Vehicle travel control system and vehicle travel control method

Abstract

Provided are a vehicle travel control system and a vehicle travel control method that make it possible to avoid deviation of a host vehicle from an operation design area, suppress the deterioration of traffic flow by reducing the impact of deviation avoidance behavior by the host vehicle on the travel of other vehicles, and improve the evaluation of an automated driving service provided by the host vehicle.　The traffic flow of the host vehicle and other peripheral vehicles is evaluated from predicted traffic conditions determined on the basis of the impact of the host vehicle on the other peripheral vehicles (S105), the deviation risk of the vehicles deviating from an operation design area is evaluated using predicted travel information and environmental state information regarding the operation design area (S106), the suitability of an automated driving service when the host vehicle travels to a destination is evaluated using the predicted travel information (S105), an evaluation value for the traffic flow, an evaluation value for the deviation risk, and an evaluation value for the suitability of the automated driving service are integrated to calculate a comprehensive traffic evaluation value for when the host vehicle travels (S108), a travel plan for the host vehicle is created repeatedly on the basis of the traffic evaluation value (S109, S111), the travel plan is confirmed when the traffic evaluation value reaches a predetermined value (S109, S110), and the travel behavior of the host vehicle is controlled on the basis of the confirmed travel plan.

Description

Vehicle driving control system and vehicle driving control method

[0001] The present invention relates to a vehicle driving control system for automatically driving a vehicle such as an automobile, and a vehicle driving control method.

[0002] In recent years, driving control systems have been developed to enable the automatic driving of various industrial vehicles such as automobiles, transport robots, and construction machinery. Automated driving of industrial vehicles is expected to reduce costs by unmanning the work site and improve work efficiency by standardizing work efficiency. Furthermore, automated driving of automobiles is expected to improve traffic congestion and reduce accidents.

[0003] The configurations and specifications of cruise control systems that realize autonomous driving of vehicles vary widely depending on the target vehicle and the operating environment. Therefore, it is necessary to define an "Operational Design Domain (ODD)," which is the environmental conditions that guarantee the operation of cruise control systems that perform autonomous driving.

[0004] Here, "environmental conditions" refer to weather, road shape, road surface condition, availability of communication infrastructure, etc. that make autonomous driving possible. Note that items other than these may also be set, and are set appropriately depending on the type of vehicle and driving environment.

[0005] An autonomous vehicle (hereinafter, "vehicle") may deviate from its operational design domain (ODD) while traveling in autonomous driving mode. For example, the amount of precipitation during rainy weather may exceed the allowable precipitation amount predetermined for the ODD. Therefore, if the vehicle deviates from the ODD, the cruise control system for autonomous driving may not be able to guarantee normal operation.

[0006] For this reason, a function is required to quickly transition the vehicle to a safe state when it deviates from the operational design domain (ODD). Of course, it goes without saying that deviation from the operational design domain (ODD) can also occur due to other changes in environmental conditions. For example, when changing lanes during autonomous driving, in a situation where deviation from the operational design domain (ODD) is predicted, it is necessary to reduce the adverse impact on traffic flow caused by changing the vehicle's lane.

[0007] Regarding deviation from the operational design domain (ODD) regarding lane changes during autonomous driving, a driving control system described in Japanese Patent Laid-Open No. 2022-53296 (Patent Document 1) described below is known.

[0008] For example, when changing lanes during automated driving, even if there are no other vehicles in a specified area of ​​the adjacent lane and it is possible to start changing lanes, there is a concern that subsequent changes in the driving environment may cause new proximity to other vehicles, which could have a negative impact on traffic flow (e.g., congestion).

[0009] In order to address such issues, in the driving control system of Patent Document 1, when deviation from the operational design domain (ODD) is predicted, a second predetermined area (inter-vehicle distance in the direction of travel) wider than the first predetermined area (inter-vehicle distance in the direction of travel) under normal circumstances is set as a criterion for determining whether or not to change driving lanes.

[0010] Under critical conditions, such as when the vehicle is outside the first predetermined area but there is another vehicle in a larger second predetermined area, the vehicle will not initiate a lane change and will remain in its lane. This is advantageous in that it can prevent adverse effects on traffic flow caused by lane changes even if the driving environment changes thereafter.

[0011] On the other hand, under non-critical conditions where there are no other vehicles in the second specified area, even if the driving environment changes thereafter, there is little possibility of approaching other vehicles traveling in adjacent lanes. Rather, it is more effective to start changing lanes and stop following the other vehicle ahead at a set distance, as this reduces the impact on traffic flow and can prevent the vehicle from missing an opportunity to change lanes.

[0012] Japanese Patent Application Laid-Open No. 2022-53296

[0013] However, the driving control system of Patent Document 1 has the problem that it cannot be used on one-lane roads on general public roads because it controls the vehicle to move to a second predetermined area that is larger than the first predetermined area.

[0014] Furthermore, on public roads with multiple lanes, if one of the lanes is a right-turn-only lane, a decision may be made to avoid deviation from the operational design area (ODD) without taking into account the sign (exclusive lane).

[0015] Therefore, if a vehicle judges a right-turn-only lane to be a wide second predetermined area and retreats into the right-turn-only lane, it may be unable to return to the intended driving route because it can only turn right, which increases the time required to reach the destination and reduces the vehicle's travel efficiency.

[0016] If this travel efficiency decreases, new issues will arise, such as a decline in the evaluation of autonomous driving services, for example, a decline in key performance indicators (KPIs).

[0017] The object of the present invention is to provide a vehicle driving control system and a vehicle driving control method that can prevent the vehicle from deviating from an operational design domain (ODD), reduce the impact of the vehicle's deviation avoidance actions on the driving of other vehicles, thereby suppressing deterioration of traffic flow, and further improve the evaluation of the autonomous driving service provided by the vehicle.

[0018] The present invention comprises: a control object related information acquisition unit that acquires control object related information related to the driving state of the control object and the surrounding environment of the control object; a control object state prediction unit that predicts driving information of the control object based on the control object related information acquired by the control object related information acquisition unit; a traffic situation prediction unit that predicts a traffic situation based on the influence of driving behavior of the control object based on the control object related information acquired by the control object related information acquisition unit and predicted driving information of the control object predicted by the control object state prediction unit; a first evaluation unit that evaluates the traffic situation predicted by the traffic situation prediction unit; an operation design area prediction unit that predicts environmental state information related to an operation design area of ​​the control object using surrounding environment information of the control object acquired by the control object related information acquisition unit; a second evaluation unit that evaluates a deviation risk of the control object deviating from the operation design area using the environmental state information of the operation design area predicted by the operation design area prediction unit and the predicted driving information of the control object predicted by the control object state prediction unit; and a third evaluation unit that evaluates the appropriateness of an automated driving service when the control object drives to a destination using the predicted driving information of the control object state prediction unit. The system is characterized by comprising: a traffic evaluation unit that calculates an overall traffic evaluation value when the controlled object is traveling using the evaluation results of the first evaluation unit, the second evaluation unit, and the third evaluation unit; a driving plan generation unit that creates a driving plan for the controlled object based on the traffic evaluation value of the traffic evaluation unit, which prevents the controlled object from deviating from the operation design area and reduces the impact of the deviation avoidance action of the controlled object on the traveling of other controlled objects; and a control unit that controls the controlled object based on the driving plan generated by the driving plan generation unit.

[0019] The present invention also provides a driving control method in a driving control system for a host vehicle that executes an autonomous driving service, the driving control system comprising the steps of: acquiring host vehicle-related information relating to the driving state of the host vehicle and the surrounding environment; determining predicted driving information for the host vehicle after a predetermined predicted time based on the host vehicle-related information; determining predicted traffic conditions based on the host vehicle-related information and the predicted driving information and on the impact that the driving behavior of the host vehicle will have on other surrounding vehicles; evaluating the traffic flow of the host vehicle and other surrounding vehicles from the predicted traffic conditions; predicting environmental state information relating to the operation design domain of the host vehicle using information about the surrounding environment of the host vehicle; evaluating the deviation risk of the host vehicle deviating from the operation design domain using the environmental state information of the operation design domain and the predicted driving information; evaluating the appropriateness of the autonomous driving service when the host vehicle drives to a destination using the predicted driving information; and calculating an overall traffic evaluation value when the host vehicle drives. The system is characterized by executing the steps of: repeatedly creating a driving plan for the vehicle based on the traffic evaluation value; and, when the traffic evaluation value reaches a predetermined value, finalizing the driving plan; and controlling the steering mechanism, power mechanism, and braking mechanism of the vehicle based on the finalized driving plan.

[0020] According to the present invention, it is possible to prevent the vehicle from deviating from the operational design domain (ODD), reduce the impact of the vehicle's deviation avoidance actions on the driving of other vehicles, suppress deterioration of traffic flow, and further improve the evaluation of the autonomous driving service provided by the vehicle.

[0021] FIG. 1 is a schematic diagram showing the configuration of a vehicle cruise control system according to a first embodiment of the present invention. FIG. 2 is an explanatory diagram explaining a map which is an example of a driving environment in the first embodiment of the present invention. FIG. 3 is an explanatory diagram explaining the relationship between items and numerical values ​​in an operation design domain (ODD) in the first embodiment of the present invention. FIG. 4 is a flowchart explaining operation in the first embodiment of the present invention. FIG. 5 is an explanatory diagram explaining operation when traveling on a general public road in the first embodiment of the present invention. FIG. 6 is an explanatory diagram explaining the state of a work site in the second embodiment of the present invention. FIG. 7 is a schematic diagram showing a vehicle cruise control system according to the second embodiment of the present invention. FIG. 8 is an explanatory diagram explaining the state of a work site in the third embodiment of the present invention. FIG. 9 is a schematic diagram showing a vehicle cruise control system according to the third embodiment of the present invention.

[0022] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. However, the present invention is not limited to the following embodiment, and various modifications and application examples within the technical concept of the present invention are also included within its scope.

[0023] First, an embodiment of the present invention will be described using as an example the driving control of an autonomous vehicle that provides an autonomous driving service in which an automobile carrying passengers automatically (autonomously) drives to a desired location. Note that the present invention can also be applied to the design of general services related to industrial machinery that can automatically drive in a specific environment, and the application of the present invention is not limited to automobiles.

[0024] In order to address the above-mentioned problems, for example, the first embodiment employs the configurations set forth in the claims, for example, claims 1 to 5.

[0025] According to the configuration of the first embodiment, it is possible to provide a vehicle driving control system that can prevent the vehicle from deviating from the operational design domain (ODD), reduce the impact of the vehicle's deviation avoidance actions on the driving of other vehicles, thereby suppressing deterioration of traffic flow, and further improve the evaluation of the autonomous driving service provided by the vehicle.

[0026] Next, an example of driving control of an autonomous driving automobile used in an autonomous driving service will be described. In the embodiment described below, the controlled object is a vehicle such as an automobile, so the "controlled object" will be referred to as a "vehicle." However, since the present invention can be applied to applications other than vehicles, in such cases it will be read as a "controlled object." Note that the expression "controlled object" is used as is in the drawings.

[0027] <Driving control system> The vehicle driving control system 1 includes a vehicle-related information acquisition unit 1a, a vehicle state prediction unit 1b, a traffic situation prediction unit 1c, and a first evaluation unit 1d, and is a system that determines the behavior (hereinafter, sometimes referred to as driving behavior) of the host vehicle 2, which is a vehicle.

[0028] Furthermore, the cruise control system 1 is connected via a control signal line (Sout) to a steering mechanism 4, a vehicle drive mechanism (internal combustion engine or electric motor) 5, and a braking mechanism 6. In accordance with control signals from the cruise control system 1 sent from the control signal line (Sout), the steering mechanism 4, the vehicle drive mechanism 5, and the braking mechanism 6 are controlled to cause the host vehicle 2 to travel along any desired travel route, acceleration / deceleration, vehicle speed, etc.

[0029] Next, the configuration of the vehicle driving control system 1 will be described. As can be seen from the drawings, the following configuration is a functional block, and is executed by a computer activated by well-known logic circuits or software. Examples of computer processors include a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit), but semiconductor devices with other computing functions can also be used as long as they perform the specified computational processing.

[0030] <Vehicle-Related Information Acquisition Unit> The vehicle-related information acquisition unit 1a has a function of acquiring vehicle-related information related to driving state information of the host vehicle 2 and surrounding environment information of the host vehicle 2. For example, the vehicle-related information is acquired from a map information acquisition device 7, a positioning device 8, a state measurement device 9, an external environment recognition device 10, etc., which are connected to the driving control system 1 via an input signal line (Sin).

[0031] The vehicle-related information includes at least surrounding environment information and driving state information.

[0032] The surrounding environment information includes a map of the driving environment, the planned driving route of the vehicle 2, road conditions and signs around the vehicle 2, status values ​​such as the positions and speeds of surrounding vehicles and other moving objects, the size of the vehicles and moving objects, and the types of pedestrians, bicycles, motorcycles, etc. It is also possible to acquire other environmental information.

[0033] The driving state information includes state values ​​such as the speed, acceleration / deceleration, steering angle, front / rear, left / right, and up / down acceleration, roll, and pitch of the vehicle 2. It is also possible to acquire state values ​​other than these.

[0034] In addition, the vehicle-related information acquisition unit 1a also has the function of recognizing lane information (number of lanes, type of lane) on which the vehicle 2 is traveling from both map information and the position information of the vehicle 2, thereby making it possible to acquire information such as the number of lanes and type of lane.

[0035] <Map information acquisition device> The map information acquisition device 7 has a function of acquiring information such as a map 7a of the driving environment as shown in FIG. 2, a position 7b of the vehicle 2 within the map 7a, a destination 7c of the autonomous driving service, and a driving route 7d from the position 7b of the vehicle 2 to the destination 7c.

[0036] The travel route 7d may be calculated by the map information acquisition device 7, or alternatively, a travel route calculated by an external operation planning system (not shown) connected to the map information acquisition device 7 may be acquired, and the map information acquisition device 7 may combine the map 7a and the acquired travel route 7d.

[0037] <Positioning Device> The positioning device 8 has a function of acquiring detailed coordinate position and direction information, such as the position 7b of the vehicle 2 shown in FIG. 2, using a satellite positioning system or the like.

[0038] <State Measurement Device> The state measurement device 9 has a function of acquiring state quantities such as the vehicle speed, longitudinal, lateral, and vertical acceleration, and angular velocities and angles of roll, pitch, yaw, etc. of the host vehicle 2. For example, the state measurement device 9 can be configured using an inertial measurement unit (IMU).

[0039] <External Environment Recognition Device> The external environment recognition device 10 has a function of acquiring three-dimensional shape and color information of the surrounding environment of the vehicle 2, and is configured by, for example, a 3D LiDAR (Light Detection and Ranging), a stereo camera, etc. Alternatively, the external environment recognition device 10 may be configured using a plurality of 3D LiDARs or stereo cameras.

[0040] Based on information from the external environment recognition device 10, the vehicle-related information acquisition unit 1a can estimate state values ​​such as the position and speed of surrounding vehicles and other moving objects, identify the size of the vehicle or moving object, and distinguish between types such as pedestrians, bicycles, and motorcycles.

[0041] <Vehicle state prediction unit> The vehicle state prediction unit 1b acquires vehicle-related information relating to the driving state information of the vehicle 2 and the surrounding environment information of the vehicle from the vehicle-related information acquisition unit 1a, and calculates a predicted value of the driving behavior of the vehicle 2 from the current time to the predicted time (T1) ahead based on this information.

[0042] The predicted driving information, which is a predicted value of the driving behavior, is a time-series change value of the driving route, including the position information of the vehicle 2, the vehicle speed, acceleration / deceleration, attitude, etc., up to the predicted time (T1) ahead. Note that there may also be change values ​​that are not time-series. The driving behavior of the vehicle 2 is predicted, for example, by numerical calculation based on the equation of motion of the vehicle 2. The predicted time (T1) varies from a few seconds to a few minutes and can be set appropriately.

[0043] The predicted times (T2 to T4) described below, including the predicted time (T1) described above, may be set in chronological order from the current time, or may be set to the same time. In short, they are predicted times at which the control described below is established.

[0044] <Traffic Condition Prediction Unit> The traffic condition prediction unit 1c has a function of predicting changes in traffic conditions caused by the impact that the driving behavior of the vehicle 2 has on other vehicles, based on state values ​​such as the positions and speeds of surrounding vehicles and other moving bodies acquired from the vehicle-related information acquisition unit 1a and predicted values ​​of the driving behavior of the vehicle 2 acquired from the vehicle state prediction unit 1b.

[0045] When predicting traffic conditions, the time series change values ​​of the driving route, vehicle speed, acceleration / deceleration, posture, etc., including position information of surrounding vehicles traveling mainly in front of, behind, or adjacent lanes of the vehicle 2, from the current time to the predicted time (T2) ahead, are predicted.

[0046] Furthermore, the driving behavior of the surrounding vehicles up to the predicted time (T2) ahead can be predicted not only by numerical calculation based on the equation of motion of the surrounding vehicles, but also by mathematically modeling the behavior selected by the drivers of the surrounding vehicles and performing numerical calculations on this model.

[0047] For example, the driving behavior of the host vehicle 2, such as acceleration / deceleration and steering operation, to avoid contact is predicted in accordance with the approach of the host vehicle 2 or other surrounding vehicles. The prediction time (T2) is, for example, several seconds.

[0048] Furthermore, there may be differences between the equations of motion of surrounding vehicles and the driver's behavior model and the actual driving behavior, which may reduce the accuracy of predicting driving behavior. As a countermeasure, the errors inherent in the formulas calculated from the equations of motion and the behavior model may be calculated using a probability distribution, and the variance in the driving behavior of each vehicle may be expressed using a probability distribution.

[0049] <First evaluation unit (traffic flow evaluation)> The first evaluation unit 1d has a function of evaluating the traffic flow based on the travel route including the position information of the surrounding vehicles up to the predicted time (T2) ahead, as well as the time-series change values ​​of the vehicle speed, acceleration / deceleration, and attitude received from the traffic situation prediction unit 1c. As the evaluation result of the first evaluation unit 1d, the traffic flow evaluation value (J1) is calculated, for example, by the following "Equation 1".

[0050]

[0051] Here, "i" is the vehicle number, "N" is the total number of surrounding vehicles, "t" is the start time, "h" is the predicted time T2, and "Vi" is the speed of each vehicle. For example, the traffic flow evaluation value (J1) shown in "Equation 1" tends to take a large value when surrounding vehicles accelerate or decelerate. This is the evaluation value used to determine whether congestion is likely to occur.

[0052] In other words, if a surrounding vehicle accelerates or decelerates to avoid the vehicle 2 before the predicted time (T2) has elapsed for the vehicle 2's driving behavior, the traffic flow evaluation value (J1) becomes a large value, and it is considered that the traffic flow of the surrounding vehicles has worsened (a traffic jam has occurred).

[0053] In this way, the first evaluation unit 1d calculates the evaluation value (J1) based on the traffic conditions predicted by the traffic condition prediction unit 1c, based on at least the magnitude of the acceleration and deceleration of other surrounding vehicles present around the host vehicle 2 and the distance between the host vehicle 2. In addition, a term expressing the degree of proximity of the vehicles depending on the distance between the host vehicle 2 and the surrounding vehicles may be added to the evaluation value (J1) of the traffic flow.

[0054] <Operation design area prediction unit> The operation design area prediction unit 1e has a function of predicting an environmental state related to the operation design area (ODD) of the host vehicle 2 using surrounding environment information of the host vehicle 2 acquired from the vehicle-related information acquisition unit 1a. In addition, the environmental state information related to the operation design area (ODD) of the host vehicle 2 may be predicted using a predicted driving route of the host vehicle 2 acquired from the vehicle state prediction unit 1b.

[0055] For example, the environmental state information after the predicted time (T3) includes weather such as precipitation and wind speed on the route traveled by the host vehicle 2, road gradient, presence or absence of speed limits due to traffic regulations, sunshine conditions, etc. The prediction of the environmental state information regarding the operation design domain (ODD) after the predicted time (T3) for the host vehicle 2 may be calculated using an expected value from past statistical values, or may be calculated as a probability distribution of statistical values.

[0056] <Second evaluation unit (evaluation of risk of deviation from operational design area)> The second evaluation unit 1f has the function of comparing the state information of the operational design area (ODD) at the predicted arrival point of the vehicle 2 after the predicted time (T4) predicted by the operational design area prediction unit 1e with predicted driving information such as driving route, vehicle speed, acceleration / deceleration, attitude, etc., including position information of the vehicle 2 after the predicted time (T1) obtained from the vehicle state prediction unit 1b, to evaluate the deviation risk, which is the degree to which the vehicle 2 will deviate from the operational design area (ODD).

[0057] "Deviation" from the operation design domain (ODD) and "deviation risk evaluation value" will be explained using the operation design domain table 11 in Figure 3. As an example, the operation design domain table 11 shows operation design domain items 11a for the host vehicle 2, and for these items, minimum allowable values ​​11b and maximum allowable values ​​11c for the host vehicle 2 are preset. This shows the range of environmental conditions under which the autonomous driving function of the host vehicle 2 operates normally. Also shown are the state values ​​of the operation design domain items 11a for the predicted destination of the host vehicle after the predicted time (T4) and the deviation risk evaluation value (J2). It goes without saying that the operation design domain items 11a can be set to items other than those shown.

[0058] 3, (1) precipitation, (2) wind speed, and (3) illuminance are set as examples of operation design domain items 11a. Also, for each operation design domain item 11a, (1) minimum allowable precipitation value (=0), (2) minimum allowable wind speed (=0), and (3) minimum allowable illuminance value (=20) are set, and further, (1) maximum allowable precipitation value (=20), (2) maximum allowable wind speed (=20), and (3) maximum allowable illuminance (=10000) are set.

[0059] Then, for each operation design domain item 11a of the predicted destination point of the vehicle after the predicted time (T4), (1) the state value of precipitation (=10), (2) the state value of wind speed (=1), and (3) the state value of illuminance (=25) are predicted. Furthermore, a deviation risk evaluation value (J2) is calculated from these predictions. The deviation risk evaluation value (J2) is calculated using the following "Equation 2."

[0060] A "deviation" from the operation design domain (ODD) occurs when, after the predicted time (T4) shown in the operation design domain table 11, the state value 11d of the operation design domain item of the predicted arrival point where the host vehicle 2 is located exceeds the range indicated by the minimum allowable value 11b and maximum allowable value 11c of the host vehicle 2. In other words, a deviation from the operation design domain (ODD) indicates that the automatic driving function of the host vehicle 2 cannot operate normally.

[0061] The evaluation value (J2) of the risk of deviation from the operation design domain is evaluated based on whether the state value 11d of the operation design domain item of the predicted arrival point where the vehicle 2 will be located after the predicted time (T4) is within the range between the minimum allowable value 11b and the maximum allowable value 11c of the vehicle 2 in the operation design domain table 11, and how close it is to the minimum allowable value 11b or the maximum allowable value 11c. As the evaluation result of the second evaluation unit 1f, the evaluation value (J2) of the risk of deviation is calculated, for example, using the following "Equation 2."

[0062]

[0063] Here, "j" is an item in the operation design domain (ODD), "M" is the number of items in the operation design domain (ODD), "Sjmin" is the minimum allowable value of item j in the operation design domain (ODD), "Sjmax" is the maximum allowable value of item j in the operation design domain (ODD), and "OjT4" is the state value of item j in the operation design domain (ODD) at the predicted time (T4). Also, "kj" is a coefficient for adjusting the risk value between each item in the operation design domain (ODD).

[0064] In this way, if any value of the predicted driving information (state value) is within the range of the operation design area (ODD), the difference between all values ​​of the predicted driving information and the values ​​(minimum value, maximum value) of the items in the operation design area (ODD) is calculated, and the calculated values ​​are combined to obtain the deviation risk evaluation value (J2).

[0065] According to "Equation 2," if the state value 11d of the operation design domain item is within the range between the minimum allowable value 11b and the maximum allowable value 11c of the vehicle 2 in the operation design domain table 11 and is close to the minimum allowable value 11b or the maximum allowable value 11c, the deviation risk evaluation value (J2) will be large, indicating a high deviation risk. Also, if the state value OjT4 is calculated using probability density by the operation design domain prediction unit 1e, the deviation risk evaluation value (J2) may be calculated using the maximum expected value for the allowable value.

[0066] <Third Evaluation Unit (Evaluation of Key Performance Indicators)> The third evaluation unit 1g has a function of calculating an evaluation value for the autonomous driving service, for example, an evaluation value (J3) of a key performance indicator (KPI), using time-series change values ​​of the driving route, vehicle speed, acceleration / deceleration, attitude, etc., including position information of the host vehicle 2 up to the predicted time (T1) ahead, obtained from the vehicle state prediction unit 1b. Here, the evaluation value for the autonomous driving service can be said to evaluate the appropriateness of the autonomous driving service.

[0067] The evaluation value for the autonomous driving service is not limited to a key performance indicator (KPI), and other indicators can also be used, and can be selected as needed.

[0068] Key performance indicators (KPIs) are indicators used to evaluate whether driving behavior is more appropriate for an automated driving service. Key performance indicators (KPIs) include, for example, the time required for the vehicle 2 to travel from its position 7b to its destination 7c as shown in FIG. 2, as well as the operating profit and vehicle operation rate obtained by performing automated driving. In particular, operating profit and vehicle operation rate are important evaluation items for the operation of commercial vehicles, and are therefore highly important.

[0069] As the evaluation result of the third evaluation unit 1g in this embodiment, the evaluation value (J3) of the key performance indicator (KPI) regarding the time required to travel to the destination is calculated, for example, by the following "Equation 3".

[0070]

[0071] Here, "R" is the route distance of the travel route from the position 7b of the vehicle 2 to the destination 7c as shown in Fig. 2, and "Vave" is the predicted average vehicle speed until the vehicle 2 reaches the destination 7c. Note that "R" may also be the straight-line distance from the position 7b of the vehicle 2 to the destination 7c.

[0072] Therefore, the evaluation value (J3) of the key performance indicator (KPI) is the square of the time required to travel from the position 7b of the vehicle 2 to the destination 7c as shown in Figure 2, and a larger value of the evaluation value (J3) of the key performance indicator (KPI) means that the key performance indicator (KPI) is lowered.

[0073] <Traffic Evaluation Unit> The traffic evaluation unit 1h has a function of calculating a traffic evaluation value (J) that indicates a change in the overall traffic situation caused by the driving behavior of the host vehicle 2, using the traffic flow evaluation value (J1) of the first evaluation unit 1d, the deviation risk evaluation value (J2) of the second evaluation unit 1f, and the key performance indicator (KPI) evaluation value (J3) of the third evaluation unit 1g. The traffic evaluation value (J) is calculated, for example, by the following "Equation 4."

[0074]

[0075] A large value of this traffic evaluation value (J) indicates an increased risk of deviation from the operation design domain (ODD), a decrease in key performance indicators (KPI), and a negative impact of the vehicle 2 on traffic flow. Priorities for improving the traffic evaluation (J) may be set for each of the evaluation values ​​(J1 to J3). In other words, priorities may be determined in advance to determine which of the evaluation values ​​(J1 to J3) can be changed to efficiently improve the traffic evaluation (J).

[0076] <Driving plan generation unit> The driving plan generation unit 1i has a function of creating a driving plan for the vehicle 2 based on the traffic evaluation value (J) of the traffic evaluation unit 1h, which reduces the adverse impact of the vehicle 2 on traffic flow for other vehicles while maintaining the evaluation value (J2) of the deviation risk of the vehicle 2's operation design domain (ODD) at a predetermined evaluation value, and further improves the autonomous driving service operated by the vehicle 2.

[0077] Here, the deviation risk evaluation value (J2) is expressed as a predetermined evaluation value because it may take a large or small evaluation value depending on the driving behavior of the vehicle 2. In other words, this is because it is assumed that driving behavior will be determined by prioritizing the evaluation value (J3) of the key performance indicator (KPI) in a state where deviation does not occur even if there is a deviation risk.

[0078] The driving plan obtained by the driving plan generation unit 1i is the planned driving route of the vehicle 2 up to the final planned time (T5) and time series values ​​such as vehicle speed value, acceleration / deceleration value, and steering value, which are the control inputs required to realize it.

[0079] For example, the driving plan generation unit can create a driving plan that takes into account the traffic flow evaluation value (J1) to suppress the acceleration and deceleration of other surrounding vehicles due to the driving behavior of the vehicle 2, while maintaining a predetermined inter-vehicle distance between the vehicle 2 and other surrounding vehicles.

[0080] The driving schedule generation unit 1i has a function of calculating a control input value that reduces the traffic evaluation value (J) expressed by "Equation 4" of the traffic evaluation unit 1h to a predetermined value, for example, to a minimum value. One example of the calculation method is model predictive control. An example of a calculation method using this model predictive control will be described using the flowchart in FIG. 4.

[0081] First, a control input value is calculated so as to make the traffic evaluation value (J) calculated by the traffic evaluation unit 1h smaller than that of the previous step, and the process starts from "Step S101" using the control input value.

[0082] <Step S102> Next, in step S102, the vehicle-related information acquisition unit 1a acquires vehicle-related information required for predicting the host vehicle 2 and the surrounding environment. Once the vehicle-related information has been acquired, the process proceeds to step S103.

[0083] In step S103, the vehicle state prediction unit 1b predicts the driving behavior of the host vehicle 2, and the traffic condition prediction unit 1c predicts the surrounding environment information and the driving behavior of the surrounding vehicles. After the driving behavior of the host vehicle 2, the surrounding environment information, the driving behavior of the surrounding vehicles, etc. are predicted, the process proceeds to step S104.

[0084] <Step S104> In step S104, the operation design area prediction unit 1e predicts environmental state information related to the operation design area (ODD) for the vehicle 2. Once the environmental state information for the operation design area (ODD) has been predicted, the process proceeds to steps S105 and S106.

[0085] <Steps S105> to <Steps S106> In step S105, the first evaluation unit 1d calculates a traffic flow evaluation value (J1), and similarly the third evaluation unit 1g calculates a key performance indicator (KPI) evaluation value (J3). In addition, in step S106, the second evaluation unit 1f calculates a deviation risk evaluation value (J2). Once the respective evaluation values ​​(J1, J2, J3) have been calculated, the process proceeds to step S107.

[0086] <Step S107> In step S107, if the deviation risk evaluation value (J2) of the operation design domain (ODD) is greater than a predetermined value, a warning that a deviation state has occurred is issued to the driver of the vehicle 2. If the deviation risk evaluation value (J2) is less than the predetermined value, the process proceeds to step S108.

[0087] In step S107, by notifying the driver in advance of a deviation from the operation design domain (ODD), even if the driver is delegated driving authority for the vehicle 2 at the time of deviation, the driver can continue driving the vehicle 2 promptly.

[0088] In this case, when the deviation risk evaluation value (J2) is equal to or greater than a predetermined value, the operation design area deviation risk notification unit 1j shown in Fig. 1 can issue a notification via the operation design area deviation risk display device 1k that can notify the driver. When the notification process in step S107 is completed, the process proceeds to step S108.

[0089] <Step S108> In step S108, the traffic evaluation unit 1h calculates a traffic evaluation value (J) based on "Equation 4" using the traffic flow evaluation value (J1) of the first evaluation unit 1d obtained in steps S105 and S106, the deviation risk evaluation value (J2) of the second evaluation unit 1f, and the key performance indicator (KPI) evaluation value (J3) of the third evaluation unit 1g. Once the traffic evaluation value (J) is calculated, the process proceeds to step S109.

[0090] <Step S109> In step S109, if the driving plan generation unit 1i determines that the traffic evaluation value (J) has become sufficiently small (Yes judgment), or determines that the difference from the traffic evaluation value (J) of one step before is small (Yes judgment), the driving plan generation unit 1i determines that the control input value is sufficient to improve the traffic evaluation value (J), and proceeds to step S110.

[0091] In this control step, the traffic evaluation value (J) may be controlled to settle at a predetermined value, for example, the minimum value of the traffic evaluation value (J), while searching for a control input value as in step S111 described below.

[0092] On the other hand, if the driving schedule generating unit 1i determines in step S109 that the current traffic evaluation value (J) is worse than the traffic evaluation value of the previous step (No determination), the process proceeds to step S111.

[0093] <Step S110> In step S110, since the determination in step S109 is Yes, the control input value is output from the driving plan generation unit 1i to the control unit 3, and the process returns to step 102 again, and the above-mentioned control steps are repeated.

[0094] <Step S111> In step S111, since the determination in step S109 is No, a control input value that can improve the traffic evaluation value (J) from one step before is searched for, and the calculation in step S103 is repeated using the searched control input value. In other words, the traffic evaluation value (J) is controlled so as to converge to a predetermined value, preferably a minimum value, while the control input value is searched for. This forms a feedback system in which the traffic evaluation value (J) is returned from the driving plan generation unit 1i to the vehicle state prediction unit 1b in FIG. 1. When the traffic evaluation value (J) reaches a predetermined value (for example, a minimum value), the control input value at this time is confirmed and becomes the confirmed control input value.

[0095] At this time, as mentioned above, it is possible to set priorities for improvement for each of the evaluation values ​​(J1 to J3) used to calculate the traffic evaluation value (J). In this case, the control input values ​​for each evaluation value (J1 to J3) may be searched for by multi-objective optimization.

[0096] In addition, instead of using "Number 2", the evaluation value (J2) of the deviation risk in the operation design domain (ODD) may be obtained by setting the range shown in the operation design domain table 11 in Figure 3 as a constraint condition, and searching for the control input value in step S111.

[0097] <Control unit> The control unit 3 calculates a control amount to realize a traffic evaluation value (J) based on the determined control input values, which are time-series values ​​of the vehicle speed value, acceleration / deceleration value, and steering value obtained from the driving plan generation unit 1i, and outputs the calculated control amount to the steering mechanism 4, the vehicle drive mechanism 5, and the braking mechanism 6 as a control signal (Sout).

[0098] This makes it possible to reduce the negative impact of the vehicle on traffic flow for other vehicles while maintaining the evaluation value of the risk of deviation from the operation design domain (ODD) of the vehicle at a predetermined evaluation value, and further improve the key performance indicators (KPIs) of the autonomous driving service operated by the vehicle.

[0099] Next, an example of the operation of the cruise control system having the above configuration and its effects will be briefly described.

[0100] 5, the host vehicle 2 and a following nearby vehicle 12a are traveling in a first driving lane 13a, and a nearby vehicle 12b is traveling in an adjacent second driving lane 13b. The first driving lane 13a and the second driving lane 13b are located at an intersection equipped with a traffic light 14. The first driving lane 13a is a straight lane, and the second driving lane 13b is a right-turn-only lane.

[0101] As a route to the destination 7c, a driving route 15a via the first driving lane 13a and a driving route 15b via the second driving lane 13b are available as driving route candidates.

[0102] Under such circumstances, the second evaluation unit 1f calculates an evaluation value (J2) of the risk of deviation of the operation design area (ODD) of the vehicle 2, and if the evaluation value (J2) of the risk of deviation is high, when the vehicle 2 decelerates to avoid deviation from the operation design area (ODD), the following surrounding vehicle 12a will also be required to decelerate rapidly, and the evaluation value (J1) of the traffic flow will be increased.

[0103] Therefore, the host vehicle 2 selects to change lanes to the second driving lane 13b, which can reduce the evaluation value of the traffic flow (J1) and the evaluation value of the deviation risk (J2) by suppressing the acceleration / deceleration of the following surrounding vehicle 12a caused by the driving behavior of the host vehicle 2 based on "Equation 1." In this way, the host vehicle 2 can avoid the risk of deviation from the operation design domain (ODD) by changing lanes to the second driving lane 13b.

[0104] On the other hand, if the vehicle 2 takes the second driving lane 13b, the driving route 15b to the destination 7c will be longer than the driving route 15a when the vehicle 2 takes the first driving lane 13a. This increases the time required to reach the destination 7c, and the key performance indicator (KPI) of the autonomous driving service will decrease.

[0105] In contrast, by using the driving control system 1 shown in Figure 1, it is possible to select driving behavior for the vehicle 2 that can avoid the risk of deviating from the operation design domain (ODD) and reduce the negative impact of traffic flow on other vehicles, thereby improving the key performance indicators (KPIs) of the autonomous driving service.

[0106] In other words, the second evaluation unit 1f calculates the deviation risk evaluation value (J2) of the operation design domain (ODD) of the vehicle 2, and when the deviation risk evaluation value (J2) is relatively high, the traffic flow evaluation value (J1) of the first evaluation unit 1d and the key performance indicator (KPI) evaluation value (J3) of the third evaluation unit 1g are taken into account, and the driving behavior described below can be performed.

[0107] For example, deviation from the operation design domain (ODD) is avoided while maintaining a relatively high evaluation value (J2) of the deviation risk of the operation design domain (ODD), and the traveling speed of the vehicle 2 is adjusted based on the traffic flow evaluation value (J1) and the evaluation value (J3) of the key performance indicator (KPI) to accelerate or decelerate the vehicle 2 so as to minimize the impact on surrounding vehicles 12a. This makes it possible to maintain traveling in the first traveling lane 13a and execute a traveling behavior of the vehicle 2 that shortens the time required to reach the destination 7c.

[0108] In this way, according to this embodiment, it is possible to prevent the vehicle from deviating from the operational design domain (ODD), reduce the impact of the vehicle's deviation avoidance actions on the driving of other vehicles, suppress deterioration of traffic flow, and further improve the evaluation of the autonomous driving service provided by the vehicle.

[0109] Next, a second embodiment of the present invention will be described. While the first embodiment has been described with respect to an autonomously driving automobile, the second embodiment is an example in which the present invention is applied to a dump truck used at a construction site or the like to transport earth and sand. The second embodiment employs the configuration described in the claims, for example, claim 6.

[0110] At a construction work site 16 such as that shown in Fig. 6, construction vehicles such as an excavator 17 and an automatically driven dump truck 19 are in operation every day. As an example, Fig. 6 shows an operation in which an excavator 17 placed on a work platform 18 excavates earth and sand on the work platform 18 and loads the earth and sand onto an automatically driven dump truck 19 that automatically stops in a parking area 21.

[0111] Here, it is assumed that the autonomous dump truck 19 is equipped with the travel control system shown in Fig. 1. The shovel 17 shown in Fig. 6 moves backward while excavating an excavation area 20, which is a work platform 18 under the front feet. Therefore, the position of the stopping area 21 suitable for loading changes each time the shovel 17 excavates earth and moves backward.

[0112] At this time, depending on the road surface condition of the stopping area 21, it may be difficult for the autonomously driven dump truck 19 to stop. For example, this may occur if there is a huge rock in the stopping area 21. In such a case, the autonomously driven dump truck 19 may not be able to stop at an appropriate stopping area 21. As a result, the excavator 17 may not be able to load the excavated earth and sand onto the autonomously driven dump truck 19, which may result in a decrease in the work efficiency of the entire construction work site 16.

[0113] The automatic driving of the automatically driving dump truck 19 is basically performed by the driving control system shown in FIG. 1, and therefore a detailed description thereof will be omitted here.

[0114] Next, a second embodiment will be described with reference to Fig. 7. Compared to the first embodiment, the cruise control system of Fig. 7 is characterized by including a steering device 23 and an emergency cruise control unit 22 connected to the steering device 23 via a signal line.

[0115] If the driving plan generation unit 1i determines that there is a risk that the autonomously driven dump truck 19 will deviate from the operation design area (ODD) and determines that it is not possible to generate a driving plan for the autonomously driven dump truck 19 that maintains or improves the previous traffic evaluation value (J), it has the function of determining that the autonomously driven dump truck 19 will ultimately deviate from the operation design area (ODD) and sending deviation judgment information to the control unit 3.

[0116] The control unit 3 has a function of switching over the driving control of the autonomously driven dump truck 19 so that it can be executed by the emergency driving control unit 22 when it receives deviation determination information.

[0117] The steering device 23 is, for example, a driving operation device equipped with a steering wheel, an accelerator pedal, a brake pedal, etc. for remote driving, which is installed at a location away from the construction work site and is wirelessly connected to the autonomously driving dump truck 19. The driving operation amount of the steering device 23 is transmitted to the emergency driving control unit 22 via a wireless communication section, and the emergency driving control unit 22 transmits the received driving operation amount to the control unit 3.

[0118] For example, if there is a huge rock in the stopping area 21, the second evaluation unit 1f determines that the rock will deviate from the flatness required for the stopping area 21, which is an item in the operation design domain (ODD) of the autonomously driving dump truck 19. In addition, the driving plan generation unit 1i determines that it is not possible to generate a driving plan for the autonomously driving dump truck 19 that maintains or improves the traffic evaluation value (J) from the previous traffic evaluation value (J).

[0119] As a result, the driving plan generation unit 1i determines that the autonomously driving dump truck 19 will eventually deviate from the operation design domain (ODD), and transmits deviation determination information to the control unit 3. The control unit 3 receives the deviation determination information and switches the driving control of the autonomously driving dump truck 19 to the emergency driving control unit 22.

[0120] As a result, the travel control of the autonomously driven dump truck 19 is based on remote operation by the steering device 23. Then, at the discretion of the driver in the remote cab, the operation of the autonomously driven dump truck 19 at the construction work site 16 is temporarily changed, the parking area 21 is set to a location that avoids huge rocks, and the travel control of the autonomously driven dump truck 19 is performed by remote operation. This makes it possible to prevent a decrease in work efficiency at the entire construction work site 16.

[0121] Furthermore, the emergency travel control unit 22 is provided with a degenerate control function that can autonomously respond to emergencies without using the steering device 23, and can also autonomously generate a travel plan for emergency evacuation.

[0122] Next, a third embodiment of the present invention will be described. While the first embodiment has been described with respect to an autonomously driven automobile, the third embodiment is an example in which the present invention is applied to an autonomous guided vehicle that carries out packages used in a logistics warehouse or the like. The third embodiment employs the configuration described in the claims, for example, claim 7.

[0123] In a logistics warehouse 24 as shown in Fig. 8, in order to ship packages stored on shelves 26, an automated guided vehicle 25 transports the packages from the shelves 26 to a shipping area 27. Here, the automated guided vehicle 25 is assumed to be equipped with the driving control system shown in Fig. 7.

[0124] 8, luggage placed in a shipping area 27 is transported by a forklift 28 to a truck 29 for shipping. An unmanned transport area 30 is set as an operation design domain (ODD) for the automated guided vehicle 25, and the automated guided area 30 is designed as an area where no vehicles other than the automated guided vehicle 25 can enter, except for the shipping area 27.

[0125] At this time, if a worker 31 enters the unmanned transport area 30, the second evaluation unit 1f of the driving control system shown in Figure 7 determines that the intrusion of the worker 31 may cause the automated transport vehicle 25 to deviate from the operation design area (ODD).

[0126] As a result, the emergency travel control unit 22 brings the automated guided vehicle 25 to an emergency stop or switches it to a degenerate mode, such as a low-speed travel mode. As a result, the travel behavior of the automated guided vehicle 25 is restricted by the control unit 3, which ultimately poses a problem of a decrease in the efficiency of transporting goods at the logistics warehouse 24.

[0127] Next, a third embodiment that addresses this issue will be described with reference to Fig. 9. Compared to the second embodiment, the cruise control system in Fig. 9 is characterized by including an operation design domain change proposing unit 32. Note that the automatic driving of the automated guided vehicle 25 is basically performed by the cruise control system shown in Fig. 1, so a detailed description will be omitted here.

[0128] The third embodiment includes, in addition to the components of the second embodiment, an operation design domain change proposing unit 32, which is connected via a communication line to a proposal notification device 33. The operation design domain change proposing unit 32 has a function of retaining the accumulated value of the number of deviations in the operation design domain (ODD), and also has a function of transmitting a change proposal for an item in the operation design domain (ODD) to the proposal notification device 33 when the accumulated value of the number of deviations exceeds a predetermined threshold.

[0129] The proposal notification device 33 has a function of notifying the manager of proposed changes to items in the operation design domain (ODD). The notification method can be visual or audible.

[0130] Next, the operation and effects of this embodiment will be briefly described.

[0131] For example, a typical automated guided vehicle driving control system has the problem that every time a worker 31 enters the unmanned transport area 30, the driving behavior of the automated guided vehicle 25 is suppressed, resulting in a decrease in cargo transport efficiency.

[0132] 1, the vehicle-related information acquisition unit 1a can detect in advance the intrusion of a worker 31 into the automated guided vehicle area 30. Therefore, the operation design area prediction unit 1e predicts the intrusion of the worker 31, and based on this prediction, the driving plan generation unit 1i can calculate a driving plan that avoids deviation from the operation design area (ODD) while suppressing adverse effects on other automated guided vehicles and also suppresses a decrease in cargo transport efficiency, which is a key performance indicator (KPI).

[0133] However, in either case, it is difficult to achieve the luggage transport efficiency in a normal state without the intrusion of the worker 31. That is, in either case, the intrusion of the worker 31 restricts the traveling behavior of the automated guided vehicle 25.

[0134] In contrast, in this embodiment, the efficiency of cargo transportation is improved by assuming in advance that the intrusion of the worker 31 into the unmanned transport area 30 is a necessary action. That is, each time the second evaluation unit 1f determines that the worker 31 has deviated from the operation design domain (ODD) by intrusion, the operation design domain change proposing unit 32 counts the number of deviations of the worker that have been determined to be a deviation by the second evaluation unit 1f.

[0135] When the cumulative deviation count exceeds a predetermined threshold, the operation design area change proposing unit 32 functions to derive a proposal to add "permission to enter" for the worker 31 to the operation design area (ODD). The threshold may be, for example, several times. Setting the cumulative deviation count in this way is a determination that the worker 31 is entering the automated transport area 30 as needed and is repeating the actions required for transporting cargo.

[0136] The operation design area change proposing unit 32 transmits the derived proposal to the proposal notification device 33, and notifies, for example, an administrator (operation design area designer) of the proposed change to the operation design area (ODD). The administrator (operation design area designer) adds permission for the worker 31 to enter the operation design area (ODD) of the automated guided vehicle 25, and updates the driving control system as the new operation design area (ODD).

[0137] With the updated driving control system, even if the worker 31 enters the automated guided vehicle area 30, the second evaluation unit 1f does not determine that the worker 31 has deviated from the operation design domain (ODD), and the deviation risk evaluation value (J2) is not affected. Therefore, the driving plan generation unit 1i can generate a driving plan that improves baggage transport efficiency, which is a key performance indicator (KPI), while minimizing the impact on other automated guided vehicles, without considering avoiding deviation from the operation design domain (ODD). As a result, the updated driving control system can prevent a decrease in baggage transport efficiency due to the worker 31's intrusion.

[0138] In the above-described embodiments, examples have been given in which the present invention is applied to self-driving automobiles, self-driving construction vehicles, and self-transport vehicles, but the present invention can also be applied to other self-driving vehicles, such as agricultural vehicles and autonomous work robots.

[0139] The present invention is not limited to the above-described embodiments, but includes various modifications. The above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. It is also possible to add, delete, or replace other configurations with respect to the configuration of each embodiment.

[0140] 1...cruising control system, 1a...control object related information acquisition unit, 1b...control object state prediction unit, 1c...traffic situation prediction unit, 1d...first evaluation unit, 1e...operation design area prediction unit, 1f...second evaluation unit, 1g...third evaluation unit, 1h...traffic evaluation unit, 1i...driving plan generation unit, 1j...operation design area deviation risk notification unit, 1k...operation design area deviation risk display device, 2...vehicle, 3...control unit, 4...steering mechanism, 5...vehicle drive mechanism, 6...braking mechanism, 7...map information acquisition device, 8...positioning device, 9...state measurement device, 10...external environment recognition device, 1 1...operation design area table, 12a, 12b...surrounding vehicles, 13a, 13b...lanes, 14...traffic lights, 15a, 15b...route, 16...work site, 17...shovel, 18...work platform, 19...autonomous driving dump truck, 20...excavation area, 21...stopping area, 22...emergency driving control unit, 23...steering device, 24...logistics warehouse, 25...autonomous guided vehicle, 26...shelf, 27...shipping area, 28...forklift, 29...truck, 30...unmanned guided area, 31...worker, 32...operation design area change proposal unit, 33...proposal notification device

Claims

1. A control object related information acquisition unit that acquires control object related information related to the driving state of a controlled object and the surrounding environment of the controlled object; a control object state prediction unit that predicts driving information of the controlled object based on the control object related information acquired by the control object related information acquisition unit; a traffic condition prediction unit that predicts a traffic condition based on an influence of the driving behavior of the controlled object based on the control object related information acquired by the control object related information acquisition unit and the predicted driving information of the controlled object predicted by the control object state prediction unit; a first evaluation unit that evaluates the traffic condition predicted by the traffic condition prediction unit; a traffic design area prediction unit that predicts environmental state information related to an operation design area of ​​the controlled object using the surrounding environment information of the controlled object acquired by the control object related information acquisition unit; a second evaluation unit that evaluates a deviation risk of the controlled object deviating from the operation design area using the environmental state information of the operation design area predicted by the operation design area prediction unit and the predicted driving information of the controlled object predicted by the controlled object state prediction unit; a third evaluation unit that uses predicted driving information from the control object state prediction unit to evaluate the appropriateness of an autonomous driving service when the control object drives to a destination; a traffic evaluation unit that uses a first evaluation result from the first evaluation unit, a second evaluation result from the second evaluation unit, and a third evaluation result from the third evaluation unit to calculate a comprehensive traffic evaluation value when the control object drives; a driving plan generation unit that creates a driving plan for the control object based on the traffic evaluation value from the traffic evaluation unit, which prevents the control object from deviating from the operation design area and reduces the impact of the deviation avoidance action of the control object on the driving of other control objects; and a control unit that controls the control object based on the driving plan generated by the driving plan generation unit.

2. A vehicle driving control system as described in claim 1, wherein the second evaluation unit has a function of comparing the predicted driving information obtained from the control object state prediction unit with one or more values ​​indicating the range of the operation design area predicted by the operation design area prediction unit, and determining that the control object will deviate from the operation design area if any value of the predicted driving information exceeds the range of the operation design area, and a function of calculating the difference between all values ​​of the predicted driving information and the values ​​of the items of the operation design area if all values ​​of the predicted driving information are within the range of the operation design area, combining the calculated values ​​and setting them as the second evaluation result, and the driving plan generation unit creates a driving plan to change the driving route of the control object or adjust the vehicle speed to suppress an increase in the difference from the range of the operation design area based on the second evaluation result.

3. The vehicle driving control system according to claim 1, wherein the appropriateness of the autonomous driving service is a key performance indicator (KPI / Key Performance Indicator).

4. A vehicle driving control system as described in claim 2, characterized in that the second evaluation unit has a function of transmitting information indicating a deviation from the operational design area to a display device when it is determined that the controlled object will deviate from the operational design area, and the display device has a function of notifying the driver when it receives information indicating a deviation from the operational design area.

5. A vehicle driving control system as described in claim 2, wherein the first evaluation unit has a function of calculating the first evaluation result based on at least the magnitude of acceleration and deceleration of other control objects present around the controlled object and the magnitude of the inter-vehicle distance from the controlled object based on the traffic conditions predicted by the traffic condition prediction unit, and the driving plan generation unit has a function of creating a driving plan that takes into account the first evaluation result, suppresses the acceleration and deceleration of the other control objects caused by the operation of the controlled object, and obtains a predetermined inter-vehicle distance between the controlled object and the other control objects.

6. A vehicle driving control system as described in claim 2, wherein the driving plan generation unit has a function of determining that it is difficult to prevent the controlled object from deviating from the operational design area and sending this determination information to the control unit when the second evaluation unit determines that the controlled object will deviate from the operational design area and determines that the traffic evaluation value cannot generate a driving plan that maintains or improves the previous traffic evaluation value, and the control unit has a function of switching the driving control of the controlled object to an emergency driving control unit upon receiving the determination information.

7. A vehicle driving control system as described in claim 2, further comprising an operation design area change proposal unit, said operation design area change proposal unit being connected to a proposal notification device via a communication line, and when the second evaluation unit determines that the controlled object will deviate from the operation design area, said operation design area change proposal unit has a function of receiving deviation information from the second evaluation unit and retaining an accumulated value of the number of deviations, and when the accumulated value of the number of deviations exceeds a predetermined number, has a function of sending a proposal to change the range of deviation in the operation design area to said proposal notification device, and said proposal notification device has a function of notifying an administrator of the change proposal.

8. A host vehicle related information acquisition unit having a function of acquiring host vehicle related information related to the driving state of the host vehicle executing the autonomous driving service and the surrounding environment; a host vehicle state prediction unit having a function of calculating predicted driving information of the host vehicle based on the host vehicle related information; a traffic condition prediction unit having a function of calculating predicted traffic conditions based on the impact of the driving behavior of the host vehicle on other surrounding vehicles based on the host vehicle related information and the predicted driving information; a first evaluation unit having a function of evaluating the traffic flow of the host vehicle and the other surrounding vehicles from the predicted traffic conditions; a traffic design area prediction unit having a function of predicting environmental state information related to the operation design area of ​​the host vehicle using surrounding environment information of the host vehicle; a second evaluation unit having a function of evaluating a deviation risk of the host vehicle deviating from the operation design area using environmental state information of the operation design area and the predicted driving information; and a third evaluation unit having a function of evaluating the appropriateness of the autonomous driving service when the host vehicle drives to a destination using the predicted driving information. a traffic evaluation unit having a function of calculating an overall traffic evaluation value when the host vehicle is traveling using an evaluation value of the first evaluation unit, an evaluation value of the second evaluation unit, and an evaluation value of the third evaluation unit; a travel plan generation unit having a function of repeatedly creating a travel plan for the host vehicle based on the traffic evaluation value, and confirming the travel plan when the traffic evaluation value reaches a predetermined value; and a control unit that controls the host vehicle based on the confirmed travel plan.

9. A driving control method in a driving control system of a vehicle that executes an autonomous driving service, the driving control system comprising: a step of acquiring host vehicle-related information relating to the driving state of the host vehicle and the surrounding environment; a step of determining predicted driving information of the host vehicle based on the host vehicle-related information; a step of determining a predicted traffic situation based on the impact of the driving behavior of the host vehicle on other surrounding vehicles based on the host vehicle-related information and the predicted driving information; a step of evaluating the traffic flow of the host vehicle and the other surrounding vehicles from the predicted traffic situation; a step of predicting environmental state information relating to the operation design area of ​​the host vehicle using surrounding environment information of the host vehicle; a step of evaluating a deviation risk of the host vehicle deviating from the operation design area using environmental state information of the operation design area and the predicted driving information; a step of evaluating the appropriateness of the autonomous driving service when the host vehicle drives to a destination using the predicted driving information; and a step of calculating a comprehensive traffic evaluation value when the host vehicle drives. a step of repeatedly creating a driving plan for the host vehicle based on the traffic evaluation value, and when the traffic evaluation value reaches a predetermined value, a step of confirming the driving plan; and a step of controlling a steering mechanism, a power mechanism, and a braking mechanism of the host vehicle based on the confirmed driving plan.

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