically determine its position based on its relative position with a plurality of GPS satellites. As further described below, the GPS circuit 585 can be used to determine the position and generate data on the acceleration, speed, braking, turning and other movements of the vehicles 105 and 165.

[0043] As previously described, the vehicle data system 111 of vehicle 105 collects data from multiple inputs. These inputs may come from the operator control system 240, the operator assistance system 260, and additional sensors 290. The data provided by these devices can provide information about speed, braking, steering, distance to other vehicles, operator actions, and many other types of information collected by the vehicle data system 111 in the vehicle driving data 101. Although the various subsystems or devices described below can be individually attributed to inclusion in the operator control system 240, operator assistance system 260, or other systems, it should be understood that the embodiments disclosed in this invention are not limited to specifically grouping these devices into other devices or grouping them together with other devices.

[0044] refer to Figure 6 The operator assistance system 260 includes multiple subsystems that provide data received by the vehicle data system 111 and included in the vehicle driving data 101. In various embodiments, the operator assistance system 260 may include a forward collision warning system 602 to alert an operator traveling at normal driving speed to the presence of a stopped vehicle or other object on the road. Engagement of the forward collision warning system 602, or repeated engagement of the forward collision warning system 602, may indicate operator inattention. Similarly, the operator assistance system 260 may include an automatic emergency braking system 604. While the forward collision warning system 602 prompts the operator to apply the brakes to avoid a stopped vehicle or other object on the road, the automatic emergency braking system 604 actually automatically engages the brakes to bring the vehicle 105 ( Figure 1 It will stop automatically. Engagement of the emergency braking system 604 can also indicate operator inattention.

[0045] The operator assistance system 260 may also include an adaptive cruise control system 606. The adaptive cruise control system 606 automatically adjusts the cruise speed set by the operator or cruise control system to reflect the speed of traffic ahead. For example, if the operator sets the adaptive cruise control system 606 to a marked highway speed of 65 mph, but due to traffic conditions, the vehicle's speed on the road ahead varies between 55 mph and 65 mph, the adaptive cruise control system 606 will repeatedly adjust the cruise speed to maintain a desired distance between the vehicle and other vehicles on the road ahead.

[0046] Operator assistance system 260 may include lane departure warning system 608, which alerts the operator when the vehicle turns in the wrong direction to approach or cross lane markings and thereby presents a clear hazard. Operator assistance system 260 may include lane keeping assist system 610, which steers the vehicle to prevent it from turning in the wrong direction to approach or cross lane markings.

[0047] The operator assistance system 260 may include a blind spot detection system 612 that alerts the operator when a vehicle is traveling in the blind spot at the rear quarter of the vehicle, thus warning the operator not to change lanes in such situations. The operator assistance system 260 may include a steering wheel engagement system 614 that detects when the operator releases the steering wheel. The release of the steering wheel may be recorded as an indication of operator inattention. The operator assistance system 260 may include a pedal engagement system 616 that detects when the operator's foot contacts the accelerator or brake pedal. The time the operator engages one of the pedals may also be recorded as an indication of operator inattention. The operator assistance system 260 may also include a traffic sign recognition system 618 that recognizes, for example, stop signs or speed limit signs.

[0048] The operator assistance system 260 may also include a rear traffic crossing alert system 620 to notify the operator of the approach of other vehicles when the vehicle leaves a space. Similarly, the operator assistance system 260 may include a reversing warning system 622 that alerts the operator when the vehicle approaches an object behind it. The operator assistance system 260 may include an automatic high beam control system 624 to deactivate and reactivate the high beams when another vehicle approaches and then passes. The availability of such systems can reduce the likelihood of accidents while driving on poorly lit or unlit highways or surface streets. The operator assistance system 260 may also include an autonomous driving system 650 that provides full autonomous control of the vehicle.

[0049] refer to Figure 7In addition to the devices included in the operator assistance system 260, the vehicle data system 111 can also receive input from multiple other sensors 290, the information of which is recorded in the vehicle driving data 101. Figure 1 In ), sensor 290 may include GPS device 730 to monitor vehicle 105. Figure 1 The sensor 290 may also include an accelerometer 732 to detect rapid acceleration or deceleration, which may indicate overly aggressive driving or sudden braking due to operator inattention or dangerous traffic patterns. The sensor 290 may include a gyroscope 734 to detect sudden changes in direction indicating dangerous road conditions, abrupt lane changes, or sharp turns. The sensor 290 may include at least one following distance / lateral distance sensor 736 to determine the degree of proximity of vehicle 105 to another vehicle or the degree of proximity of vehicle 105 to other vehicles. The following distance / lateral distance sensor 736 may use any technology that can determine the following distance to another vehicle, such as radar, LiDAR, optical measurements using cameras or other optical sensors, ultrasonic measurements, laser measurements, or any other technology that can be used to determine the following distance to another vehicle.

[0050] Sensor 290 may also include device sensors (such as tire pressure sensor 738) to monitor whether the tires are inflated to the recommended level. Sensor 290 may also include miscellaneous device sensors 740 to determine whether other systems, such as lights, horns, and windshield wipers, have been used on a particular route. Sensor 290 may also include seat belt sensors 742 to indicate whether an occupant is wearing a seat belt on a particular route. Sensor 290 may also include a telephone usage sensor 744 (which may take the form of an application running on a telephone) to report whether an operator is holding or operating an operator's telephone on a particular route. Sensor 290 may include an airbag deployment sensor 746 or a collision sensor 748 to report catastrophic events that result in a collision and / or a severe collision, thereby ensuring airbag deployment. Finally, sensor 290 may include one or more cameras 750 to detect and assess conditions in and around vehicle 105. External cameras 750 may be able to monitor the vehicle's position relative to other vehicles and its position on the road, monitor driving conditions such as traffic conditions, weather conditions, and road conditions, and collect other data. The camera 750 inside the vehicle can be used to identify the operator, determine whether the occupant is wearing a seat belt, whether the operator is distracted, and collect other information.

[0051] The data collected from these devices can be received by the vehicle data system 111 and included in the vehicle driving data 101. Table 1 presents a list of data that can be included in the vehicle driving data 101. Table 1 includes the data fields that can be recorded and the frequency at which the data is sampled and / or stored.

[0052] Table 1

[0053] <![CDATA[ Fields ]]> <![CDATA[ Detailed Implementation ]]> <![CDATA[ Minimum reporting frequency ]]> Driver ID A unique identifier for each driver (if available). NA Trip ID Unique identifier for a specific trip NA The journey begins Start date and time of the trip NA End of trip End date and time of the trip NA Road speed 1Hz, using multiple sensors 1Hz GPS accuracy 1Hz GPS speed 1Hz GPS altitude 1Hz GPS heading 1Hz GPS latitude 1Hz GPS length 1Hz accelerometer 10Hz Bluetooth 1Hz gyroscope 10Hz Collision / impact sensor Real-time calculations based on available sensor and contextual data rear end Real-time calculations based on available sensor and contextual data Side impact Real-time calculations based on available sensor and contextual data airbag sensor 10Hz Vehicle rollover Real-time calculations based on available sensor and contextual data Vehicle spinning and sliding Real-time calculations based on available sensor and contextual data Vehicle security vulnerabilities When the alarm is triggered 1Hz Odometer Trip start / end NA Impact sensor event Accidental 10Hz Driver seat belt incident switch 1Hz Passenger seat belts switch 1Hz

[0054]

[0055]

[0056] The data in Table 1 (which may include some or all of the vehicle driving data 101) is used by the analysis system 100 to generate pattern data 170. Figure 1 (as further described below).

[0057] refer to Figure 8 Portable computing systems 112 and 122 may include portable sensors that generate portable driving data that can be included in portable driving data 102 and 132, respectively. Figure 1 The data in the portable computing systems 112 and 122 may include smartphones, laptops, tablets, smartwatches, or other types of portable computing systems that can be carried on vehicle 105 or vehicle 165.

[0058] In various embodiments, portable computing systems 112 and 122 may include a number of sensors to collect portable driving data 102 and 132 from vehicles 105 and 165, respectively. Figure 8 Examples of some sensors that can be used are shown. It should be understood that portable computing systems 112 and 122 may not include all of the listed sensors, or may include them. Figure 8 Additional sensors not shown.

[0059] Sensors may include one or more accelerometers 810, which can be used to sense the acceleration of portable computing systems 112 and 122 in one or more directions. In various embodiments, the accelerometers 810 can detect the stopping and starting, and left and right movement of portable computing systems 112 and 122, which may respectively reflect the corresponding movement of vehicle 105 or vehicle 165. A GPS device 812 can also be used to monitor the speed and motion of portable computing systems 112 and 122, which may respectively reflect the corresponding movement of vehicle 105 or vehicle 165. One or more gyroscopes 814 can be used to detect the attitude and orientation of the vehicles in two-dimensional or three-dimensional space. A compass 816 can also be used to determine the orientation of the vehicles. One or more magnetometers 818 can be used to detect the presence of other vehicles or perform other functions.

[0060] Portable computing systems 112 and 122 may also include a pedometer 820, which, when having circuitry capable of detecting the number of steps taken by a user, can be used to detect other movements of the portable computing systems 112 and 122, such as when an operator uses the portable computing systems 112 and 122 within the vehicle. One or more biometric sensors 822 may be used to identify or detect a specific user via fingerprint recognition, facial recognition, or other technologies. A touchscreen sensor 824 may be used to determine when an operator is using the portable computing systems 112 and 122, which may indicate distracted driving. A proximity sensor 826 may also be used to detect engagement with the portable computing systems 112 and 122. One or more cameras 828, light sensors 830, microphones 832, and / or light detection and ranging or laser imaging, detection, and ranging (LIDAR) devices 834 may also be used to monitor the environment within the vehicle to identify the operator or detect the presence of other people in the vehicle, and to monitor their activities to detect distracted driving and perform other functions.

[0061] Communication systems (such as near-field communication circuitry 836, Wi-Fi circuitry 838, cellular communication circuitry 840, Bluetooth circuitry 842, and / or beacon micro-positioning circuitry 844) can be used to determine the vehicle's position relative to global coordinates or relative to other known signal sources. Temperature sensor 846, barometer 848, and other pressure sensors 850 can be used to monitor weather conditions. Furthermore, portable computing systems 112 and 122 can communicate with other wearable devices or attached portable devices 852 to determine the operator's condition or to indicate the operator's focused or distracted movements. These devices may include smartwatches, fitness trackers, handsets (including headphones, earbuds, and similar audio devices including voice recognition systems and other processing capabilities), and other devices that can be used to monitor the operator's condition and movements.

[0062] As previously described, comparative analysis of vehicle driving data 101 from vehicle 105 and portable device driving data 102 can be used to identify patterns that can be derived from portable driving data 102, making it possible to evaluate the driving of vehicle 165 using portable driving data 132 alone.

[0063] refer to Figure 9A and Figure 9B A vehicle may narrowly avoid a collision, but the driving behaviors that lead to a close-range collision can be significantly different. Figure 9A In the example, vehicle 910 uses moderate acceleration 920 (described by a medium-sized dashed arrow) as it moves toward object 950 in road 960. Object 950 may include a pile of gravel in road 960, a person or animal that suddenly moves into road 960, or any other object. Upon seeing object 950, the operator of vehicle 910 performs sudden braking 930 and a sudden turn 940 to avoid a collision with object 950. (Source: Vehicle Data System) Figure 9A Both vehicle driving data 962 (not shown) and portable driving data 964 reflect acceleration 920, hard braking 930, and sudden turning 940. Figure 9B In the example, vehicle 911 uses high acceleration 921 (depicted by a large solid line arrow) as it moves toward object 951 in road 961. Upon seeing object 951, the operator of vehicle 910 executes very sharp braking 931 (indicated by a large arrow) and a sudden turn 941 to avoid a collision with object 951. (Source: Vehicle Data System) Figure 9B Both vehicle driving data 963 (not shown) and portable driving data 965 reflect high acceleration 921, very hard braking 931, and sudden turning 941.

[0064] In both cases, vehicle driving data 962 and 963 may be assigned a high-risk level (e.g., Figure 1 (As shown), because each situation involves sudden braking and abrupt turning. Figure 9A In the example shown, vehicle driving data 962 may include, for example, data from camera 750 ( Figure 7 The data captured indicates that an object 950 suddenly appears in the road 960, and therefore indicates safe and attentive operation of the vehicle 910. However, there may not be any identifiable pattern in the portable driving data 964 that can distinguish the operation as safe or unsafe. In subsequent examples, sudden braking 930 and sudden turning 940 after apparent moderate acceleration 920 in the portable driving data 964 may not help indicate the risk manifested in the operation.

[0065] In contrast, Figure 9BIn the illustrated example, when comparing vehicle driving data 963 with portable driving data 965, the use of high acceleration 921 may be related to the pedal engagement system 616 included in the vehicle driving data 963. Figure 6 The input corresponding to the evaluator 100 indicates that the operator of vehicle 911 engaged the brake pedal too late when applying very strong braking 931. Therefore, the evaluator 100 ( Figure 1 It can be observed that the patterns of high acceleration 921 and very hard braking 931 in portable driving data 965 may always correspond to instances where vehicle driving data 963 indicates late brake pedal engagement. Therefore, in vehicles without a vehicle data system 111 for generating vehicle driving data 962 or 963, Figure 1 In other cases, when the portable driving data 964 or 965 presents a pattern of high acceleration 921 and very hard braking 931, the portable driving data 964 or 965 may indicate high-risk driving behavior on its own.

[0066] refer to Figure 10 Another example of the operation of vehicle 1000 illustrates how pattern data can be derived from vehicle driving data 1062 and portable driving data 1064 to identify patterns in subsequently captured portable driving data without benefiting from the vehicle driving data. Vehicle 1000 uses moderate acceleration 1002 (described by arrows) as it moves toward object 1050 in road 1060. At position 1010 where vehicle 1000 begins acceleration, a steering correction 1011 is made to one side of road 1060. As vehicle 1000 moves to position 1020, another opposing steering correction 1021 is made to the other side of road 1060. As vehicle 1000 moves to position 1030, another steering correction 1031 is made to the side of road 1060 opposite to the previous steering correction 1021. Then, as the vehicle approaches object 1050, hard braking 1040 is used to avoid a collision with object 1050. Evaluator 100 ( Figure 1 The vehicle driving data 1062 and portable driving data 1064 can be compared to derive a pattern 170 that can be identified individually from the subsequently captured portable driving data.

[0067] As previously mentioned, operator actions (such as sudden turns or braking to avoid a collision) may reflect appropriate operator behavior. In contrast, correlating vehicle driving data 1062 and portable driving data 1064 can be used to identify patterns in portable driving data 1064 that should be identified as high-risk. Figure 10 For example, vehicle driving data 1062 may include data from steering wheel engagement system 614. Figure 6The input indicates that the operator occasionally or loosely engages the steering wheel, which may result in steering wheel corrections 1011, 1021, and 1031. Furthermore, a series of steering corrections 1011, 1021, and 1031, followed by sudden braking 1040, can be associated with the pedal engagement system 616 where no foot is on either pedal. Therefore, the pattern of steering corrections 1011, 1021, and 1031, followed by sudden braking 1040, can be determined by one or more accelerometers 732 in the portable computing system. Figure 7 The pattern was detected and thus captured in portable driving data 1064. Therefore, when a similar pattern is detected in portable driving data, the pattern can be identified as high-risk even without a vehicle driving dataset for comparison.

[0068] Comparative analysis of vehicle driving data 101, which reflects how the vehicle responds to traffic conditions, and portable device driving data 102 can also be used to identify patterns that can be derived from portable driving data 102, allowing portable driving data 132 to be used alone to evaluate the driving of vehicle 165. (Reference) Figure 11A and Figure 11B Vehicle 1110 operates in response to changing traffic conditions on the two-lane road 1160. Road 1160 includes edge lines 1171 and 1172 and a dashed lane divider 1173. (Reference) Figure 11A It can be assumed that vehicle 1110 travels at the indicated speed, represented by vector 1120, when traffic does not impede travel. While traveling at the indicated speed, vehicle 1110 travels at the same speed as the leading vehicle 1111, represented by vector 1122. By traveling at the same speed as the leading vehicle 1111, vehicle 1110 maintains a consistent and safe following distance behind the leading vehicle 1111, such that if the leading vehicle suddenly stops, vehicle 1110 can, for example, stop without a collision. Ideally, for the same reason, the following vehicle 1112 also travels at the same speed, represented by vector 1124, to allow a safe following distance 1182. Furthermore, it is ideal that vehicle 1110 travels at the center of its lane at equal distances 1130 and 1132 from the adjacent edge line 1171 and dividing line 1173.

[0069] refer to Figure 11B When traffic congestion builds up, the leading vehicle 1111 reduces its speed to a lower speed represented by vector 1123. Vehicle 1110 correspondingly reduces its speed to the same lower speed represented by vector 1125 to maintain a safe following distance 1181. (It should be understood that the following distance 1181 during speed reduction can be lower than...) Figure 11AThe following distance is 1180, because a shorter distance is needed to react and / or stop when traveling at lower speeds. Ideally, vehicle 1110 continues to travel in the center of its lane at equal distances 1130 and 1132 from the adjacent edge line 1171 and lane dividing line 1173. If vehicle 1110 notices a change in traffic conditions and takes action, the speed of vehicle 1110 gradually decreases without making any sudden turns within its lane, as this could lead to sudden braking or stopping. Appropriate responses to traffic can be manually controlled by the operator or can be handled automatically by operator assistance and / or automated driving facilities on vehicle 1110.

[0070] In this example of vehicle 1110, vehicle driving data 1162, appropriately adjusted according to traffic changes, records changes in the vehicle's speed relative to speeds represented by vectors 1120 and 1125. Using various vehicle sensors, it records that vehicle 1110 did not make any sudden turns and maintained distances of 1180, 1130, and 1132, respectively, behind the leading vehicle 1110 and between it and the edge of its lane. Portable driving data 1164 may not have the ability to distinguish distances 1180, 1130, and 1132, but it can still detect gradual speed changes and the absence of sudden turns within the lane in which vehicle 1110 is traveling. Therefore, a comparison of portable driving data 1164 with vehicle driving data 1162 may be able to identify appropriate, cautious driving behavior based on gradual speed changes, whether managed by an operator or by operator assistance and / or automated driving facilities on vehicle 1110.

[0071] In contrast, if the operator does not use operator assistance and / or autonomous driving facilities, or does not drive cautiously, the portable driving data 1164 (which can be verified from vehicle driving data 1162) can show indications of the lack of operator assistance and / or the operator's failure to drive with a predetermined level of caution based on monitored speed, braking, following distance, and other monitored parameters. Reference Figure 12A , such as in Figure 11A and Figure 11B As in the example, vehicle 1210 travels at a speed represented by vector 1220, the same speed as the leading vehicle 1211 traveling at, and represented by vector 1222, thus leaving a following distance 1280. Simultaneously, vehicle 1210 travels at the center of its lane 1260 at distances 1230 and 1232 equal to those from the edge line 1271 and the lane divider 1273. (See previous reference...) Figure 11A and Figure 11B The fact that vehicle 1210 maintains the same speed as the leading vehicle 1211 allows for a consistent and safe following distance between vehicle 1210 and the leading vehicle 1211.

[0072] In comparison, reference Figure 12BIf vehicle 1210 maintains a speed represented by vector 1220 when the leading vehicle 1211 accelerates to a speed represented by vector 1223, an increased following distance 1281 may occur between vehicle 1210 and the leading vehicle 1211. In response, refer to... Figure 12C The operator (not shown) can accelerate vehicle 1210 to a greater speed represented by vector 1225, but when the leading vehicle decelerates to a speed represented by vector 1224, the following distance shortens to a distance 1283, and the operator suddenly brakes vehicle 1210 to apply a high deceleration represented by vector 1226, thereby avoiding a collision with the leading vehicle 1211. Utilizing the high deceleration represented by vector 1226, the vehicle can suddenly turn to one side represented by vector component 1227, causing vehicle 1210 to move from the center of lane 1260 at distances 1230 and 1232 equal to those from the edge line 1271 and lane dividing line 1273.

[0073] Based on Figures 12A to 12C The events represented by vehicle driving data 1262 can capture data including the vehicle's changing speed represented by vectors 1220, 1225, and 1226; the changing following distances 1280, 1281, and 1283 between vehicle 1210 and the leading vehicle 1211; and data on sudden cornering of vehicle 1210 during sudden braking to avoid a collision. This is achieved through the use of various sensors (such as vehicle data system 111). Figure 1 The vehicle driving data 1262, including the camera and proximity sensor, may also capture data on changing following distances 1280, 1281, and 1283; changing distances 1230, 1231, 1232, and 1233 from the edge of lane 1260; proximity of vehicle 1210 to the leading vehicle 1211; and operator engagement with the steering wheel, accelerator, and brake pedal, among other data. The vehicle driving data 1262 may also include data collected from the camera and other sensors that may indicate whether distracted driving has occurred.

[0074] By using accelerometers, GPS circuitry, and other sensors in portable computing devices 112 and 122, portable driving data 1264 can also capture data including changes in the speed of vehicle 1210, represented by vectors 1220, 1225, and 1226, and sudden turning of vehicle 1210, represented by vector 1127, during sudden braking to avoid a collision. Portable driving data 1264 can also use cameras and other sensors to collect data on markers used by the operator's telephone or other actions that may have indicated possible distracted driving.

[0075] By correlating and analyzing vehicle driving data 1262 and portable driving data 1264, markers and / or patterns present in portable driving data 1264 can be identified that indicate the quality of driving behavior. For example, inconsistent rates of change of vehicle 1210, represented by vectors 1220, 1225, and 1226, can be correlated with vehicle driving data 1262 to indicate that operator assistance features and / or automated driving facilities are not being utilized. Inconsistent rates of change of vehicle 1210, represented by vectors 1220, 1225, and 1226, can also indicate relatively inattentive driving, particularly when a sudden stop, represented by vector 1226, ends. Sensor data captured by both vehicle driving data 1262 and portable driving data 1264 can indicate phone use or other distracted driving behaviors that lead to inconsistent rates of change of vehicle 1210, represented by vectors 1220, 1225, and 1226, ultimately resulting in a sudden stop, represented by vector 1226. As a result of such comparisons, it can be determined that portable driving data 1264 independently reflects patterns indicating high-risk levels. The ability to compare and analyze portable driving data 1264 with available vehicle driving data 1262 provides a better understanding of the driving information that can be presented in portable driving data 1264, enabling a more accurate assessment of driving behavior and events solely from portable driving data 1264 when only portable driving data 1264 is available. Therefore, when portable driving data 1264 is collected in a vehicle not equipped to collect vehicle driving data 1262, portable driving data 1264 can be used alone to assess the risk level associated with driving behavior.

[0076] For example, sudden lateral movement, as well as rapid acceleration and deceleration, can be analyzed to assess driver behavior. (Reference) Figure 13A Vehicle 1310 may be traveling behind vehicles 1311 and 1312, each of which is traveling at a speed represented by vector 1322. The operator of vehicle 1310 may decide to overtake one or more of vehicles 1311 and 1312, accelerating and turning to a speed represented by vector 1325. (Reference) Figure 13B After passing vehicle 1311, the operator of vehicle 1310 may suddenly drive behind vehicle 1312. After accelerating past vehicle 1311, vehicle 1310 may have to decelerate rapidly by sudden braking, represented by vector 1337, while driving into the space between vehicles 1311 and 1312.

[0077] Vehicle driving data 1362 can capture data including the vehicle's changing speed represented by vectors 1325 and 1337, the short following distance of vehicle 1310 behind vehicle 1312 after an overtaking maneuver, and the short margin between vehicle 1310 and vehicle 1311. As previously described, vehicle driving data 1362 may include data from vehicle data system 111 ( Figure 1 Input from a camera or other distance sensor is used to capture details of the maneuver, as well as input from the steering wheel, accelerator, and brake pedal to capture operator movements. This is achieved through the use of portable computing devices 112 and 122. Figure 1 The portable driving data 1364, which includes accelerometers, GPS circuitry, and other sensors, can also capture data on changes in the speed of vehicle 1310, represented by vectors 1325 and 1337, and sudden turns when passing vehicle 1311.

[0078] As previously referenced Figure 9A and Figure 9B As stated, in certain situations, sudden braking and turning may be appropriate, such as to avoid objects in the road ahead of the vehicle. However, by correlating and evaluating vehicle driving data 1362 and portable driving data 1364, patterns of evasive driving indicating potentially high-risk driving behavior rather than focused attention can be found in portable driving data 1364. For example, it might be necessary to travel in one direction and then turn clockwise in the opposite direction to avoid a pile of gravel or an animal appearing on the road before returning to the vehicle's original path. Figure 13A and Figure 13B In the example, this type of accident can be ruled out by reviewing camera images or other images from vehicle driving data 1362. Furthermore, the acceleration and turning of vehicle 1310 as it exits to overtake vehicle 1311, as represented by vector 1325, is inconsistent with maneuvers to avoid obstacles in the road. The acceleration and sudden turning of vehicle 1310 entering the vicinity of vehicle 1311, as represented by vector 1325, can be detected by the accelerometers, GPS, and other sensors of portable computing systems 112 and 122, just as vehicle 1310 enters the rapid deceleration and sudden turning between vehicles 1311 and 1312. By comparing and evaluating vehicle driving data 1362 and portable driving data 1364, patterns such as acceleration of vehicle 1310 before sudden turning and braking can indicate high-risk driving, while evasive maneuvers not preceding acceleration do not necessarily indicate high-risk driving. Similarly, as a result of such comparisons, it can be determined that portable driving data 1364 independently reflects patterns indicating high-risk levels, which can be collected in portable driving data 1364 events without accessing vehicle driving data 1362 provided by a vehicle equipped to provide such data.

[0079] refer to Figure 14In various implementations, an exemplary method 1400 is provided for developing a model from a parallel driving dataset to identify risk levels of events in one of the driving datasets. Method 1400 begins at box 1405. At box 1410, vehicle driving data is received. The vehicle driving data is collected by a vehicle data system operatively coupled to at least one sensor on the vehicle and configured to collect data representing driving behavior of an operator operating the vehicle during at least one trip. At box 1420, portable driving data is received. The portable driving data is collected by a portable data system capable of being delivered on the vehicle to collect data representing driving behavior of an operator operating the vehicle during at least one trip. At box 1430, the vehicle driving data and the portable driving data are evaluated. This evaluation includes assigning a risk level to at least one event included in the vehicle driving data based on data provided by at least one sensor. The evaluation also includes correlating the vehicle driving data with the portable driving data to identify patterns in the portable driving data that can be associated with risk levels. Method 1400 ends at box 1435.

[0080] It should be understood that the above detailed description is merely illustrative in nature, and variations that do not depart from the spirit and / or essence of the claimed subject matter are intended to be within the scope of the claims. Such variations should not be considered as departing from the spirit and scope of the claimed subject matter.

Claims

1. A system comprising: A vehicle data system, operatively coupled to at least one sensor on the vehicle and configured to collect vehicle driving data representing driving behavior of an operator during at least one trip; A portable data collection module, configured to cause a portable computing system capable of being transported in a vehicle to collect portable driving data representing the driving behavior of the operator when operating the vehicle during the at least one trip; and The evaluation system is configured as follows: Receive the portable driving data and the vehicle driving data; Assign a risk level to at least one event included in the vehicle driving data based on data provided by the at least one sensor; The vehicle driving data is correlated with the portable driving data to identify patterns in the portable driving data that can be associated with the at least one event; Assign risk levels to the patterns in the portable driving data; The pattern is identified in subsequently received portable driving data; as well as The risk level is assigned to the pattern identified in subsequently received portable driving data.

2. The system of claim 1, wherein the at least one sensor comprises at least one device selected from the following: a forward collision warning system, an automatic emergency braking system, an adaptive cruise control system, a lane departure warning system, a lane keeping assist system, a blind spot detection system, a steering wheel engagement system, a pedal engagement system, a traffic sign recognition system, a rear cross traffic alert system, a reversing warning system, an automatic high beam control system; an autonomous driving system, a Global Positioning System (GPS) device, an accelerometer, a gyroscope, a following / lateral distance sensor, a tire pressure sensor, a seatbelt usage sensor, a telephone usage sensor, an airbag deployment sensor, a collision sensor, a camera, and a device sensor configured to monitor the use of at least one of a vehicle light, a vehicle horn, and a windshield wiper.

3. The system of claim 1, wherein the vehicle data system includes an operator identifier configured to determine whether the operator is operating the vehicle during the at least one trip.

4. The system of claim 3, wherein the operator identifier includes at least one identifier selected from: a key card identifier, the key card identifier being configured to identify the driver based on the presence of a key card associated with the identified driver; A smart phone identifier configured to detect the presence of a smart phone associated with an identified driver on the vehicle; A seat position identifier configured to detect the position of a driver's seat previously used by the identified driver; and an imaging system configured to visually identify the identified driver.

5. The system of claim 1, wherein the portable computing system comprises a computing system selected from the following: a portable computer, a tablet computer, a smartphone, a smartwatch, and a handset.

6. The system of claim 1, wherein the portable data collection module includes an application program that can be executed on the portable computing system.

7. The system of claim 5, wherein the portable computing system comprises at least one portable sensor selected from the following: accelerometer, GPS device, gyroscope, compass, magnetometer, biometric sensor, touchscreen sensor, proximity sensor, camera, light sensor, microphone, near-field communication system, Wi-Fi communication system, cellular communication system, beacon micro-positioning system, temperature sensor, barometer, pressure sensor, wearable sensing device, and additional portable device.

8. A vehicle, said vehicle comprising: The cabin is configured to accommodate at least one entity selected from the following: operator, passenger, and cargo; A driving system configured to start, accelerate, decelerate, stop, and steer the vehicle; An operator control system configured to allow the operator to guide the operation of the vehicle; An operator assistance system configured to perform at least one function selected from the following: The vehicle can be autonomously controlled without operator assistance. as well as The operator is assisted in controlling the vehicle; and A vehicle data system, operatively coupled to at least one sensor on the vehicle and configured to collect vehicle driving data representing the operator's driving behavior while operating the vehicle during at least one trip, and to provide the vehicle driving data to an evaluation system, wherein the vehicle driving data is configured to: Based on data provided by the at least one sensor, a risk level is assigned to at least one event included in the vehicle driving data; as well as The portable driving data collected by the portable computing system on the vehicle is associated with the portable driving data to enable the identification of patterns in the portable driving data that can be associated with the at least one event and the risk level assigned to the at least one event included in the vehicle driving data. The assessment system then assigns the risk level to the pattern identified in subsequently received portable driving data.

9. The vehicle of claim 8, wherein the at least one sensor comprises at least one device selected from the following: a forward collision warning system, an automatic emergency braking system, an adaptive cruise control system, a lane departure warning system, a lane keeping assist system, a blind spot detection system, a steering wheel engagement system, a pedal engagement system, a traffic sign recognition system, a rear cross traffic alert system, a reversing warning system, an automatic high beam control system; an autonomous driving system, a GPS device, an accelerometer, a gyroscope, a follow / lateral distance sensor, a tire pressure sensor, a seatbelt usage sensor, a telephone usage sensor, an airbag deployment sensor, a collision sensor, a camera, and a device sensor configured to monitor the use of at least one of a device selected from vehicle lights, a vehicle horn, and windshield wipers.

10. The vehicle of claim 8, wherein the vehicle data system includes an operator identifier configured to determine whether the operator is operating the vehicle during the at least one trip.

11. The vehicle of claim 10, wherein the operator identifier includes at least one identifier selected from: a key card identifier, the key card identifier being configured to identify the driver based on the presence of a key card associated with the identified driver; A smart phone identifier configured to detect the presence of a smart phone associated with an identified driver on the vehicle; A seat position identifier configured to detect the position of a driver's seat previously used by the identified driver; and an imaging system configured to visually identify the identified driver.

12. A computer-implemented method, the computer-implemented method comprising: Receive vehicle driving data collected by a vehicle data system, the vehicle data system being operatively coupled to at least one sensor on the vehicle and configured to collect data representing driving behavior of an operator when operating the vehicle during at least one trip; Receive portable driving data collected by a portable data system, which is capable of being delivered on the vehicle to collect data representing the driving behavior of the operator when operating the vehicle during the at least one trip; The evaluation of the vehicle driving data and the portable driving data includes: Based on data provided by the at least one sensor, a risk level is assigned to at least one event included in the vehicle driving data; and Correlating the vehicle driving data with the portable driving data to identify patterns in the portable driving data that can be associated with the at least one event and the risk level assigned to the at least one event included in the vehicle driving data; and The risk level is then assigned to the pattern identified in subsequently received portable driving data.

13. The computer-implemented method of claim 12, wherein collecting data representing the operator's driving behavior while operating the vehicle comprises collecting data from at least one device selected from: a forward collision warning system, an automatic emergency braking system, an adaptive cruise control system, a lane departure warning system, a lane keeping assist system, a blind spot detection system, a steering wheel engagement system, a pedal engagement system, a traffic sign recognition system, a rear cross traffic alert system, a reversing warning system, an automatic high beam control system; an autonomous driving system, a Global Positioning System (GPS) device, an accelerometer, a gyroscope, a follow / lateral distance sensor, a tire pressure sensor, a seatbelt usage sensor, a telephone usage sensor, an airbag deployment sensor, a collision sensor, a camera, and a device sensor configured to monitor the use of at least one of a vehicle light, a vehicle horn, and a windshield wiper.

14. The computer-implemented method of claim 12, the method further comprising identifying the operator who is operating the vehicle during the at least one trip.

15. The computer-implemented method of claim 14, wherein identifying the operator comprises determining at least one identifier selected from: the presence of a key card associated with a driver in the vehicle; the presence of a smartphone associated with the driver in the vehicle; The position of the driver's seat previously used by the driver; And images of the driver using an imaging system configured to visually identify the driver.

16. The computer-implemented method of claim 12, wherein collecting the portable driving data using the portable data system comprises collecting the portable driving data from a computing system selected from: a portable computer, a tablet computer, a smartphone, a smartwatch, and a handset.

17. The computer-implemented method of claim 16, the method further comprising executing an application on the portable data system to collect the portable driving data.

18. The computer-implemented method of claim 16, wherein acquiring the portable driving data from the portable data system comprises acquiring data from a device selected from at least one portable sensor, the at least one portable sensor being selected from the following: accelerometer, GPS device, gyroscope, compass, magnetometer, biometric sensor, touchscreen sensor, proximity sensor, camera, light sensor, microphone, near-field communication system, Wi-Fi communication system, cellular communication system, beacon micro-positioning system, temperature sensor, barometer, pressure sensor, wearable sensing device, and additional portable device.

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