Information processing device, information processing system, program, and information processing method

Abstract

The information processing device according to the present technology comprises a dangerous driving extraction unit, a road feature acquisition unit, a calculation model generation unit, a determination model generation unit, and a driving danger evaluation unit. The dangerous driving extraction unit extracts dangerous driving behaviour on the basis of sensor data. On the basis of the sensor data, the road feature acquisition unit acquires a road feature of a road on which the vehicle was travelling at the time of the dangerous driving behaviour. On the basis of the dangerous driving behaviour and the road feature, the calculation model generation unit generates a calculation model for calculating a degree of driving danger of a driver. The determination model generation unit generates a determination model in which the relationship between accident probability and the degree of driving danger is modelled. The driving danger evaluation unit uses the calculation model and the determination model to evaluate the degree of driving danger of the driver on a specific road according to the accident probability.

Description

Information processing device, information processing system, program, and information processing method

[0001] The present technology relates to an information processing device, an information processing system, a program, and an information processing method for evaluating the risk of driving by a driver.

[0002] As an evaluation index of the driving risk of existing drivers, a system has been disclosed that extracts driving behaviors corresponding to an accident correlation model as features, calculates and displays the accident occurrence probability from driving risk tendencies, and applies this to the insurance business (see, for example, Patent Document 1).

[0003] Also disclosed is a system that generates an accident risk estimation model, estimates the accident risk of a scene based on the determined driving risk of the driver, and outputs the probability of an accident occurring as the estimation result (see, for example, Patent Document 2). This system mainly utilizes the driver's work status and biometric data to evaluate the accident risk.

[0004] International Patent Publication No. 2019 / 069732 Japanese Patent Publication No. 2022-187790

[0005] However, the above systems do not take into account road information such as speed limits, traffic volume, road surface conditions, etc. When evaluating driving behavior and the associated accident probability, it is also important to consider information about the environment in which the behavior was performed.

[0006] In view of the above circumstances, an object of the present technology is to provide an information processing device, an information processing system, a program, and an information processing method that are capable of evaluating a driving risk level by taking road information into account.

[0007] To achieve the above object, an information processing device according to one embodiment of the present technology includes a risky driving extraction unit, a road feature acquisition unit, a calculation model generation unit, a judgment model generation unit, and a driving risk assessment unit. The risky driving extraction unit extracts risky driving behaviors of a driver of a vehicle based on sensor data. The road feature acquisition unit acquires road features that are characteristics of the road on which the vehicle was traveling at the time of the risky driving behavior based on the sensor data. The calculation model generation unit generates a calculation model that calculates a driving risk level indicating the driving risk of the driver based on the risky driving behavior and the road features. The judgment model generation unit generates a judgment model that models the relationship between an accident probability and the driving risk level. The driving risk assessment unit evaluates the driving risk level of the driver on a specific road based on an accident probability using the calculation model and the judgment model.

[0008] The driving risk assessment unit may input the presence or absence or degree of risky driving behavior of the driver into the calculation model, and assess a change in driving risk due to the risky driving behavior based on an accident probability.

[0009] The driving risk assessment unit may input road characteristics of the road on which the vehicle is traveling or roads around the vehicle into the calculation model, and assess changes in driving risk due to the road characteristics based on accident probability.

[0010] The driving risk assessment unit may input the presence or degree of specific risky driving behavior by the driver and the road characteristics of the road on which the vehicle is traveling or the roads around the vehicle into the calculation model, and evaluate the change in driving risk due to the risky driving behavior and the road characteristics based on the accident probability.

[0011] The driving risk assessment unit may detect risky driving behavior of the driver based on the sensor data, and input the presence or absence or degree of the detected risky driving behavior into the calculation model to calculate the accident probability.

[0012] The information processing device may further include an information presentation unit that presents to the driver an increase in accident probability due to the detected risky driving behavior, based on the evaluation result supplied from the driving risk evaluation unit.

[0013] The information processing device may further include an information presentation unit that presents to the driver the amount of reduction in accident probability due to elimination of the detected risky driving behavior, based on the evaluation result supplied from the driving risk evaluation unit.

[0014] The driving risk assessment unit may identify a road on which the vehicle is traveling based on location information of the vehicle, and input road characteristics of the road into the calculation model to calculate the accident probability.

[0015] The information processing device may further include an information presentation unit that presents to the driver road features that contribute significantly to the accident probability based on the evaluation results supplied from the driving risk evaluation unit.

[0016] The driving risk assessment unit may identify surrounding roads that are roads around the vehicle based on the vehicle position information, and input road characteristics of the surrounding roads into the calculation model to calculate the accident probability.

[0017] The information processing device may further include an information presentation unit that presents to the driver the accident probability of each of the surrounding roads based on the evaluation results supplied from the driving risk evaluation unit.

[0018] The information processing device may further include an information presentation unit that presents to the driver, for each road, the difference in accident probability between the road on which the vehicle is scheduled to pass and other roads among the surrounding roads, based on the evaluation results supplied from the driving risk evaluation unit.

[0019] The information processing device may further include an information presentation unit that presents to the driver, for each road, an additional insurance premium for driving on the surrounding roads based on the evaluation results supplied from the driving risk evaluation unit.

[0020] The information processing device may further include an information presentation unit that presents to the driver for each road the amount of insurance premium that will be added or subtracted for driving on the surrounding roads by comparing the roads with a reference accident probability based on the evaluation results supplied from the driving risk evaluation unit.

[0021] The information processing device may further include an information presentation unit that presents to the driver, for each road, the difference between the insurance premium for driving on a road among the surrounding roads that the vehicle is scheduled to pass through and the insurance premium for driving on other roads among the surrounding roads, based on the evaluation results supplied from the driving risk evaluation unit.

[0022] The calculation model generating unit may classify the feature amounts for calculating the driving risk level into layers.

[0023] The calculation model generation unit may generate the calculation model based on the road characteristics that differ for each time period.

[0024] The sensor data may be acquired by a sensor provided in the vehicle.

[0025] To achieve the above object, an information processing system according to one embodiment of the present technology includes a vehicle, a dangerous driving extraction unit, a road feature acquisition unit, a calculation model generation unit, a judgment model generation unit, a driving risk assessment unit, and an information presentation unit. The dangerous driving extraction unit extracts risky driving behaviors of a driver of the vehicle based on sensor data. The road feature acquisition unit acquires road features that are characteristics of the road on which the vehicle was traveling when the risky driving behavior was exhibited based on the sensor data. The calculation model generation unit generates a calculation model that calculates a driving risk level indicating the driving risk of the driver based on the risky driving behavior and the road features. The judgment model generation unit generates a judgment model that models the relationship between an accident probability and the driving risk level. The driving risk assessment unit evaluates the driving risk level of the driver on a specific road based on the accident probability using the calculation model and the judgment model. The information presentation unit presents information generated based on the evaluation result by the driving risk assessment unit to the driver.

[0026] To achieve the above object, a program according to one embodiment of the present technology causes an information processing device to operate as a dangerous driving extraction unit, a road feature acquisition unit, a calculation model generation unit, a judgment model generation unit, and a driving risk assessment unit. The dangerous driving extraction unit extracts risky driving behaviors of a driver of a vehicle based on sensor data. The road feature acquisition unit acquires road features that are characteristics of the road on which the vehicle was traveling at the time of the risky driving behavior based on the sensor data. The calculation model generation unit generates a calculation model that calculates a driving risk level indicating the driving risk of the driver based on the risky driving behavior and the road features. The judgment model generation unit generates a judgment model that models the relationship between accident probability and the driving risk level. The driving risk assessment unit evaluates the driving risk level of the driver on a specific road based on the accident probability using the calculation model and the judgment model.

[0027] To achieve the above object, in an information processing method according to one embodiment of the present technology, a road feature acquisition unit extracts risky driving behavior, which is a risky driving behavior of a driver who drives a vehicle, based on sensor data. The road feature acquisition unit acquires road features, which are features of the road on which the vehicle was traveling at the time of the risky driving behavior, based on the sensor data. A calculation model generation unit generates a calculation model that calculates a driving risk level indicating the driving risk of the driver, based on the risky driving behavior and the road features. A judgment model generation unit generates a judgment model that models the relationship between the accident probability and the driving risk level. A driving risk assessment unit evaluates the driving risk level of the driver on a specific road based on the accident probability using the calculation model and the judgment model.

[0028] 14 is a schematic diagram of an information processing system according to an embodiment of the present technology. FIG. 15 is a flowchart showing the operation of an information processing device included in the information processing system. FIG. 16 is a flowchart showing the operation of an in-vehicle device included in the information processing system. FIG. 17 is an example of a display image generated by the in-vehicle device. FIG. 18 is an example of a display image generated by the in-vehicle device. FIG. 19 is an example of a display image generated by the in-vehicle device. FIG. 19 is an example of a display image generated by the in-vehicle device. FIG. 19 is an example of a display image generated by the in-vehicle device. FIG. 19 is an example of a display image generated by the in-vehicle device. FIG. 19 is a schematic diagram showing a hardware configuration of the information processing device. FIG. 19 is a block diagram showing an example configuration of a vehicle control system. FIG. 19 is a diagram showing an example of a sensing area of ​​an external recognition sensor of the vehicle control system of FIG.

[0029] [Configuration of Information Processing System] An information processing system 100 according to an embodiment of the present technology will be described. Fig. 1 is a schematic diagram of the information processing system 100 according to the embodiment. As shown in the figure, the information processing system 100 includes an in-vehicle device 110 and an information processing device 120.

[0030] The in-vehicle device 110 is a device mounted on a vehicle and includes a sensor 111, a communication unit 112, and an information presentation unit 113. Of these, the communication unit 112 and the information presentation unit 113 are functional configurations realized by cooperation between hardware and software. The vehicle is an automobile, a motorcycle, or the like, and includes commercial vehicles such as taxis and buses, self-driving cars, self-driving taxis, self-driving buses, etc. The number of in-vehicle devices 110 constituting the information processing system 100 is not particularly limited.

[0031] The sensors 111 are various sensors mounted on the vehicle. Specifically, the sensors 111 include a GNSS (Global Navigation Satellite System) sensor that detects the position of the vehicle. The sensors 111 also include one or more sensors that detect the vehicle state, such as acceleration and speed, and the vehicle environment, such as the inter-vehicle distance. Sensors that detect the vehicle state include, for example, an IMU (Inertial Measurement Unit) sensor and a CAN (Controller Area Network) sensor. Sensors that detect the vehicle environment include millimeter-wave radar, infrared sensors, cameras, etc. Hereinafter, the sensing results from the sensors 111 will be referred to as "sensor data."

[0032] The communication unit 112 communicates with the information processing device 120. Specifically, the communication unit 112 transmits the sensor data to the information processing device 120. The communication unit 112 also receives an evaluation result of the driving risk level, which will be described later, from the information processing device 120. The communication unit 112 may be any unit that is capable of communicating with the information processing device 120 via a communication line such as a mobile phone line.

[0033] The information presentation unit 113 generates presentation information to be presented to the driver based on the evaluation results. The information presentation unit 113 may present the presentation information to the driver by displaying an image on a display provided in the vehicle, such as a display for a car navigation system or a head-up display, or may present the presentation information to the driver by emitting sound from a speaker provided in the vehicle.

[0034] The in-vehicle device 110 may be fixed to the vehicle or may be portable. For example, a terminal such as a smartphone may be mounted on the vehicle and used as part or all of the sensor 111, the communication unit 112, and the information presentation unit 113.

[0035] The information processing device 120 is a server of the in-vehicle device 110, and evaluates the driving risk of a driver who drives a vehicle. The information processing device 120 includes a communication unit 121, a risky driving extraction unit 122, a road feature acquisition unit 123, a calculation model generation unit 124, a judgment model generation unit 125, a driving risk evaluation unit 126, a vehicle sensor log information DB (database) 127, and a road information DB (database) 128. These components are functional configurations realized by cooperation between hardware and software. Hereinafter, a specific driver who is the subject of driving risk evaluation will be referred to as a "target driver," and a vehicle driven by the target driver will be referred to as a "target vehicle."

[0036] The communication unit 121 communicates with the in-vehicle device 110. Specifically, the communication unit 121 receives the identifier and sensor data of the target vehicle from the communication unit 112 of the in-vehicle device 110. The communication unit 121 accumulates the received sensor data for each vehicle in the vehicle sensor log information DB 127. The communication unit 121 also transmits the evaluation results, which will be described later, to the communication unit 112.

[0037] The dangerous driving extraction unit 122 extracts the "risky driving behavior" of the target driver based on the sensor data. The risky driving behavior is a dangerous driving behavior of the target driver, such as one or more of speeding, following too closely, lane departure, sudden acceleration, sudden deceleration, ignoring traffic lights, and approaching pedestrians. The dangerous driving extraction unit 122 can also identify other dangerous driving behaviors of the target driver as risky driving behaviors. The dangerous driving extraction unit 122 supplies the extracted risky driving behavior to the calculation model generation unit 124. The dangerous driving extraction unit 122 also supplies vehicle position information at the time of the risky driving to the road feature acquisition unit 123.

[0038] The road feature acquisition unit 123 acquires road features that are characteristics of the road on which the target vehicle was traveling when the risky driving behavior occurred. Specifically, the road features include one or more of the following: road traffic volume, number of pedestrians, visibility, road shape, road width, number of lanes, number of children running out into the road, presence or absence of traffic safety instructions, presence or absence of traffic signs, presence or absence of traffic lights, speed limit, weather information (wind speed, rainfall, snowfall), and road surface condition. In addition, the road features may be any other road features that represent the characteristics of the road.

[0039] More specifically, the road feature acquisition unit 123 identifies the road on which the target vehicle is traveling based on the position information of the target vehicle when the reckless driving occurred. The road feature acquisition unit 123 refers to a road information DB (database) 128 and acquires road features of the identified road. The road feature acquisition unit 123 supplies the acquired road features to the calculation model generation unit 124. Note that the road feature acquisition unit 123 may acquire road features for each section of the road (for example, every 100 meters or every time the road environment changes). Furthermore, when road features change depending on the time of day or weather conditions, such as traffic volume, number of pedestrians, and road surface conditions, the road feature acquisition unit 123 acquires the road features at the time the reckless driving occurred.

[0040] The calculation model generation unit 124 generates a calculation model for calculating a "driving risk level" that indicates the driving risk level of a target driver based on the risky driving behavior and road characteristics. The following formula 1 shows the calculation model. Driving risk level=f(each risky driving behavior, road characteristics) (Formula 1)

[0041] The following formulas 2 to 4 are specific examples of formula 1. Driving risk level = a_01 * (whether or not the vehicle is speeding) + a_02 * (whether or not the vehicle is too close) + ... + a_11 * b_11 * (whether or not the vehicle is speeding) * (traffic volume) (Formula 2)

[0042] Driving risk level = a_01 * (whether or not the driver is speeding) + a_02 * (whether or not the vehicle is too close) + ... + a_10 * (traffic volume) + a_11 * (number of pedestrians) + ... + b_00 * (whether or not the driver is speeding) * (traffic volume) + ... + c_01 * (traffic volume) * (number of pedestrians) + ... + ... (Equation 3)

[0043] Driving risk level = a_01 * (whether or not the vehicle is speeding) + a_02 * (whether or not the vehicle is too close) + ... + a_10 * log (traffic volume) + a_11 * log (number of pedestrians) (Equation 4)

[0044] As described above, the calculation model generation unit 124 generates, as a calculation model, an equation representing a driving risk level, which includes the presence or absence of risky driving behavior and road characteristics as feature quantities. Note that the feature quantity may be only one of the presence or absence of risky driving behavior and road characteristics. Coefficients such as "a_01" and "b_00" in the calculation model are parameters estimated by processing described below. Note that the calculation model generation unit 124 may generate a calculation model based on road characteristics that vary depending on the time period, such as traffic volume, number of pedestrians, and road surface conditions. The driving risk level may be a value for a certain period of time (e.g., one year) or a value for a certain moment. The calculation model generation unit 124 supplies the generated calculation model to the driving risk level assessment unit 126.

[0045] The judgment model generation unit 125 defines the accident probability as P (driving risk) from the relationship between the driving risk and accident probability for a large number of people, and generates a judgment model that models the relationship between the driving risk and accident probability. P is some assumed probability distribution. The judgment model generation unit 125 supplies the generated judgment model to the driving risk evaluation unit 126.

[0046] The driving risk assessment unit 126 assesses the driving risk of the target driver on a specific road by the accident probability based on the calculation model and the judgment model. Specifically, the driving risk assessment unit 126 estimates parameters (such as "a_01" and "b_00" in the above equations 2 to 4) of the accident probability P(f(each risky driving behavior, road feature)) using data on the target driver's risky driving behavior and whether or not there have been any accidents. This allows the driving risk assessment unit 126 to calculate the accident probability P(f(each risky driving behavior, road feature), i.e., to quantitatively assess the driving risk of the target driver on a specific road by the accident probability P(f(each risky driving behavior, road feature).

[0047] More specifically, the driving risk assessment unit 126 inputs the presence or absence of a specific risky driving behavior of the target driver into the calculation model, and can evaluate the change in driving risk due to the risky driving behavior using the accident probability. In this case, when the driving risk assessment unit 126 detects the risky driving behavior of the target driver, such as speeding or sudden acceleration, based on the sensor data, it inputs the presence or absence of the detected risky driving behavior into the calculation model, and can evaluate the change in driving risk due to the risky driving behavior using the accident probability.

[0048] Furthermore, the driving risk assessment unit 126 identifies the "currently traveling road," which is the road on which the target vehicle is traveling, based on the position information of the target vehicle, and inputs the road features of the currently traveling road into the calculation model, thereby being able to evaluate changes in driving risk due to those road features based on the accident probability. At this time, the driving risk assessment unit 126 acquires the road features of the currently traveling road from the road information DB 128 and inputs them into the calculation model, thereby being able to evaluate changes in driving risk due to those road features based on the accident probability. The driving risk assessment unit 126 can also input road features for each section of the currently traveling road into the calculation model, thereby evaluating changes in driving risk for each section.

[0049] Furthermore, the driving risk assessment unit 126 identifies "surrounding roads," which are roads located around the target vehicle, based on the position information of the target vehicle, and inputs the road characteristics of the surrounding roads into the calculation model, thereby being able to evaluate changes in driving risk due to those road characteristics based on the accident probability.In this case, the driving risk assessment unit 126 acquires the road characteristics of the surrounding roads from the road information DB 128 and inputs them into the calculation model, and is able to evaluate changes in driving risk due to those road characteristics based on the accident probability.The driving risk assessment unit 126 can also input road characteristics for each section of the surrounding roads into the calculation model, thereby evaluating changes in driving risk for each section.

[0050] Furthermore, the driving risk assessment unit 126 can input both the presence or absence of specific risky driving behaviors of the target driver and road characteristics into the calculation model, and evaluate changes in driving risk due to risky driving behaviors and road characteristics based on accident probability. The road characteristics are road characteristics of one or both of the road currently being traveled and surrounding roads. When calculation models are generated based on road characteristics that differ for each time period, the driving risk assessment unit 126 can evaluate driving risk based on accident probability using different calculation models for each time period.

[0051] The driving risk assessment unit 126 generates an assessment result based on the calculated accident probability. The assessment result may be the accident probability itself, or an increase / decrease or difference in the accident probability. The assessment result may also include one or both of risky driving behaviors and road characteristics that have a large impact on the accident probability. The driving risk assessment unit 126 transmits the generated assessment result to the in-vehicle device 110 via the communication unit 121 and provides it to the information presentation unit 113.

[0052] The information presenting unit 113 generates presentation information based on the evaluation result supplied from the driving risk assessment unit 126 and presents the information to the target driver. Details of this will be described later.

[0053] The information processing system 100 has the above-described configuration. Note that the functional configurations of the in-vehicle device 110 and the information processing device 120 do not necessarily have to be provided in each device. For example, one or both of the dangerous driving extraction unit 122 and the road feature acquisition unit 123 may be provided in the in-vehicle device 110, and the in-vehicle device 110 may extract dangerous driving behaviors and acquire road features. Furthermore, the information presentation unit 113 may be provided in the information processing device 120, and the generated images and sounds may be transmitted to the in-vehicle device 110. In addition, each functional configuration may be realized by cooperation between the in-vehicle device 110 and the information processing device 120. Furthermore, the in-vehicle device 110 and the information processing device 120 may each be realized by multiple devices connected via an information communication network.

[0054] [Operation of Information Processing System] The operation of the information processing system 100 will be described below. Fig. 2 is a flowchart showing the operation of the information processing device 120, and Fig. 3 is a flowchart showing the operation of the in-vehicle device 110.

[0055] As shown in FIG. 2, when the communication unit 121 receives information about the target vehicle, that is, the identifier and sensor data of the target vehicle, from the in-vehicle device 110 (Step 101), the communication unit 121 updates the vehicle sensor log information DB 127 with the information (Step 102).

[0056] Next, the risky driving extraction unit 122 analyzes the driving behavior of the target driver based on the sensor data and extracts risky driving behavior (St 103). Furthermore, the road feature acquisition unit 123 acquires the road features of the road on which the target vehicle was traveling at the time from the road information DB 128 based on the position information of the target vehicle at the time the risky driving behavior occurred (St 104).

[0057] Next, the driving risk assessment unit 126 analyzes the driving risk (St105). The driving risk assessment unit 126 assesses the driving risk based on the accident probability using the calculation model generated by the calculation model generation unit 124 and the judgment model generated by the judgment model generation unit 125. Specifically, the driving risk assessment unit 126 inputs at least one of the presence or absence of risky driving behavior of the target driver, the road characteristics of the road currently being traveled, and the road characteristics of surrounding roads into the calculation model, and can calculate the accident probability. Furthermore, the driving risk assessment unit 126 transmits the assessment result based on the calculated accident probability to the information presentation unit 113 via the communication unit 121 (St106).

[0058] As shown in FIG. 3, when the communication unit 112 receives the evaluation result from the communication unit 121 (St111), the information presentation unit 113 generates presentation information based on the accident probability (St112) and presents it to the target driver (St113).

[0059] 4 to 8 are examples of the information presented by the information presentation unit 113, and show a display image G that is displayed on a display or the like disposed inside the vehicle. As shown in each figure, the display image G shows the vehicle C on which the on-board device 110 is mounted, the current road R on which the vehicle C is currently traveling, and the surrounding roads S that are roads located around the vehicle C.

[0060] As shown in FIG. 4, the information presentation unit 113 can display the accident probability of the surrounding roads S for each road. Since the accident probability for each road is included in the evaluation results supplied from the driving risk assessment unit 126 as described above, the information presentation unit 113 can obtain the accident probability of the surrounding roads S to be displayed on the display image. This enables the driver to select a road with a low accident probability to drive on. As described above, the road characteristics of the surrounding roads S change depending on the traffic volume, road surface condition, time of day, weather conditions, etc., and therefore the accident probability of the surrounding roads S also changes depending on the time of day, weather conditions, etc. The same applies to the following.

[0061] 5, the information presentation unit 113 can also display, for each road, the difference in accident probability between a road S1 that the driver plans to pass through and another road S2 among the surrounding roads S. The information presentation unit 113 can identify the road S1 that the driver plans to pass through based on a car navigation system or a specification by the driver, and can calculate the difference in accident probability between road S1 and road S2. In this case, the driver can also select a road to travel on while taking into account the accident probability.

[0062] Furthermore, as shown in FIG. 6 , the information presentation unit 113 can also display the additional insurance premium (accumulation insurance) for each road due to driving on the surrounding road S. The information presentation unit 113 can obtain the additional premium for each road using a conversion formula or conversion table between accident probability and insurance premium stored in the in-vehicle device 110. The information presentation unit 113 can also supply the accident probability for each road to another information processing device, such as an insurance company's server, and obtain the additional premium for each road. Furthermore, the driving risk assessment unit 126 in the information processing device 120 may obtain the additional premium and provide it to the information presentation unit 113 together with the assessment result. The driver can select a road to drive on while taking into account the additional insurance premium.

[0063] 7, the information presentation unit 113 can also display, for each road, the amount of insurance premium that will be added or subtracted for driving on a surrounding road S by comparing the road with a reference accident probability. Furthermore, as shown in FIG. 8, the information presentation unit 113 can also display, for each road, the difference in insurance premium between a road S1 that is to be passed through and another road S2 among the surrounding roads S. In these cases, too, the driver can select a road to drive on while taking into account the added insurance premium.

[0064] 4 to 8 show the accident probability or insurance premium for the surrounding road S, the information presentation unit 113 may also present the accident probability or insurance premium for each section of the surrounding road. The sections of the surrounding road are, for example, sections spaced at regular intervals or sections where the road environment changes.

[0065] 9 to 11 show examples of presentation information presented by the information presentation unit 113, and show a display image H displayed on a head-up display mounted on the windshield of the vehicle or on augmented reality (AR) glasses. The roads in each figure are roads that the driver can see through the windshield.

[0066] As shown in Figure 9, the information presentation unit 113 can display an image H1 indicating the increase in accident probability due to risky driving behavior. As described above, when the driving risk assessment unit 126 extracts risky driving behavior from the sensor data supplied from the vehicle, it can calculate the increase in accident probability due to the risky driving behavior. Therefore, the information presentation unit 113 can obtain the increase in accident probability due to risky driving behavior from the evaluation result supplied from the driving risk assessment unit 126. The driver can understand the increase in accident probability due to his or her own risky driving behavior from the image H1.

[0067] 10 , the information presentation unit 113 can also display an image H2 indicating the reduction in accident probability due to the elimination of risky driving behavior. When the driving risk assessment unit 126 extracts risky driving behavior from the sensor data supplied from the vehicle, it can calculate the increase in accident probability due to the risky driving behavior. When the risky driving behavior is no longer extracted, it becomes possible to include the elimination of this increase in the evaluation result. Therefore, the information presentation unit 113 can obtain the reduction in accident probability due to the elimination of risky driving behavior from the evaluation result supplied from the driving risk assessment unit 126. From the image H2, the driver can understand the reduction in accident probability due to the elimination of risky driving behavior.

[0068] Furthermore, as shown in FIG. 11 , the information presentation unit 113 can also display an image H3 indicating points to be careful about due to road characteristics while the vehicle is traveling. When calculating the accident probability for each road, the driving risk assessment unit 126 can extract one or more road features that contribute significantly to the accident probability and include these road features in the assessment results. Therefore, the information presentation unit 113 can acquire road features that contribute significantly to the accident probability from the assessment results supplied from the driving risk assessment unit 126. The road feature that contributes significantly is, for example, one or more road features arranged in descending order of contribution. From the image H3, the driver can grasp the road features that require particular attention while the vehicle is traveling.

[0069] The information processing system 100 operates as described above. The information processing system 100 may perform the above operations continuously or periodically. The information processing system 100 may also perform the above operations in accordance with the location information of the vehicle equipped with the on-vehicle device 110 and the time period during which the vehicle is traveling. For example, the information processing system 100 may perform the above operations only on roads with a high number of accidents or during time periods with a high number of accidents.

[0070] [Effects of the Information Processing System] As described above, the information processing system 100 can quantitatively evaluate the driving risk of a driver based on the accident probability. Since the calculation model for the driving risk includes components of the road characteristics of the road on which the vehicle is traveling, it is possible to evaluate the driving risk taking the road characteristics into account.

[0071] By presenting the driver with the driving risk assessment results, the information processing system 100 can help the driver recognize which of his or her driving behaviors increases the accident probability to what extent, and can encourage the driver to improve his or her driving (see FIGS. 9 and 10). Furthermore, by presenting the driver with points to be careful of due to road characteristics (see FIG. 11), it is possible to encourage caution. Furthermore, by presenting the accident probability for each road (see FIGS. 4 and 5), the driver can select a road to drive on, taking the accident probability into consideration.

[0072] Furthermore, the information processing system 100 can use the driver's accident probability to calculate insurance premiums (see Figures 6 to 9), which can be used to create new financial products. By raising the insurance premiums of drivers with a high accident probability and lowering the premiums of drivers with a low accident probability, drivers can be given an incentive to drive safely, and accidents can be prevented in advance.

[0073] Furthermore, since the information processing system 100 is capable of evaluating the driving risk taking into account road characteristics, by extracting and analyzing driving risks due to road characteristics, efficient measures can be taken in urban design, such as placing "traffic safety instructions" or installing traffic lights only on roads with high risk.

[0074] [Modification] In the above description, the calculation model generation unit 124 generates a calculation model that calculates a driving risk level based on risky driving behavior and road characteristics, and although the presence or absence of risky driving behavior is used as a feature quantity in the calculation model (see Equations 2 to 4), a continuous value indicating the degree of risky driving behavior can also be used as a feature quantity. The continuous value can be, for example, an average value of the degree of closeness of the following distance over a certain period of time, the degree of closeness of the following distance at each time, the proportion of the number of times the following distance was too close among the number of times the vehicle stopped, the amount of speed exceeding the speed limit, etc.

[0075] Furthermore, the driving risk assessment unit 126 is described as estimating the accident probability P (parameter of f(each risky driving behavior, road feature)) using data on the risky driving behavior of the target driver and the presence or absence of accidents. However, instead of the presence or absence of accidents, data on the expected number of accidents or the presence or absence of accidents by accident type may be used. Accidents by accident type include, for example, personal injury accidents, collision accidents with other vehicles, single-vehicle accidents, etc. This makes it possible for the information processing system 100 to evaluate risky driving behaviors using the accident probability of personal injury accidents.

[0076] The calculation model generation unit 124 generates a calculation model for calculating a driving risk level using risky driving behavior and road characteristics as feature quantities (see Equation 1), but it is also possible to hierarchize the feature quantities for calculating a driving risk level as shown in the following Equation 5. Risky driving behavior = g (factors related to each risky driving behavior) (Equation 5)

[0077] For example, factors related to each risky driving behavior (hereinafter referred to as "behavioral factors") include whether or not the driver is distracted while driving, when the risky driving behavior is leaving too close a following distance. If distracted driving occurs, there is a risk that the driver will leave too close a following distance, so whether or not the driver is distracted while driving corresponds to a behavioral factor for leaving too close a following distance. In addition, the calculation model generation unit 124 can hierarchically classify various behavioral factors as features for each risky driving behavior. The driving risk assessment unit 126 includes the behavioral factors in the evaluation results, and the information presentation unit 113 generates presentation information that also presents the behavioral factors to the driver. This allows the driver to recognize the behavioral factors when a risky driving behavior occurs, and can encourage the driver to eliminate the risky driving behavior by suppressing the behavioral factors.

[0078] Furthermore, in the above explanation, the driving risk assessment unit 126 assesses the driving risk based on the accident probability, but it is also possible to use the number of near miss accidents as the accident probability instead of the number of accidents that occur.

[0079] [Hardware Configuration of Information Processing Apparatus] The hardware configuration of the information processing apparatus 120 according to this embodiment will be described below with reference to Fig. 12, which is a schematic diagram showing this hardware configuration.

[0080] As shown in the figure, the information processing device 120 incorporates a CPU (Central Processing Unit) 1001 and a GPU (Graphics Processing Unit) 1002. An input / output interface 1006 is connected to the CPU 1001 and the GPU 1002 via a bus 1005. A ROM (Read Only Memory) 1003 and a RAM (Random Access Memory) 1004 are connected to the bus 1005.

[0081] The input / output interface 1006 is connected to an input unit 1007 including input devices such as a keyboard and a mouse through which a user inputs operation commands, an output unit 1008 that outputs a processing operation screen and images of processing results to a display device, a storage unit 1009 including a hard disk drive or the like that stores programs and various data, and a communication unit 1010 including a LAN (Local Area Network) adapter or the like that executes communication processing via a network typified by the Internet. Also connected is a drive 1011 that reads and writes data from a removable storage medium 1012 such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory.

[0082] The CPU 1001 executes various processes in accordance with a program stored in a ROM 1003 or a program read from a removable storage medium 1012 such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, installed in a storage unit 1009, and loaded from the storage unit 1009 into a RAM 1004. The RAM 1004 also stores data necessary for the CPU 1001 to execute various processes as appropriate. The GPU 1002 executes calculations necessary for image rendering under the control of the CPU 1001.

[0083] In the information processing device 120 configured as described above, the CPU 1001 performs the above-described series of processes by, for example, loading a program stored in the memory unit 1009 into the RAM 1004 via the input / output interface 1006 and the bus 1005 and executing the program.

[0084] The program executed by the information processing device 120 can be provided by being recorded on a removable storage medium 1012 such as a package medium, for example. The program can also be provided via a wired or wireless transmission medium such as a local area network, the Internet, or digital satellite broadcasting.

[0085] Furthermore, in the information processing device 120, the program can be installed in the storage unit 1009 via the input / output interface 1006 by inserting the removable storage medium 1012 into the drive 1011. The program can also be received by the communication unit 1010 via a wired or wireless transmission medium and installed in the storage unit 1009. Alternatively, the program can be installed in advance in the ROM 1003 or the storage unit 1009.

[0086] The program executed by the information processing device 120 may be a program that is processed chronologically in the order described in this disclosure, or may be a program that is processed in parallel or at the required timing, such as when called.

[0087] Furthermore, the entire hardware configuration of the information processing device 120 does not have to be installed in a single device, and the information processing device 120 may be configured by multiple devices. Also, the hardware configuration of the information processing device 120 may be installed in part of the hardware configuration or in multiple devices connected via a network. The hardware configuration of the in-vehicle device 110 according to this embodiment may also be similar to the above hardware configuration.

[0088] [Configuration example of vehicle control system] The following describes a vehicle control system of a vehicle equipped with the on-vehicle device 110. Fig. 13 is a block diagram showing a configuration example of the vehicle control system 11 of a vehicle equipped with the on-vehicle device 110. The vehicle control system 11 can use the presentation information output by the on-vehicle device 110 for operation assistance in a driver assistance function described later and for driving route selection in an automatic driving function.

[0089] The vehicle control system 11 is provided in the vehicle 1 and performs processing related to automated driving of the vehicle 1. This automated driving includes levels 1 to 5 of automated driving, as well as remote driving and / or remote assistance of the vehicle 1 by a remote driver. The level of automated driving may refer to the Society of Automotive Engineers (SAE) J3016™ APL2021 Levels of Driving Automation, where SAE Level 0 denotes the lowest level of automated driving and SAE Level 5 denotes the highest level of automated driving. For example, SAE Level 1 automated driving may consist of driver assistance functions that provide steering or braking / acceleration support to the driver, and SAE Level 5 automated driving may consist of automated driving functions that can drive the vehicle under all conditions.

[0090] The vehicle control system 11 includes a vehicle control ECU (Electronic Control Unit) 21, a communication unit 22, a map information storage unit 23, a location information acquisition unit 24, an external recognition sensor 25, an in-vehicle sensor 26, a vehicle sensor 27, a memory unit 28, a driving automation control unit 29, a DMS (Driver Monitoring System) 30, an HMI (Human Machine Interface) 31, and a vehicle control unit 32.

[0091] Two or more (or in some cases, all) of the vehicle control ECU 21, communication unit 22, map information storage unit 23, location information acquisition unit 24, external recognition sensor 25, in-vehicle sensor 26, vehicle sensor 27, memory unit 28, driving automation control unit 29, DMS 30, HMI 31, and vehicle control unit 32 are communicatively connected to each other via a communication network 41. The communication network 41 is configured, for example, by an in-vehicle communication network or bus conforming to a digital two-way communication standard such as a Controller Area Network (CAN), a Local Interconnect Network (LIN), a Local Area Network (LAN), FlexRay (registered trademark), or Ethernet (registered trademark). In some embodiments, the communication network 41 may include two or more types of communication networks, and different types of communication networks may be used depending on the type of data being transmitted. For example, a CAN may be used for data related to vehicle control, and an Ethernet may be used for large-volume data. In some embodiments, two or more (or in some cases, all) units of the vehicle control system 11 may be directly connected using wireless communication (e.g., communication at a relatively short distance) without using the communication network 41. In some embodiments, the wireless communication may use a short-range wireless communication technology. Non-limiting examples of short-range wireless communication technologies include near field communication (NFC) and Bluetooth (registered trademark). In some embodiments, two or more (or in some cases, all) units of the vehicle control system 11 may be connected using the communication network 41 and a wireless communication technology (e.g., a short-range wireless communication technology).

[0092] Hereinafter, in an embodiment in which two or more units of the vehicle control system 11 communicate with each other via the communication network 41, the description of the communication network 41 will be omitted. For example, in an embodiment in which the vehicle control ECU 21 and the communication unit 22 communicate with each other via the communication network 41, it will simply be described that the vehicle control ECU 21 and the communication unit 22 communicate with each other.

[0093] The vehicle control ECU 21 is configured with various processors such as a CPU (Central Processing Unit), an MPU (Micro Processing Unit), etc. The vehicle control ECU 21 controls the entire or part of the functions of the vehicle control system 11.

[0094] The communication unit 22 communicates with various devices inside the vehicle 1 (hereinafter referred to as in-vehicle devices), various devices outside the vehicle 1 (hereinafter referred to as out-vehicle devices), other vehicles, base stations, etc., and transmits and receives various types of data. In some embodiments, the communication unit 22 may communicate using multiple communication technologies.

[0095] A non-limiting example of communication between the communication unit 22 and an external device will now be briefly described. In some embodiments, the communication unit 22 may communicate with a server (hereinafter referred to as an external server) or the like on an external network via a base station or an access point using wireless communication technology. Non-limiting examples of wireless communication technology include 5G (5th Generation Mobile Communication System), LTE (Long Term Evolution), DSRC (Dedicated Short Range Communications), etc. The external network with which the communication unit 22 can communicate is, for example, the Internet, a cloud network, or a network specific to a carrier. The communication technology used by the communication unit 22 to communicate with the external network is not particularly limited as long as it is a wireless communication technology that enables digital two-way communication at a communication speed equal to or higher than a predetermined distance.

[0096] In some embodiments, the communication unit 22 may use P2P (Peer to Peer) technology to communicate with a terminal located near the vehicle. The terminal located near the vehicle may be, for example, a terminal attached to a mobile object moving at a relatively slow speed, such as a pedestrian or a bicycle, a terminal installed at a fixed location in a store, and / or an MTC (Machine Type Communication) terminal. In some embodiments, the communication unit 22 may perform V2X (Vehicle to Everything) communication. V2X communication generally refers to communication between the vehicle and another entity. Non-limiting examples of V2X communication include vehicle-to-vehicle communication with another vehicle, vehicle-to-infrastructure communication with a roadside unit, vehicle-to-home communication, and vehicle-to-pedestrian communication with a terminal carried or worn by a pedestrian.

[0097] In some embodiments, the communication unit 22 may receive a program for updating software that controls the operation of the vehicle control system 11 from outside the vehicle 1 (e.g., over the air). In some embodiments, the communication unit 22 may receive map information, traffic information, information about the surroundings of the vehicle 1, etc. from outside the vehicle 1. In some embodiments, the communication unit 22 may transmit information about the vehicle 1 or information about the surroundings of the vehicle 1, etc. to an external device or an external network. Non-limiting examples of information about the vehicle 1 that the communication unit 22 transmits to an external device or an external network include data indicating the status of the vehicle 1, recognition results by the recognition unit 73, etc. In some embodiments, the communication unit 22 may communicate with a vehicle emergency notification system. Non-limiting examples of a vehicle emergency notification system include eCall, etc.

[0098] In some embodiments, the communication unit 22 may receive electromagnetic waves transmitted by a road traffic information communication system. In some embodiments, the electromagnetic waves may be transmitted using a radio beacon, an optical beacon, FM multiplex broadcasting, or the like.

[0099] Non-limiting examples of communication with in-vehicle devices that can be performed by the communication unit 22 will now be briefly described. In some embodiments, the communication unit 22 may communicate with the in-vehicle devices using wireless communication. For example, in some embodiments, the communication unit 22 may communicate with the in-vehicle devices using wireless communication technology that enables digital bidirectional communication at a predetermined communication speed or higher. Non-limiting examples of wireless communication technology include wireless LAN, Bluetooth, NFC, and WUSB (Wireless USB). Alternatively, the communication unit 22 may communicate with the in-vehicle devices using wired communication (in addition to or as an alternative to wireless communication). For example, in some embodiments, the communication unit 22 may communicate with the in-vehicle devices using wired communication via a cable connected to a connection terminal (not shown). In some embodiments, the communication unit 22 may communicate with the in-vehicle devices using wired communication technology that enables digital bidirectional communication at a predetermined communication speed or higher. Non-limiting examples of wired communication technologies include Universal Serial Bus (USB), High-Definition Multimedia Interface (HDMI)®, and Mobile High-definition Link (MHL).

[0100] Here, the in-vehicle devices refer to, for example, devices inside the vehicle 1 that are not connected to the communication network 41. The in-vehicle devices are divided into devices that constitute the vehicle control system 11 and devices that do not constitute the vehicle control system 11. Non-limiting examples of in-vehicle devices that do not constitute the vehicle control system 11 include mobile devices and wearable devices carried by users of the vehicle 1 (for example, the driver or passengers), and information devices that are temporarily installed inside the vehicle 1. These devices can, for example, be moved outside the vehicle 1 and become external devices.

[0101] The map information storage unit 23 stores maps acquired from an external device or an external network and / or maps created by the vehicle 1. For example, the map information storage unit 23 may store a three-dimensional high-precision map, a global map that has lower precision than a high-precision map and covers a wide area, or the like.

[0102] The high-precision map may be, for example, a dynamic map, a point cloud map, a vector map, etc. The dynamic map may be, for example, a map consisting of four layers of dynamic information, quasi-dynamic information, quasi-static information, and static information, and may be provided to the vehicle 1 from an external server or the like. The point cloud map may be a map composed of a point cloud (point cloud data). The vector map may be, for example, a map adapted for automated driving by associating traffic information such as the positions of lanes and traffic lights with the point cloud map.

[0103] The point cloud map and vector map may be provided, for example, from an external server or the like, or may be created in the vehicle 1 based on sensing results from the camera 51, radar 52, LiDAR 53, etc. as a map for matching with a local map (described later) and stored in the map information storage unit 23. Furthermore, when a high-precision map is provided from an external server or the like, map data of, for example, an area of ​​several hundred square meters related to the planned route along which the vehicle 1 will travel may be acquired from the external server or the like in order to reduce communication capacity.

[0104] The position information acquisition unit 24 acquires position information of the vehicle 1. The acquired position information may be supplied to the driving automation control unit 29. In some embodiments, the position information acquisition unit 24 may receive GNSS (Global Navigation Satellite System) signals from GNSS satellites. In some embodiments, the position information acquisition unit 24 may receive signals from beacons or the like.

[0105] The external recognition sensor 5 includes various sensors used to recognize the situation outside the vehicle 1, and supplies sensor data from one or more (or in some cases, all) sensors to one or more (or in some cases, all) units of the vehicle control system 11. The type and number of sensors included in the external recognition sensor 25 are arbitrary.

[0106] In some embodiments, the external recognition sensor 25 may include a camera 51, a radar 52, a LiDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging) 53, and an ultrasonic sensor 54. Without being limited to this, the external recognition sensor 25 may be configured to include one or more types of sensors selected from the camera 51, the radar 52, the LiDAR 53, and the ultrasonic sensor 54. The number of cameras 51, radar 52, LiDAR 53, and ultrasonic sensors 54 is not particularly limited as long as the number is a number that can be realistically installed on the vehicle 1. Furthermore, the types of sensors included in the external recognition sensor 25 are not limited to this example, and the external recognition sensor 25 may include other types of sensors. Examples of sensing areas of each sensor included in the external recognition sensor 25 will be described later.

[0107] The camera 51 may use any suitable imaging method. In some embodiments, the camera 51 may use an imaging method capable of distance measurement. Non-limiting examples of cameras using imaging methods capable of distance measurement include a time-of-flight (ToF) camera, a stereo camera, a monocular camera, and an infrared camera. However, the camera 51 may simply acquire an image without distance measurement.

[0108] In some embodiments, the external recognition sensor 25 may include an environmental sensor for detecting characteristics of the environment around the vehicle 1. Non-limiting examples of environmental characteristics that may be detected include weather, climate, brightness, etc. In some embodiments, the environmental sensor may include various sensors such as a rain sensor, a fog sensor, a sunlight sensor, a snow sensor, and an illuminance sensor.

[0109] In some embodiments, the external recognition sensor 25 may include a microphone used to detect sounds around the vehicle 1 and the location of sound sources.

[0110] The interior sensor 26 includes various sensors for detecting information about the interior of the vehicle 1, and supplies sensor data from one or more (or in some cases, all) sensors to one or more (or in some cases, all) units of the vehicle control system 11. The types and number of the various sensors included in the interior sensor 26 are not particularly limited as long as they are of the types and number that can be realistically installed in the vehicle 1.

[0111] In some embodiments, the interior sensor 26 may include one or more sensors selected from the group consisting of a camera, radar, a seating sensor, a microphone, and a biometric sensor. In some embodiments, the camera included in the interior sensor 26 may use an imaging method capable of measuring distances. Non-limiting examples of cameras using imaging methods capable of measuring distances include a Time of Flight (ToF) camera, a stereo camera, a monocular camera, and an infrared camera. The camera included in the interior sensor 26 may also be used simply to acquire captured images, regardless of distance measurement. The biometric sensor included in the interior sensor 26 may be provided, for example, on a seat or a steering wheel, and may detect various types of biometric information of the user.

[0112] The vehicle sensor 27 includes various sensors for detecting the state of the vehicle 1, and supplies sensor data from one or more (or in some cases, all) sensors to one or more (or in some cases, all) units of the vehicle control system 11. The types and number of the various sensors included in the vehicle sensor 27 are not particularly limited as long as they are of the types and number that can be realistically installed on the vehicle 1.

[0113] In some embodiments, the vehicle sensor 27 may include a speed sensor, an acceleration sensor, an angular velocity sensor (gyro sensor), and / or an inertial measurement unit (IMU) that integrates these. In some embodiments, the vehicle sensor 27 may include a steering angle sensor that detects the steering angle of the steering wheel, a yaw rate sensor, an accelerator sensor that detects the amount of accelerator pedal operation (e.g., pedal force, pedal stroke), and / or a brake sensor that detects the amount of brake pedal operation (e.g., pedal force, pedal stroke). In some embodiments, the vehicle sensor 27 may include a rotation sensor that detects the number of rotations of the engine or motor, an air pressure sensor that detects tire air pressure, a slip ratio sensor that detects tire slip ratio, and / or a wheel speed sensor that detects the rotation speed of the wheels. In some embodiments, the vehicle sensor 27 may include a battery sensor that detects the remaining battery level and temperature, and / or an impact sensor that can detect external impacts.

[0114] The storage unit 28 includes at least one of a non-volatile storage medium and a volatile storage medium, and stores data and programs. Non-limiting examples of storage media include magnetic storage devices such as electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), and / or hard disk drives (HDDs), semiconductor storage devices, optical storage devices, and magneto-optical storage devices. The storage unit 28 stores various programs and data used by one or more (or in some cases, all) units of the vehicle control system 11. In some embodiments, the storage unit 28 may include an event data recorder (EDR) or a data storage system for automated driving (DSSAD), and may store information about the vehicle 1 before and after an event such as an accident, as well as information acquired by the in-vehicle sensors 26.

[0115] The driving automation control unit 29 controls the driving automation function of the vehicle 1. In some embodiments, the driving automation control unit 29 may include an analysis unit 61, an action planning unit 62, and an operation control unit 63.

[0116] The analysis unit 61 performs an analysis process of the vehicle 1 and / or the surrounding situation. The analysis unit 61 includes a self-position estimation unit 71, a sensor fusion unit 72, and a recognition unit 73.

[0117] In some embodiments, the self-position estimation unit 71 may estimate the self-position of the vehicle 1 based on sensor data from the external recognition sensor 25 and a high-precision map stored in the map information storage unit 23. For example, the self-position estimation unit 71 may generate a local map based on the sensor data from the external recognition sensor 25 and estimate the self-position of the vehicle 1 by matching the local map with the high-precision map. The position of the vehicle 1 may be based on, for example, the center of the rear wheel pair axle.

[0118] In some embodiments, the local map may be a three-dimensional high-precision map, an occupancy grid map, or the like created using a technique such as SLAM (Simultaneous Localization and Mapping). The three-dimensional high-precision map may be, for example, the point cloud map described above. The occupancy grid map may be a map obtained by dividing a three-dimensional or two-dimensional space around the vehicle 1 into grids of a predetermined size and indicating the occupancy status of objects on a grid-by-grid basis. The occupancy status of an object may be indicated, for example, by the presence or absence of an object or a probability of its presence. In some embodiments, the local map may also be used, for example, in the detection process and / or recognition process of the situation outside the vehicle 1 by the recognition unit 73.

[0119] In some embodiments, the self-position estimation unit 71 may estimate the self-position of the vehicle 1 based on the position information acquired by the position information acquisition unit 24 and / or sensor data from the vehicle sensor 27 .

[0120] The sensor fusion unit 72 performs sensor fusion processing to obtain information by combining multiple different types of sensor data (for example, image data supplied from the camera 51 and sensor data supplied from the radar 52). Methods for combining different types of sensor data include, but are not limited to, compounding, integration, fusion, and association.

[0121] The recognition unit 73 executes a detection process for detecting the situation outside the vehicle 1 and / or a recognition process for recognizing the situation outside the vehicle 1 .

[0122] For example, the recognition unit 73 may perform detection processing and / or recognition processing of the situation outside the vehicle 1 based on information from the external recognition sensor 25, information from the self-position estimation unit 71, information from the sensor fusion unit 72, etc.

[0123] Specifically, for example, the recognition unit 73 may perform a detection process and / or a recognition process of objects around the vehicle 1. The object detection process may be, for example, a process of detecting the presence or absence, size, shape, position, movement, etc. of an object. The object recognition process may be, for example, a process of recognizing attributes such as the type of object, or a process of identifying a specific object. The detection process and the recognition process are not necessarily clearly separated, and there may be at least a partial overlap.

[0124] In some embodiments, the recognition unit 73 may detect objects around the vehicle 1 by performing clustering to classify a point cloud based on sensor data from the radar 52 and / or the LiDAR 53, etc. into clusters of points. This makes it possible to detect the presence, size, shape, and position of objects around the vehicle 1.

[0125] In some embodiments, the recognition unit 73 may detect the movement of objects around the vehicle 1 by tracking the movement of clusters of point clouds classified by clustering. This makes it possible to detect the speed and / or traveling direction (movement vector) of objects around the vehicle 1.

[0126] In some embodiments, the recognition unit 73 may detect and / or recognize vehicles (including bicycles), people, obstacles, structures, roads, traffic lights, traffic signs, road markings, etc. based on image data supplied from the camera 51. In some embodiments, the recognition unit 73 may recognize the type of object around the vehicle 1 by performing recognition processing such as semantic segmentation.

[0127] In some embodiments, the recognition unit 73 may perform a recognition process of traffic rules around the vehicle 1 based on the map stored in the map information storage unit 23, the result of estimation of the self-position by the self-position estimation unit 71, and / or the result of recognition of objects around the vehicle 1 by the recognition unit 73. Through this process, the recognition unit 73 may recognize the position and / or state of traffic lights, the contents of traffic signs and / or road markings, the contents of traffic regulations, and / or lanes that can be traveled, etc.

[0128] In some embodiments, the recognition unit 73 may perform recognition processing of the environment around the vehicle 1. In some embodiments, the recognition unit 73 may recognize weather characteristics (temperature, humidity, brightness) and / or road surface conditions, etc.

[0129] The behavior planning unit 62 creates a behavior plan for the vehicle 1. For example, the behavior planning unit 62 may create a behavior plan by performing route planning and route tracking.

[0130] In some embodiments, path planning may include global path planning and local path planning. Global path planning may include a process of planning a rough route from a start to a goal. Local path planning, also referred to as trajectory planning, may include generating a trajectory that allows the vehicle 1 to proceed safely and smoothly along a planned route in the vicinity of the vehicle 1, taking into account the motion characteristics of the vehicle 1, the presence of any obstacles, and the like.

[0131] In some embodiments, the path following may be a planning of an operation for safely and accurately traveling along a route planned by the route planner within a planned time. The behavior planning unit 62 may, for example, calculate a target speed and / or a target angular velocity of the vehicle 1 based on the result of the path following process.

[0132] The operation control unit 63 controls the operation of the vehicle 1 in order to realize the action plan created by the action planning unit 62 .

[0133] For example, in some embodiments, the operation control unit 63 may control the steering control unit 81, the brake control unit 82, and / or the drive control unit 83 included in the vehicle control unit 32 (described later) to perform lateral vehicle motion control and / or longitudinal vehicle motion control so that the vehicle 1 travels along the trajectory calculated by the trajectory plan. For example, the operation control unit 63 may perform control (e.g., lateral vehicle motion control, longitudinal vehicle motion control) for one or more driver assistance functions and / or driving automation. Non-limiting examples of driver assistance functions include collision avoidance or impact mitigation, following distance control (e.g., control to maintain a specific distance from a vehicle traveling in front of the vehicle 1), vehicle speed control (e.g., control to maintain a specific speed), vehicle collision warning, and lane departure warning. Non-limiting examples of driving automation include driving without operation by a driver or a remote driver.

[0134] In some embodiments, the DMS 30 may perform a driver authentication process and / or a driver state recognition process based on sensor data from the in-vehicle sensors 26 and / or input data input to the HMI 31 (described later), etc. Non-limiting examples of the driver state that may be recognized include physical condition, alertness, concentration, fatigue, gaze direction, level of intoxication, driving operation, posture, etc.

[0135] In some embodiments, the DMS 30 may perform authentication processing of a user other than the driver (e.g., a passenger) and / or recognition processing of the state of the user. In some embodiments, the DMS 30 may perform recognition processing of the interior situation of the vehicle 1 based on sensor data from the interior sensors 26. Non-limiting examples of characteristics of the interior situation of the vehicle 1 that can be recognized include temperature, humidity, brightness, odor, etc.

[0136] The HMI 31 receives various data, instructions, etc. as input, and presents the various data to the user.

[0137] The input of data to the HMI 31 will be briefly described. The HMI 31 includes an input device through which a person inputs data, instructions, etc. The HMI 31 generates an input signal based on the data, instructions, etc. input via the input device and supplies the signal to one or more (or in some cases, all) units of the vehicle control system 11. In some embodiments, the HMI 31 may include a touch panel, buttons, switches, and / or levers as input devices. Without being limited thereto, the HMI 31 may also include an input device that allows information to be input by a method other than manual operation, such as voice or gestures. In some embodiments, the HMI 31 may include an input device such as a remote control device using infrared and / or radio waves, or an externally connected device that can operate the vehicle control system 11. Non-limiting examples of externally connected devices include a mobile device (e.g., a smartphone) and a wearable device (e.g., a smart watch).

[0138] The presentation of data by the HMI 31 will be briefly described. The HMI 31 generates visual information, auditory information, and / or tactile information for the user and / or a person outside the vehicle 1. The HMI 31 may also perform output control, controlling the output, output content, output timing, and / or output method of each piece of generated information. Non-limiting examples of visual information that can be generated and output by the HMI 31 include information displayed by images or lights, such as an operation screen, a status display of the vehicle 1, a warning display, and a monitor image showing the situation around the vehicle 1. Non-limiting examples of auditory information that can be generated and output by the HMI 31 include voice guidance, warning sounds, warning messages, etc. Non-limiting examples of tactile information that can be generated and output by the HMI 31 include information imparted to the user's sense of touch by force, vibration, movement, etc.

[0139] In some embodiments, the HMI 31 may include, as an output device capable of outputting visual information, a display device that presents visual information by displaying an image itself or a projector device that presents visual information by projecting an image. In some embodiments, the display device may be, in addition to or instead of a typical display device, a device that displays visual information within the user's field of view, such as a head-up display, a see-through display, or a wearable device with an augmented reality (AR) function. In some embodiments, the HMI 31 may include, as an output device capable of outputting visual information, a display device included in a navigation device, an instrument panel, a camera monitoring system (CMS), an electronic mirror, a lamp, or the like provided in the vehicle 1.

[0140] In some embodiments, the HMI 31 may include an audio speaker, headphones, or earphones as output devices capable of outputting auditory information.

[0141] In some embodiments, the HMI 31 may include a haptic element using haptic technology as an output device capable of outputting tactile information. The haptic element may be provided on a part of the vehicle 1 that the user comes into contact with, such as the steering wheel or the seat.

[0142] The vehicle control unit 32 controls one or more (or in some cases, all) units of the vehicle 1. The vehicle control unit 32 includes a steering control unit 81, a brake control unit 82, a drive control unit 83, a body system control unit 84, a light control unit 85, and a horn control unit 86.

[0143] The steering control unit 81 detects and / or controls the state of the steering system of the vehicle 1. The steering system includes, for example, a steering mechanism including a steering wheel, an electric power steering, etc. The steering control unit 81 includes, for example, a steering ECU that controls the steering system, an actuator that drives the steering system, etc.

[0144] The brake control unit 82 detects and / or controls the state of the brake system of the vehicle 1. The brake system includes, for example, a brake mechanism including a brake pedal, an antilock brake system (ABS), a regenerative brake mechanism, etc. The brake control unit 82 includes, for example, a brake ECU that controls the brake system, an actuator that drives the brake system, etc.

[0145] The drive control unit 83 detects and / or controls the state of the drive system of the vehicle 1. The drive system includes, for example, an accelerator pedal, a drive force generating device for generating drive force such as an internal combustion engine or a drive motor, and a drive force transmission mechanism for transmitting the drive force to the wheels. The drive control unit 83 includes, for example, a drive ECU for controlling the drive system, and an actuator for driving the drive system.

[0146] The body system control unit 84 detects and / or controls the states of the body system systems of the vehicle 1. The body system systems include, for example, a keyless entry system, a smart key system, a power window device, a power seat, an air conditioning system, an airbag, a seat belt, a shift lever, etc. The body system control unit 84 includes, for example, a body system ECU that controls the body system systems, an actuator that drives the body system systems, etc.

[0147] The light control unit 85 detects and / or controls the states of various lights of the vehicle 1. Non-limiting examples of lights that can be controlled by the light control unit 85 include headlights, backlights, fog lights, turn signals, brake lights, projector lights, and bumper indicators. The light control unit 85 includes a light ECU that controls the lights, an actuator that drives the lights, and the like.

[0148] The horn control unit 86 detects and / or controls the state of the car horn of the vehicle 1. The horn control unit 86 includes, for example, a horn ECU that controls the car horn, an actuator that drives the car horn, and the like.

[0149] Fig. 14 is a diagram showing an example of a sensing area by the camera 51, the radar 52, the LiDAR 53, and the ultrasonic sensor 54 of the external recognition sensor 25 in Fig. 13. Fig. 14 schematically shows the vehicle 1 as viewed from above.

[0150] Sensing area 101F and sensing area 101B show examples of sensing areas of the ultrasonic sensors 54. Sensing area 101F (e.g., sensing area of ​​the multiple ultrasonic sensors 54) covers the periphery of the front end of the vehicle 1. Sensing area 101B (e.g., sensing area of ​​the multiple ultrasonic sensors 54) covers the periphery of the rear end of the vehicle 1.

[0151] The sensing results in sensing area 101F and / or sensing area 101B may be used, for example, for parking assistance for vehicle 1.

[0152] Sensing area 102F, sensing area 102B, sensing area 102L, and sensing area 102R show examples of sensing areas of a short-range or medium-range radar 52. Sensing area 102F covers a position farther in front of the vehicle 1 than sensing area 101F. Sensing area 102B covers a position farther behind the vehicle 1 than sensing area 101B. Sensing area 102L covers the surrounding area behind the left side of the vehicle 1. Sensing area 102R covers the surrounding area behind the right side of the vehicle 1.

[0153] The sensing results in the sensing area 102F may be used, for example, to detect vehicles, pedestrians, etc. present in front of the vehicle 1. The sensing results in the sensing area 102B may be used, for example, for a collision prevention function behind the vehicle 1. The sensing results in the sensing area 102L and / or the sensing area 102R may be used, for example, to detect one or more objects in blind spots on the left and / or right sides of the vehicle 1.

[0154] Sensing area 103F, sensing area 103B, sensing area 103L, and sensing area 103R show examples of sensing areas sensed by camera 51. Sensing area 103F covers a position farther in front of vehicle 1 than sensing area 102F. Sensing area 103B covers a position farther behind vehicle 1 than sensing area 102B. Sensing area 103L covers the periphery on the left side of vehicle 1. Sensing area 103R covers the periphery on the right side of vehicle 1.

[0155] The sensing results in sensing area 103F may be used, for example, for recognizing traffic lights and traffic signs, a lane departure prevention assistance system, or an automatic headlight control system. The sensing results in sensing area 103B may be used, for example, for parking assistance and / or a surround view system. The sensing results in sensing area 103L and / or sensing area 103R may be used, for example, for a surround view system.

[0156] Sensing area 104 shows an example of the sensing area of ​​LiDAR 53. Sensing area 104 covers a position farther ahead of vehicle 1 than sensing area 103F. On the other hand, sensing area 104 has a narrower range in the left-right direction of vehicle 1 than sensing area 103F.

[0157] The sensing results in the sensing area 104 may be used to detect objects such as surrounding vehicles, for example.

[0158] Sensing area 105 shows an example of the sensing area of ​​the long-distance radar 52. Sensing area 105 covers a position further ahead of the vehicle 1 than sensing area 104. On the other hand, sensing area 105 has a narrower range in the left-right direction of the vehicle 1 than sensing area 104.

[0159] The sensing results in the sensing area 105 may be used for, for example, adaptive cruise control (ACC), emergency braking, collision avoidance, and the like.

[0160] In some embodiments, the sensing area of ​​each of the external recognition sensors 25 (e.g., the camera 51, the radar 52, the LiDAR 53, and the ultrasonic sensor 54) may have various configurations other than the configuration shown in Fig. 14. Specifically, in some embodiments, the ultrasonic sensor 54 may also sense the sides of the vehicle 1, and the LiDAR 53 may sense the rear of the vehicle 1. Furthermore, the installation position of each sensor is not limited to the above-mentioned examples. Furthermore, the number of each sensor may be one or more.

[0161] As described above, the vehicle control system 11 can use the presented information output by the in-vehicle device 110 to assist in operation in the driver assistance function or to select a driving route in the automatic driving function. For example, the vehicle control system 11 can select a road with a lower accident probability and assist the driver's operation or cause the vehicle 1 to drive automatically, thereby causing the vehicle 1 to drive on the selected road. The vehicle control system 11 can also select a road with a lower insurance premium and similarly cause the vehicle 1 to drive on the selected road.

[0162] Specifically, the behavior planning unit 62 can take into account the presented information output by the in-vehicle device 110 when creating a behavior plan for the vehicle 1. Furthermore, the operation control unit 63 controls the vehicle control unit 32 to assist the driver's operation or to enable automatic driving so as to realize the behavior plan created by the behavior planning unit 62.

[0163] [About the present disclosure] The effects described in this disclosure are merely examples and are not limiting, and other effects may also be present. The description of multiple effects does not necessarily mean that these effects are exhibited simultaneously. It means that at least one of the effects described above can be obtained depending on the conditions, etc., and effects not described in this disclosure may also be exhibited. Furthermore, it is possible to combine at least two of the characteristic features described in this disclosure.

[0164] The present technology can also be configured as follows.

[0165] (1) An information processing device comprising: a dangerous driving extraction unit that extracts risky driving behavior that is a risky driving behavior of a driver who drives a vehicle based on sensor data, a road feature acquisition unit that acquires road features that are characteristics of the road on which the vehicle was traveling when the risky driving behavior was exhibited based on the sensor data, a calculation model generation unit that generates a calculation model to calculate a driving risk level that indicates the driving risk of the driver based on the risky driving behavior and the road features, a judgment model generation unit that generates a judgment model that models the relationship between an accident probability and the driving risk level, and a driving risk assessment unit that evaluates the driving risk level of the driver on a specific road based on the accident probability using the calculation model and the judgment model. (2) The information processing device described in (1), wherein the driving risk assessment unit inputs the presence or absence or degree of risky driving behavior of the driver to the calculation model, and evaluates a change in the driving risk level due to the risky driving behavior based on the accident probability. (3) The information processing device described in (1) above, wherein the driving risk assessment unit inputs road features of a road on which the vehicle is traveling or roads around the vehicle into the calculation model, and evaluates a change in driving risk due to the road features based on an accident probability. (4) The information processing device described in (1) above, wherein the driving risk assessment unit inputs the presence or absence or degree of a specific risky driving behavior of the driver and road features of a road on which the vehicle is traveling or roads around the vehicle into the calculation model, and evaluates a change in driving risk due to the risky driving behavior and the road features based on an accident probability. (5) The information processing device described in (2) above, wherein the driving risk assessment unit detects risky driving behavior of the driver based on the sensor data, and calculates the accident probability by inputting the presence or absence or degree of the detected risky driving behavior into the calculation model. (6) The information processing device according to (5) above, further comprising an information presentation unit that presents to the driver an increase in accident probability due to the detected risky driving behavior based on the evaluation result supplied from the driving risk evaluation unit.(7) The information processing device according to (6), further comprising an information presentation unit that presents to the driver a reduction in accident probability due to elimination of the detected risky driving behavior, based on the evaluation result supplied from the driving risk assessment unit. (8) The information processing device according to (3), wherein the driving risk assessment unit identifies a road on which the vehicle is traveling, based on position information of the vehicle, and inputs road features of the road into the calculation model to calculate the accident probability. (9) The information processing device according to (8), further comprising an information presentation unit that presents to the driver road features that contribute significantly to the accident probability, based on the evaluation result supplied from the driving risk assessment unit. (10) The information processing device according to (3), wherein the driving risk assessment unit identifies surrounding roads that are roads around the vehicle, based on position information of the vehicle, and inputs road features of the surrounding roads into the calculation model to calculate the accident probability. (11) The information processing device according to (10) above, further comprising an information presentation unit that presents to the driver the accident probability of the surrounding roads for each road based on the evaluation results supplied from the driving risk assessment unit. (12) The information processing device according to (10) above, further comprising an information presentation unit that presents to the driver the difference in accident probability between a road among the surrounding roads through which the vehicle is scheduled to pass and other roads for each road based on the evaluation results supplied from the driving risk assessment unit. (13) The information processing device according to (10) above, further comprising an information presentation unit that presents to the driver the additional amount of insurance premium for driving on the surrounding roads for each road based on the evaluation results supplied from the driving risk assessment unit. (14) The information processing device described in (10) above, further comprising an information presentation unit that presents to the driver for each road the amount of insurance premium increase or decrease due to driving on the surrounding roads by comparing the roads with a reference accident probability based on the evaluation results supplied from the driving risk evaluation unit.(15) The information processing device according to (10), further comprising an information presentation unit that presents to the driver, for each road, a difference between insurance premiums for driving on a road among the surrounding roads through which the vehicle is scheduled to pass and insurance premiums for driving on other roads among the surrounding roads, based on the evaluation result supplied from the driving risk evaluation unit. (16) The information processing device according to any one of (1) to (15), wherein the calculation model generation unit hierarchizes feature amounts for calculating the driving risk. (17) The information processing device according to any one of (1) to (16), wherein the calculation model generation unit generates the calculation model based on the road features that differ for each time period. (18) The information processing device according to any one of (1) to (17), wherein the sensor data is acquired by a sensor provided in the vehicle. (19) An information processing system comprising: a vehicle; a dangerous driving extraction unit that extracts risky driving behavior that is a risky driving behavior of a driver who drives the vehicle based on sensor data; a road feature acquisition unit that acquires road features that are characteristics of the road on which the vehicle was traveling at the time of the risky driving behavior based on the sensor data; a calculation model generation unit that generates a calculation model that calculates a driving risk level that indicates the risk of the driver's driving based on the risky driving behavior and the road features; a judgment model generation unit that generates a judgment model that models the relationship between the accident probability and the driving risk level; a driving risk evaluation unit that evaluates the driving risk level of the driver on a specific road based on the accident probability using the calculation model and the judgment model; and an information presentation unit that presents information generated based on the evaluation result by the driving risk evaluation unit to the driver.(20) A program that causes an information processing device to operate as: a dangerous driving extraction unit that extracts risky driving behavior, which is a risky driving behavior of a driver who drives a vehicle, based on sensor data; a road feature acquisition unit that acquires road features, which are characteristics of the road on which the vehicle was traveling at the time of the risky driving behavior, based on the sensor data; a calculation model generation unit that generates a calculation model that calculates a driving risk level that indicates the risk of the driver's driving, based on the risky driving behavior and the road features; a judgment model generation unit that generates a judgment model that models the relationship between the accident probability and the driving risk level; and a driving risk evaluation unit that evaluates the driver's driving risk on a specific road based on the accident probability using the calculation model and the judgment model. (21) An information processing method, in which a road feature acquisition unit extracts, based on sensor data, risky driving behavior that is a risky driving behavior of a driver who drives a vehicle; a road feature acquisition unit acquires, based on the sensor data, road features that are characteristics of the road on which the vehicle was traveling at the time of the risky driving behavior; a calculation model generation unit generates, based on the risky driving behavior and the road features, a calculation model that calculates a driving risk level that indicates the riskiness of the driver's driving; a judgment model generation unit generates a judgment model that models the relationship between the accident probability and the driving risk level; and a driving risk evaluation unit evaluates the driver's driving risk on a specific road based on the accident probability using the calculation model and the judgment model.

[0166] DESCRIPTION OF SYMBOLS 100... Information processing system 110... In-vehicle device 111... Sensor 112... Communication unit 113... Information presentation unit 120... Information processing device 121... Communication unit 122... Dangerous driving extraction unit 123... Road feature acquisition unit 124... Calculation model generation unit 125... Determination model generation unit 126... Driving risk evaluation unit 127... Vehicle sensor log information DB 128... Road information DB

Claims

1. An information processing device comprising: a dangerous driving extraction unit that extracts risky driving behavior, which is a dangerous driving behavior of a driver who drives a vehicle, based on sensor data; a road feature acquisition unit that acquires road features, which are characteristics of the road on which the vehicle was traveling at the time of the risky driving behavior, based on the sensor data; a calculation model generation unit that generates a calculation model that calculates a driving risk level that indicates the risk of the driver's driving based on the risky driving behavior and the road features; a judgment model generation unit that generates a judgment model that models the relationship between the accident probability and the driving risk level; and a driving risk evaluation unit that evaluates the driving risk level of the driver on a specific road based on the accident probability using the calculation model and the judgment model.

2. An information processing device according to claim 1, wherein the driving risk assessment unit inputs the presence or absence or degree of risky driving behavior of the driver into the calculation model, and evaluates the change in driving risk due to the risky driving behavior based on the accident probability.

3. An information processing device according to claim 1, wherein the driving risk assessment unit inputs road characteristics of the road on which the vehicle is traveling or roads around the vehicle into the calculation model, and evaluates the change in driving risk due to the road characteristics based on the accident probability.

4. An information processing device according to claim 1, wherein the driving risk assessment unit inputs the presence or absence or degree of specific risky driving behavior of the driver and the road characteristics of the road on which the vehicle is traveling or the roads around the vehicle into the calculation model, and evaluates the change in driving risk due to the risky driving behavior and the road characteristics based on the probability of an accident.

5. An information processing device according to claim 2, wherein the driving risk assessment unit detects risky driving behavior of the driver based on the sensor data, and inputs the presence or absence or degree of the detected risky driving behavior into the calculation model to calculate the accident probability.

6. An information processing device according to claim 5, further comprising an information presentation unit that presents to the driver an increase in accident probability due to the detected risky driving behavior based on the evaluation results supplied from the driving risk evaluation unit.

7. An information processing device according to claim 5, further comprising an information presentation unit that presents to the driver the reduction in accident probability resulting from the elimination of the detected risky driving behavior, based on the evaluation results supplied from the driving risk evaluation unit.

8. An information processing device according to claim 3, wherein the driving risk assessment unit identifies the road on which the vehicle is traveling based on the vehicle's position information, and inputs the road characteristics of the road into the calculation model to calculate the accident probability.

9. An information processing device according to claim 8, further comprising an information presentation unit that presents to the driver road features that contribute significantly to the accident probability based on the evaluation results supplied from the driving risk evaluation unit.

10. An information processing device according to claim 3, wherein the driving risk assessment unit identifies surrounding roads that are roads around the vehicle based on the vehicle's position information, and inputs the road characteristics of the surrounding roads into the calculation model to calculate the accident probability.

11. An information processing device according to claim 10, further comprising an information presentation unit that presents to the driver the accident probability of each of the surrounding roads based on the evaluation results supplied from the driving risk evaluation unit.

12. An information processing device according to claim 10, further comprising an information presentation unit that presents to the driver, for each road, the difference in accident probability between the road on which the vehicle is scheduled to pass and other roads among the surrounding roads, based on the evaluation results supplied from the driving risk evaluation unit.

13. An information processing device according to claim 10, further comprising an information presentation unit that presents to the driver, for each road, the additional insurance premium amount for driving on the surrounding roads based on the evaluation results supplied from the driving risk evaluation unit.

14. An information processing device according to claim 10, further comprising an information presentation unit that presents to the driver for each road the amount of insurance premium increase or deduction resulting from driving on the surrounding roads by comparing the roads with a reference accident probability based on the evaluation results supplied from the driving risk evaluation unit.

15. An information processing device according to claim 10, further comprising an information presentation unit that presents to the driver, for each road, the difference in insurance premium for driving on the road that the vehicle is scheduled to pass through among the surrounding roads and the insurance premium for driving on other roads among the surrounding roads, based on the evaluation results supplied from the driving risk evaluation unit.

16. An information processing device according to claim 1, wherein the calculation model generation unit hierarchizes the feature quantities used to calculate the driving risk level.

17. An information processing device according to claim 1, wherein the calculation model generation unit generates the calculation model based on the road characteristics that differ for each time period.

18. An information processing device according to claim 1, wherein the sensor data is acquired by a sensor provided in the vehicle.

19. An information processing system comprising: a vehicle; a dangerous driving extraction unit that extracts risky driving behavior that is risky driving behavior of a driver who drives the vehicle based on sensor data; a road feature acquisition unit that acquires road features that are characteristics of the road on which the vehicle was traveling at the time of the risky driving behavior based on the sensor data; a calculation model generation unit that generates a calculation model that calculates a driving risk level that indicates the risk of the driver's driving based on the risky driving behavior and the road features; a judgment model generation unit that generates a judgment model that models the relationship between the accident probability and the driving risk level; a driving risk evaluation unit that evaluates the driving risk of the driver on a specific road based on the accident probability using the calculation model and the judgment model; and an information presentation unit that presents information generated based on the evaluation results by the driving risk evaluation unit to the driver.

20. A program that causes an information processing device to operate as: a dangerous driving extraction unit that extracts risky driving behavior, which is the risky driving behavior of a driver operating a vehicle, based on sensor data; a road feature acquisition unit that acquires road features, which are characteristics of the road on which the vehicle was traveling at the time of the risky driving behavior, based on the sensor data; a calculation model generation unit that generates a calculation model that calculates a driving risk level that indicates the riskiness of the driver's driving, based on the risky driving behavior and the road features; a judgment model generation unit that generates a judgment model that models the relationship between the accident probability and the driving risk level; and a driving risk evaluation unit that evaluates the driver's driving risk on a specific road based on the accident probability using the calculation model and the judgment model.

21. An information processing method in which a road feature acquisition unit extracts risky driving behavior, which is a risky driving behavior of a driver operating a vehicle, based on sensor data; a road feature acquisition unit acquires road features, which are characteristics of the road on which the vehicle was traveling at the time of the risky driving behavior, based on the sensor data; a calculation model generation unit generates a calculation model that calculates a driving risk level indicating the risk of the driver's driving, based on the risky driving behavior and the road features; a judgment model generation unit generates a judgment model that models the relationship between the accident probability and the driving risk level; and a driving risk assessment unit evaluates the driver's driving risk on a specific road based on the accident probability using the calculation model and the judgment model.

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