Vehicle travel control system and vehicle travel control method

Abstract

To provide a vehicle travel control system and a vehicle travel control method which avoid an own vehicle from deviating from a design operation range and curbs deterioration of a traffic flow.SOLUTION: A vehicle travel control system: evaluates a traffic flow of an own vehicle and another surrounding vehicle using a predicted traffic condition based on an influence of the own vehicle (S105); evaluates a deviation risk that a vehicle deviates from a design operation range (S106); evaluates appropriateness of an automatic operation service to cause the own vehicle to travel to a destination based on predicted travel information (S105); calculates a comprehensive traffic evaluation value by integrating respective evaluation values for the traffic flow, the deviation risk and the appropriateness of the automatic operation service (S108); recreates a travel plan of the own vehicle based on the traffic evaluation value (S109 and S111); finalizes the travel plan when the traffic evaluation value reaches a predetermined value (S109 and S110); and controls travel behavior of the own vehicle based on the finalized travel plan.SELECTED DRAWING: Figure 4

Description

[Technical field]

[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. [Background technology]

[0002] In recent years, driving control systems have been developed to automatically drive various industrial vehicles such as automobiles, transport robots, and construction machines. By automatically driving industrial vehicles, it is expected that costs will be reduced by automating the work site and that work efficiency will be improved by standardizing work efficiency. In addition, it is expected that automatic driving of automobiles will 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 the "operational design domain (ODD)", which is the environmental conditions that guarantee the operation of the cruise control system performing autonomous driving.

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

[0005] In an autonomous vehicle (hereinafter referred to as a vehicle), there are cases where the vehicle deviates from the operational design domain (ODD) while driving autonomously. For example, the amount of precipitation during rainy weather may exceed the allowable precipitation amount predefined in the operational design domain (ODD). Therefore, if the vehicle deviates from the operational design domain (ODD), there is a risk that the cruise control system for autonomous driving will not be able to guarantee normal operation.

[0006] For this reason, if the vehicle deviates from the operational design domain (ODD), it is necessary to have a function that quickly transitions the vehicle to a safe state. Of course, it goes without saying that deviations 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 effects on traffic flow caused by changing lanes of the vehicle.

[0007] Regarding deviation from the operational design domain (ODD) regarding lane changes during autonomous driving, a driving control system described in JP 2022-53296 A (Patent Document 1) described below is known.

[0008] For example, when changing lanes during autonomous driving, even if there are no other vehicles in a specified area of ​​an adjacent lane and the lane change can be started, there is a concern that subsequent changes in the driving environment may result in 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 specified area (inter-vehicle distance in the driving direction) wider than the first specified area (inter-vehicle distance in the driving direction) under normal circumstances is set as a criterion for determining whether or not to change driving lanes.

[0010] Then, under critical conditions where the vehicle is outside the first predetermined area but is in a larger second predetermined area, the lane change is not initiated and the vehicle remains in the lane, which is advantageous in suppressing 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 a low possibility of approaching other vehicles traveling in adjacent lanes. Rather, it is more effective to start changing lanes and leave following the other vehicle ahead at a set distance, as this reduces the impact on traffic flow and is likely to avoid missing the opportunity to change lanes. [Prior art documents] [Patent documents]

[0012] [Patent Document 1] Patent Publication No. 2022-53296 Summary of the Invention [Problem to be solved by the invention]

[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 specified area that is larger than the first specified area.

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

[0015] Therefore, if a vehicle judges the right-turn-only lane to be a wide second specified area and retreats into the right-turn-only lane, it may not be able to return to the intended driving route because it can only turn right, which will increase the time required to reach the destination and reduce the vehicle's movement efficiency.

[0016] If this travel efficiency decreases, new issues will arise, such as a decline in the evaluation of autonomous driving services, for example 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. [Means for solving the problem]

[0018] The present invention relates to a control object related information acquisition unit that acquires control object related information relating to a driving state of the control object and a 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 condition prediction unit that predicts a traffic condition based on an influence of a driving behavior of the controlled object based on the controlled object related information acquired by the controlled object related information acquisition unit and the predicted driving information of the controlled object predicted by the controlled object state prediction unit; A first evaluation unit that evaluates the traffic condition predicted by the traffic condition prediction unit; an operation design area prediction unit that predicts environmental state information regarding 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 operational design area using the environmental state information of the operational design area predicted by the operational 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 evaluates the appropriateness of an automated driving service when the controlled object travels to a destination using the predicted travel information of the controlled object state prediction unit; A traffic evaluation unit that calculates a comprehensive traffic evaluation value when the control object is traveling using the evaluation result of the first evaluation unit, the evaluation result of the second evaluation unit, and the evaluation result of the third evaluation unit; a travel plan generation unit that generates a travel plan for the controlled object based on the traffic evaluation value of the traffic evaluation unit, the travel plan preventing the controlled object from departing from the operation design region and reducing the influence of the deviation avoidance action of the controlled object on the travel of other controlled objects; a control unit that controls a control target based on the driving plan generated by the driving plan generation unit; The present invention is characterized by comprising:

[0019] The present invention also provides a method for producing a semiconductor device comprising the steps of: A driving control method in a driving control system of a vehicle that executes an autonomous driving service, comprising: The driving control system is acquiring vehicle-related information relating to a driving state of the vehicle and a surrounding environment; A step of obtaining predicted driving information of the host vehicle after a predetermined prediction time based on the host vehicle related information; A step of calculating a predicted traffic situation based on the influence of the driving behavior of the host vehicle on other surrounding vehicles based on the host vehicle-related information and the predicted driving information; evaluating a traffic flow of the host vehicle and other surrounding vehicles based on a predicted traffic situation; A step of predicting environmental state information regarding a driving 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 and predicted driving information of the operation design area; Using the predicted driving information, evaluate the appropriateness of the autonomous driving service when the vehicle drives to the destination; A step of integrating the traffic flow evaluation value, the deviation risk evaluation value, and the appropriateness evaluation value of the autonomous driving service to calculate a comprehensive traffic evaluation value when the host vehicle is traveling; repeatedly creating a travel plan for the host vehicle based on the traffic evaluation value, and finalizing the travel plan when the traffic evaluation value reaches a predetermined value; Controlling the steering mechanism, the power mechanism, and the braking mechanism of the host vehicle based on the determined driving plan; The present invention is characterized by carrying out the following steps. Effect of the Invention

[0020] According to the present invention, it is possible to prevent the vehicle from departing from the operational design domain (ODD), reduce the impact of the vehicle's departure 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. [Brief description of the drawings]

[0021] [Figure 1] 1 is a schematic diagram showing a configuration of a vehicle cruise control system according to a first embodiment of the present invention. [Diagram 2] FIG. 2 is an explanatory diagram for explaining a map as an example of a driving environment in the first embodiment of the present invention. [Diagram 3] FIG. 2 is an explanatory diagram illustrating the relationship between items and numerical values ​​of an operation design domain (ODD) in the first embodiment of the present invention. [Figure 4] FIG. 2 is a flow chart illustrating an operation according to the first embodiment of the present invention. [Diagram 5] FIG. 2 is an explanatory diagram illustrating the operation when traveling on a general public road in the first embodiment of the present invention. [Figure 6] FIG. 11 is an explanatory diagram illustrating a state of a work site in a second embodiment of the present invention. [Figure 7] FIG. 4 is a schematic diagram showing a vehicle cruise control system according to a second embodiment of the present invention. [Figure 8] FIG. 13 is an explanatory diagram illustrating a state of a work site in a third embodiment of the present invention. [Figure 9] FIG. 11 is a schematic diagram showing a vehicle cruise control system according to a third embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[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. EXAMPLES

[0023] First, an embodiment of the present invention will be described by taking as an example the driving control of an autonomous vehicle that executes an autonomous driving service in which an automobile carrying passengers drives itself (autonomously) to any desired location. Note that the present invention can also be applied to the design of general services related to industrial machines capable of autonomous driving 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 configurations described 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 departing from the operational design domain (ODD), reduce the impact of the vehicle's departure 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 vehicle 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 described as a "vehicle", but the present invention can be applied to things other than vehicles, so in that case it should be read as a "controlled object". Note that the expression "controlled object" is used as is in the drawings.

[0027] <Drive control system> The vehicle driving control system 1 is equipped with a vehicle-related information acquisition unit 1a, a vehicle state prediction unit 1b, a traffic condition 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 vehicle itself, namely, the host vehicle 2.

[0028] Furthermore, the cruise control system 1 is connected to a steering mechanism 4, a vehicle drive mechanism (internal combustion engine or electric motor) 5, and a braking mechanism 6 via a control signal line (Sout). According to a control signal 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 an arbitrary travel route, at an arbitrary acceleration / deceleration, at an arbitrary 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. Although a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit) can be considered as an example of a computer processor, it is also possible to use a semiconductor device equipped with other arithmetic functions as long as it executes a predetermined arithmetic process.

[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 vehicle 2 and surrounding environment information of the 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, and the like, 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, a 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, sizes of vehicles and moving objects, 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 a function of recognizing lane information (number of lanes, type of lane) on which the vehicle 2 is traveling from both map information and 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, the position 7b of the vehicle 2 on 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 driving route 7d may be calculated by the map information acquisition device 7, or, alternatively, a driving route calculated by, for example, 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 driving 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, by using a satellite positioning system or the like.

[0038] <Status measurement device> The state measurement device 9 has a function of acquiring state quantities such as the vehicle speed, forward / backward, left / right, and up / down acceleration, and angular velocities and angles of roll, pitch, yaw, and the like of the host vehicle 2. For example, it can be configured using an inertial measurement unit (IMU).

[0039] <External world 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, for example, by a 3D LiDAR (Light Detection And Ranging), a stereo camera, etc. In addition, it 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 vehicles and moving objects, and distinguish between types of vehicles 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 host vehicle 2 and the surrounding environment information of the host vehicle from the vehicle-related information acquisition unit 1a, and calculates a predicted value of the driving behavior of the host vehicle 2 from the current time to the predicted time (T1) 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 be cases where the change value is not a time series. The prediction of the driving behavior of the vehicle 2 is performed, for example, by a numerical calculation based on the equation of motion of the vehicle 2. Furthermore, the predicted time (T1) varies from a few seconds to a few minutes and can be set appropriately.

[0043] Incidentally, the predicted times (T2 to T4) described later, 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 later is established.

[0044] <Traffic Condition Prediction Division> 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 host vehicle 2 has on other vehicles, based on state values ​​such as the positions, speeds, etc. 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 host vehicle 2 acquired from the vehicle state prediction unit 1b.

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

[0046] In addition, 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 modeling the behavior selected by the drivers of the surrounding vehicles using a mathematical formula and predicting it by numerical calculation of this model.

[0047] For example, the driving behavior based on acceleration / deceleration and steering operation for performing contact avoidance behavior is predicted in accordance with the approach of the host vehicle 2 and other surrounding vehicles, etc. The prediction time (T2) is, for example, several seconds.

[0048] Furthermore, since there may be a difference between the equation of motion of the surrounding vehicles or the driver's behavior model and the actual driving behavior, the accuracy of predicting the driving behavior may decrease. As a countermeasure to this, the error of the formula in the numerical value calculated from the equation of motion or the behavior model may be calculated using a probability distribution, and the variance of the driving behavior of each vehicle may be expressed using a probability distribution.

[0049] <First evaluation section (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, and the time-series change values ​​of the vehicle speed, acceleration / deceleration, and attitude received from the traffic condition prediction unit 1c. As the evaluation result of the first evaluation unit 1d, the evaluation value (J1) of the traffic flow is calculated, for example, by the following "Equation 1".

[0050]

number

[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 be large 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 host vehicle 2 before the predicted time (T2) for the driving behavior of the host vehicle 2 has elapsed, the traffic flow evaluation value (J1) becomes large, and it is regarded as having worsened the traffic flow of the surrounding vehicles (causing a traffic jam).

[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 around the host vehicle 2 and the distance between the host vehicle 2. In addition, a term expressing the proximity of the vehicles according to the distance between the host vehicle 2 and the surrounding vehicles may be added to the traffic flow evaluation value (J1).

[0054] <Operation Design Area Prediction Division> The operation design domain prediction unit 1e has a function of predicting an environmental state related to the operation design domain (ODD) of the host vehicle 2 using the 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 domain (ODD) of the host vehicle 2 may be predicted using the 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 driving route traveled by the 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) of the vehicle 2 may be calculated as an expected value from past statistical values, or may be calculated as a probability distribution of statistical values.

[0056] <Second evaluation section (assessment of risk of deviation from operational design domain)> The second evaluation unit 1f has a function of comparing state information of the operation design domain (ODD) at the predicted destination of the vehicle 2 after the predicted time (T4) predicted by the operation design domain prediction unit 1e with predicted driving information such as driving route, vehicle speed, acceleration / deceleration, posture, 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 deviates from the operation design domain (ODD).

[0057] "Deviation" from the operation design domain (ODD) and the "deviation risk evaluation value" will be explained using the operation design domain table 11 in Fig. 3. As an example, the operation design domain table 11 has operation design domain items 11a of the host vehicle 2, and for these items, the minimum allowable value 11b and the maximum allowable value 11c of the host vehicle 2 are set in advance. This shows the range of environmental conditions for the automatic driving function of the host vehicle 2 to operate normally. In addition, the state value of the operation design domain item 11a of the predicted arrival point of the host vehicle after the predicted time (T4) and the deviation risk evaluation value (J2) are shown. It goes without saying that the operation design domain item 11a can be set to items other than those shown.

[0058] In Fig. 3, (1) precipitation, (2) wind speed, and (3) illuminance are set as examples of operation design area items 11a. In addition, for each operation design area item 11a, (1) minimum allowable precipitation value (=0), (2) minimum allowable wind speed value (=0), and (3) minimum allowable illuminance value (=20) are set, and further, (1) maximum allowable precipitation value (=20), (2) maximum allowable wind speed value (=20), and (3) maximum allowable illuminance value (=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. In addition, the 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 operational design domain (ODD) is when, after the predicted time (T4) shown in the operational design domain table 11, the state value 11d of the operational design domain item of the predicted destination point where the host vehicle 2 is located exceeds the range indicated by the allowable minimum value 11b and the allowable maximum value 11c of the host vehicle 2. In other words, a deviation from the operational 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 destination 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, by the following "Equation 2".

[0062]

number

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

[0064] In this way, when all values ​​of the predicted driving information (state values) are within the range of the operational design domain (ODD), the differences between all values ​​of the predicted driving information and the values ​​(minimum, maximum) of the items in the operational design domain (ODD) are calculated, and the calculated values ​​are combined to obtain the deviation risk assessment 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 by the operation design domain prediction unit 1e using probability density, the deviation risk evaluation value (J2) may be calculated using the maximum expected value for the allowable value.

[0066] <Third Evaluation Section (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 the position information of the vehicle 2 up to the predicted time (T1) ahead, acquired 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] In addition, the evaluation value for the autonomous driving service is not limited to key performance indicators (KPIs), and other indicators can be used, and can be selected as needed.

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

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

[0070]

number

[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 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 move 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 a decrease in the key performance indicator (KPI).

[0073] <Transportation Evaluation Division> The traffic evaluation unit 1h has a function of calculating a traffic evaluation value (J) indicating a change in overall traffic conditions caused by the driving behavior of the 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]

number

[0075] If the traffic evaluation value (J) is large, it indicates an increased risk of deviation from the operation design domain (ODD), a decrease in the key performance indicators (KPI), and a negative impact of the vehicle 2 on the traffic flow. Note that a priority order for improving the traffic evaluation (J) may be set for each of the evaluation values ​​(J1 to J3). In other words, a priority order may be determined in advance as to which of the evaluation values ​​(J1 to J3) should be changed to efficiently improve the traffic evaluation (J).

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

[0077] Here, the evaluation value (J2) of the deviation risk may be large or small depending on the driving behavior of the vehicle 2, so it is expressed as a predetermined evaluation value. In other words, it is assumed that the driving behavior will be determined by giving priority to 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 ​​of the vehicle speed, acceleration / deceleration values, steering values, etc., which are the control inputs required to realize the planned route.

[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 host vehicle 2, while maintaining a specified inter-vehicle distance between the host vehicle and other surrounding vehicles.

[0080] The driving schedule generating unit 1i has a function of calculating a control input value that makes the traffic evaluation value (J) expressed by "Equation 4" of the traffic evaluation unit 1h a predetermined value, for example, a minimum value. One example of the calculation method is model predictive control. An example of the calculation method using this model predictive control will be described with reference to 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. After acquiring the vehicle-related information, the process proceeds to step S103.

[0083] <Step S103> 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 predicting the driving behavior of the host vehicle 2, the surrounding environment information, the driving behavior of the surrounding vehicles, etc., the process proceeds to step S104.

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

[0085] <Step S105> to <Step S106> In step S105, the first evaluation unit 1d obtains an evaluation value (J1) of the traffic flow, and similarly, the third evaluation unit 1g obtains an evaluation value (J3) of the key performance indicator (KPI). In addition, in step S106, the second evaluation unit 1f obtains an evaluation value (J2) of the deviation risk. After obtaining the respective evaluation values ​​(J1, J2, J3), 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 notification that a deviation state will occur 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 departure from the operational design domain (ODD), even if the driver is delegated driving authority of the vehicle 2 at the time of the departure, 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 capable of notifying 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 "Mathematical formula 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. When the traffic evaluation value (J) is obtained, 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 the previous step is small (Yes judgment), the driving plan generation unit 1i determines that the control input value sufficiently improves the traffic evaluation value (J) and proceeds to step S110.

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

[0092] On the other hand, in step S109, if the driving schedule generating unit 1i determines 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 schedule generating unit 1i to the control unit 3, and the process returns to step 102 again to repeat the above-mentioned control steps.

[0094] <Step S111> In step S111, since the result of the determination in step S109 is No, a control input value that can improve the traffic evaluation value (J) of one step before is searched for, and the calculation in step S103 is repeated again using the 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 searching for a control input value. This is a feedback system that 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 as the confirmed control input value.

[0095] At this time, as described above, it is possible to set a priority order 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, the evaluation value (J2) of the deviation risk in the operational design domain (ODD) may be set as a constraint condition in the range shown in operational design domain table 11 in FIG. 3 instead of "Math 2", and the control input value may be searched for in step S111.

[0097] <Control Unit> The control unit 3 calculates a control amount for realizing 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 as a control signal (Sout) to the steering mechanism 4, the vehicle drive mechanism 5, and the braking mechanism 6.

[0098] This makes it possible to reduce the adverse impact of the vehicle on traffic flow for other vehicles while maintaining the evaluation value of the risk of deviation from the operational design domain (ODD) of the vehicle at a specified 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-mentioned configuration and its effects will be briefly described.

[0100] In the traffic situation shown in Fig. 5, the host vehicle 2 and the surrounding vehicle 12a following behind are traveling in the first travel lane 13a, and the surrounding vehicle 12b is traveling in the adjacent second travel lane 13b. The first travel lane 13a and the second travel lane 13b are located at an intersection equipped with a traffic light 14, and the first travel lane 13a is a straight lane and the second travel 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 exist as candidates for the driving route.

[0102] Under such circumstances, the second evaluation unit 1f calculates an evaluation value (J2) of the risk of deviation from the operational design domain (ODD) of the host vehicle 2. If the evaluation value (J2) of the risk of deviation is high, when the host vehicle 2 decelerates to avoid deviation from the operational design domain (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 (J1) of the traffic flow while reducing the evaluation value (J2) of the deviation risk by suppressing the acceleration / deceleration of the surrounding vehicle 12a following behind caused by the driving behavior of the host vehicle 2 based on "Mathematical formula 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, when the vehicle 2 uses the second driving lane 13b, the driving route 15b to the destination 7c becomes longer than the driving route 15a when the vehicle 2 uses 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 decreases.

[0105] In response to this, 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 operational 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, so that the driving behavior described below can be performed.

[0107] For example, deviation from the operational design domain (ODD) is avoided while maintaining a relatively high deviation risk evaluation value (J2) for the ODD, and the travel speed of the host vehicle 2 is adjusted based on the traffic flow evaluation value (J1) and the key performance indicator (KPI) evaluation value (J3) to accelerate or decelerate the host vehicle 2 so as to minimize the impact on the surrounding vehicles 12a. This makes it possible to maintain travel in the first travel lane 13a and execute a travel behavior of the host 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 departing from the operational design domain (ODD), reduce the impact of the vehicle's departure 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. EXAMPLES

[0109] Next, a second embodiment of the present invention will be described. The first embodiment has been described with respect to an autonomous vehicle, but 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 soil and sand. The second embodiment employs the configuration described in the claims, for example, claim 6.

[0110] At a construction work site 16 as shown in Fig. 6, a shovel 17 and an automatically driven dump truck 19 are in operation as construction vehicles on a daily basis. Fig. 6 shows, as an example, an operation in which a shovel 17 placed on a work platform 18 excavates soil on the work platform 18 and loads the soil onto an automatically driven dump truck 19 that automatically stops in a parking area 21.

[0111] Here, the autonomous dump truck 19 is assumed to be 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 every time the shovel 17 excavates earth and moves backward.

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

[0113] Incidentally, the automatic driving of the automatically driving dump truck 19 is basically performed by the driving control system shown in FIG. 1, so 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 in Fig. 7 is characterized in that it includes 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 domain (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), the driving plan generation unit 1i has a function of determining that the autonomously driven dump truck 19 will ultimately deviate from the operation design domain (ODD) and sending deviation judgment information to the control unit 3.

[0116] The control unit 3 has a function of switching over the travel control of the autonomously driven dump truck 19 so that it can be executed by the emergency travel control unit 22 upon receiving 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 provided at a position away from the construction work site and wirelessly connected to the autonomously driven 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 deviates from the flatness required for the stopping area 21, which is an item of the operation design domain (ODD) of the autonomously driven 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 driven 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 generating 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 driving control of the autonomous 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 autonomous dump truck 19 at the construction work site 16 is temporarily changed, the parking area 21 is set to a place to avoid huge rocks, and the driving of the autonomous dump truck 19 is remotely controlled. This makes it possible to suppress a decrease in the work efficiency of the entire construction work site 16.

[0121] In addition, the emergency travel control unit 22 is provided with a degenerate control function capable of autonomously responding to an emergency without using the steering device 23, and can also autonomously generate a travel plan for emergency evacuation. EXAMPLES

[0122] Next, a third embodiment of the present invention will be described. The first embodiment has been described with respect to an autonomous vehicle, but the third embodiment is an example of the case where the present invention is applied to an autonomous transport vehicle that carries out luggage 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 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) of the unmanned transport vehicle 25, and the unmanned transport area 30 is designed as an area where nothing other than the unmanned transport vehicle 25 can enter, except for the shipping area 27.

[0125] At this time, if a worker 31 enters the automated guided vehicle 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 guided vehicle 25 to deviate from the operation design domain (ODD).

[0126] As a result, the emergency travel control unit 22 makes an emergency stop of the automated guided vehicle 25 or transitions to a degenerate mode, for example, 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 results in a problem of a decrease in the baggage transport efficiency of 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 driving control system in Fig. 9 is characterized by having an operation design domain change proposing unit 32. Note that the automatic driving of the automated guided vehicle 25 is basically performed by the driving control system shown in Fig. 1, so a detailed description will be omitted here.

[0128] In the third embodiment, in addition to the components of the second embodiment, an operation design domain change proposing unit 32 is provided, and the operation design domain change proposing unit 32 is connected to a proposal notification device 33 via a communication line. 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 the items 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 value.

[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 audio.

[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 a problem in that every time a worker 31 enters an unmanned transport area 30, the driving behavior of the automated guided vehicle 25 is restricted, resulting in a decrease in luggage transport efficiency.

[0132] 1, the vehicle-related information acquisition unit 1a can detect in advance the intrusion of the worker 31 into the automated guided vehicle area 30. Therefore, the operation design domain 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 domain (ODD) while suppressing adverse effects on other automated guided vehicles and further suppresses a decrease in baggage transportation efficiency, which is a key performance indicator (KPI).

[0133] However, in either case, it is difficult to achieve the same 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 travel behavior of the automated guided vehicle 25.

[0134] In contrast, in this embodiment, the efficiency of transporting luggage 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, every 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 accumulates the number of deviations of the worker that are determined to be deviations by the second evaluation unit 1f.

[0135] Then, when the accumulated deviation count exceeds a predetermined threshold, the operation design domain change proposing unit 32 functions to derive a proposal to add "permission to enter" the operator 31 to the operation design domain (ODD). The threshold may be, for example, several times. Setting the accumulated deviation count in this way is a judgment that the operator 31 is deemed to be entering the unmanned transport area 30 as necessary and is repeating the actions required for transporting luggage.

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

[0137] According to 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 operational design domain (ODD), and the deviation risk evaluation value (J2) is not affected. Therefore, the driving plan generating unit 1i can generate a driving plan that improves the baggage transport efficiency, which is a key performance indicator (KPI), while suppressing the impact on other automated guided vehicles, without considering avoiding deviation from the operational design domain (ODD). As a result, the updated driving control system can suppress a decrease in baggage transport efficiency due to the worker 31's intrusion.

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

[0139] The present invention is not limited to the above-mentioned embodiments, but includes various modified examples. The above-mentioned embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those having all the configurations described. In addition, it is possible to replace a 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. [Explanation of symbols]

[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...self-driving dump truck, 20...digging area, 21...parking area, 22...emergency driving control unit, 23...steering gear, 24...logistics warehouse, 25...automated transport vehicle, 26...shelf, 27...shipping area, 28...forklift, 29...truck, 30...unmanned transport 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 relating to a running state of the control object and a 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 condition prediction unit that predicts a traffic condition based on an influence of a driving behavior of the control object based on the control object related information acquired by the control object related information acquisition unit and the predicted driving information of the control 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; an operation design area prediction unit that predicts environmental state information regarding an operation design area of ​​the control object by using the 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 environmental state information of the operation design area predicted by the operation design area prediction unit and predicted travel information of the control object predicted by the control object state prediction unit; and A third evaluation unit that evaluates the appropriateness of an autonomous driving service when the control object travels to a destination using the predicted travel information of the control object state prediction unit; a traffic evaluation unit that calculates a comprehensive traffic evaluation value when the control object is traveling, using a first evaluation result of the first evaluation unit, a second evaluation result of the second evaluation unit, and a third evaluation result of the third evaluation unit; a travel plan generation unit that creates a travel plan for the controlled object based on the traffic evaluation value of the traffic evaluation unit, the travel plan preventing the controlled object from departing from the operation design domain and reducing an impact of the departure avoidance action of the controlled object on travel of other controlled objects; a control unit that controls the control target based on the driving plan generated by the driving plan generation unit; A vehicle driving control system comprising:

2. 2. The vehicle cruise control system according to claim 1, the second evaluation unit has a function of comparing predicted driving information acquired 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 deviates from the operation design area when any value of the predicted driving information exceeds the range of the operation design area; and when any value of the predicted driving information is within the range of the operation design area, calculating differences between all values ​​of the predicted driving information and values ​​of items of the operation design area, integrating the calculated values ​​and setting them as the second evaluation result; The driving plan generation unit generates a driving plan for changing a driving route of the control object or adjusting a vehicle speed in order to suppress an increase in a difference from the range of the operation design domain based on the second evaluation result. A vehicle driving control system comprising:

3. 2. The vehicle cruise control system according to claim 1, The suitability of the autonomous driving service is a key performance indicator (KPI). A vehicle driving control system comprising:

4. 3. The vehicle cruise control system according to claim 2, The second evaluation unit has a function of transmitting information indicating a deviation from the operation design area to a display device when it is determined that the control object deviates from the operation design area, The display device has a function of notifying the driver when information indicating a deviation from the operation design area is received. A vehicle driving control system comprising:

5. 3. The vehicle cruise control system according to claim 2, the first evaluation unit has a function of calculating the first evaluation result based on at least magnitudes of acceleration and deceleration of another control object present around the control object and a distance between the control object and the other control object based on the traffic condition predicted by the traffic condition prediction unit, The driving plan generating unit has a function of generating a driving plan for suppressing acceleration and deceleration of another controlled object due to the operation of the controlled object by taking into account the first evaluation result, and for obtaining a predetermined inter-vehicle distance between the controlled object and the other controlled object. A vehicle driving control system comprising:

6. 3. The vehicle cruise control system according to claim 2, the driving plan generation unit has a function of determining that it is difficult to avoid the deviation of the operation design area of ​​the control object when the second evaluation unit determines that the control object will deviate from the operation design area and that the traffic evaluation value cannot generate the driving plan that maintains or improves the previous traffic evaluation value, and transmitting this determination information to the control unit; 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. A vehicle driving control system comprising:

7. 3. The vehicle cruise control system according to claim 2, The system further includes an operation design domain change proposal unit, and the operation design domain change proposal unit is connected to the proposal notification device via a communication line, When the second evaluation unit determines that the control object deviates from an operation design area, the operation design area change proposal unit has a function of receiving deviation information from the second evaluation unit and holding an integrated value of the number of deviations, and has a function of transmitting a proposal to change the range of deviation of the operation design area to the proposal notification device when the integrated value of the number of deviations exceeds a predetermined number, The proposal notification device has a function of notifying an administrator of the change proposal. A vehicle driving control system comprising:

8. A vehicle-related information acquisition unit having a function of acquiring vehicle-related information related to the driving state of a vehicle that executes an 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 a predicted traffic condition based on an influence that a driving behavior of the host vehicle has 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 a traffic flow of the host vehicle and the other surrounding vehicles based on the predicted traffic situation; an operation design area prediction unit that predicts environmental state information regarding a vehicle operation design area using surrounding environment information of the vehicle; A second evaluation unit having a function of evaluating a deviation risk of the host vehicle deviating from the operation design area by using the environmental state information of the operation design area and the predicted travel information; A third evaluation unit having a function of evaluating the appropriateness of an autonomous driving service when the vehicle travels to a destination using the predicted travel information; a traffic evaluation unit having a function of calculating a comprehensive traffic evaluation value when the host vehicle is traveling, using the evaluation value of the first evaluation unit, the evaluation value of the second evaluation unit, and the evaluation value of the third evaluation unit; a travel plan generating unit that has a function of repeatedly generating a travel plan for the host vehicle based on the traffic evaluation value, and finalizing the travel plan when the traffic evaluation value reaches a predetermined value; a control unit that controls the host vehicle based on the determined driving plan; A vehicle driving control system comprising:

9. A driving control method in a driving control system of a vehicle that executes an autonomous driving service, The driving control system includes: acquiring vehicle-related information relating to a driving state of the vehicle and a surrounding environment; determining predicted travel information of the host vehicle based on the host vehicle-related information; A step of calculating a predicted traffic situation based on an influence of a driving behavior of the host vehicle on other surrounding vehicles, based on the host vehicle-related information and the predicted driving information; evaluating a traffic flow of the host vehicle and the other surrounding vehicles based on the predicted traffic conditions; A step of predicting environmental state information regarding a driving 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 travel information; Using the predicted driving information, evaluate the appropriateness of an autonomous driving service when the vehicle drives to a destination; A step of integrating the evaluation value of the traffic flow, the evaluation value of the deviation risk, and the evaluation value of the appropriateness of the autonomous driving service to calculate a comprehensive traffic evaluation value when the host vehicle is traveling; repeatedly creating a travel plan for the host vehicle based on the traffic evaluation value, and finalizing the travel plan when the traffic evaluation value reaches a predetermined value; controlling a steering mechanism, a power mechanism, and a braking mechanism of the host vehicle based on the determined driving plan; A vehicle driving control method comprising:

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