Patent ID: 12214810

DETAILED DESCRIPTION OF EMBODIMENTS

An embodiment of the present disclosure will be described with reference to accompanying drawings.

1. Overview of Remote Assistance

FIG.1is a conceptual diagram illustrating a remote assistance system according to the present embodiment. The remote assistance system includes an autonomous driving vehicle1, a remote assistance device2, and a communication network3.

The autonomous driving vehicle1is capable of autonomous driving. Here, as the autonomous driving, it is assumed that a driver does not necessarily have to fully concentrate on driving (a so-called autonomous driving of Level3or higher). The autonomous driving vehicle1may be an autonomous driving vehicle of Level4or higher in which a driver is not required. The autonomous driving vehicle1is a target of remote assistance in the present embodiment.

The remote assistance device2is a device used for executing remote assistance to the autonomous driving vehicle1, and is operated by a remote operator. The autonomous driving vehicle1is connected to the remote assistance device2such that the autonomous driving vehicle1and the remote assistance device2are capable of communicating with each other via the communication network3. The remote assistance device2communicates with the autonomous driving vehicle1via the communication network3to execute the remote assistance to traveling of the autonomous driving vehicle1. In more detail, the remote operator operates the remote assistance device2to execute the remote assistance to the traveling of the autonomous driving vehicle1. It can be said that the remote assistance device2is a device that supports the remote assistance for the autonomous driving vehicle1by the remote operator.

The communication network3includes a wireless base station, a wireless communication network, a wired communication network, and the like. As the wireless communication network, a 5G network is exemplified.

FIG.2is a conceptual diagram used for describing an overview of the remote assistance according to the present embodiment. The autonomous driving system10controls the autonomous driving vehicle1. During the autonomous driving, the autonomous driving system10executes various vehicle processes. Typical vehicle processes during the autonomous driving include the followings.(1) Recognition processing: the autonomous driving system10recognizes the situation on the surroundings of the autonomous driving vehicle1using a recognition sensor. For example, the autonomous driving system10recognizes a signal display of a signal device (for example, a green signal, a yellow signal, a red signal, and a right turn signal) using a camera.(2) Action determination processing: the autonomous driving system10determines whether to execute an action based on a result of the recognition processing. Examples of the action include starting, stopping, turning right, turning left, and changing lanes.(3) Timing determination processing: the autonomous driving system10determines an execution timing for executing the above actions.

Typically, a situation in which the remote assistance by the remote operator is required is a situation where the autonomous driving is difficult. For example, the remote assistance can be required at an intersection, as illustrated inFIG.3.

For example, when a signal device installed at the intersection is exposed to the sunlight, the recognition accuracy of the signal display can be reduced. When the signal display cannot be accurately distinguished by the recognition processing, the autonomous driving system10requires the remote assistance for signal recognition. Further, when the signal display cannot be distinguished, it is also difficult to determine what action should be executed at what timing. Therefore, the autonomous driving system10requires the remote assistance also for the action determination processing and the timing determination processing.

Even when the signal display is distinguished, a situation where it may be difficult to determine whether the action may actually be executed is also conceivable. For example, even after the signal display viewed from the autonomous driving system10is “right turn possible”, an oncoming vehicle may enter an intersection, or an oncoming vehicle or a preceding vehicle may remain in the intersection. In such a case, the autonomous driving system10may request the remote assistance for the action determination processing or the timing determination processing while the vehicle stops.

As yet another example, when a construction section exists in front of the autonomous driving vehicle1, a situation where it may be difficult to determine whether to change lanes is also conceivable. In such a case, the autonomous driving system10may request the remote assistance for the action determination processing.

The autonomous driving system10may request the remote operator to remotely drive (remotely operate) the autonomous driving vehicle1. In the present embodiment, the “remote assistance” is a concept that includes not only assistance for at least one of the recognition processing, the action determination processing, and the timing determination processing, but also the remote driving (the remote operation).

Upon determining that the remote assistance is required, the autonomous driving system10transmits a remote assistance request REQ to the remote assistance device2via the communication network3. The remote assistance request REQ is information for requesting the remote assistance for the autonomous driving vehicle1from the remote operator. The remote assistance device2notifies the remote operator of the received remote assistance request REQ. In response to the remote assistance request REQ, the remote operator starts the remote assistance for the autonomous driving vehicle1.

During the remote assistance, the autonomous driving system10transmits vehicle information VCL to the remote assistance device2via the communication network3. The vehicle information VCL indicates a state of the autonomous driving vehicle1, the situation on the surroundings thereof, a result of the vehicle processing by the autonomous driving system10, and the like. The remote assistance device2presents the remote operator with the vehicle information VCL received from the autonomous driving system10. For example, as illustrated inFIG.2, the remote assistance device2displays image information IMG captured by a camera mounted on the autonomous driving vehicle1on the display device.

The remote operator executes the remote assistance for the autonomous driving vehicle1with reference to the vehicle information VCL. An operator instruction INS is an instruction to the autonomous driving vehicle1input by the remote operator. The remote assistance device2receives an input of the operator instruction INS from the remote operator. Then, the remote assistance device2transmits the operator instruction INS to the autonomous driving vehicle1via the communication network3. The autonomous driving system10receives the operator instruction INS from the remote assistance device2and controls the autonomous driving vehicle1according to the received operator instruction INS.

2. Processing at Time of Occurrence of Abnormality in Remote Assistance System

2-1. Abnormality in Remote Assistance System

In the present embodiment, a “remote assistance system4” means a configuration or a function used for providing the remote assistance to the autonomous driving vehicle1. For example, the remote assistance system4includes the remote assistance device2, the communication network3, a communication device mounted on the autonomous driving vehicle1, and the like (seeFIG.1). Examples of the communication device mounted on the autonomous driving vehicle1include a communication electronic control unit (ECU), a communication module, and a transmission/reception circuit.

Hereinafter, a case where an “abnormality” occurs in at least a part of the remote assistance system4that provides the remote assistance to the autonomous driving vehicle1is conceivable.

For example, abnormalities in the remote assistance system4include a “functional failure” in which a function of the remote assistance system4is lost. An example of the functional failure of the remote assistance system4is a communication disruption. For example, when trouble occurs in the communication network3, a communication disruption may occur. Another example of the functional failure in the remote assistance system4is a defect (down) of the remote assistance device2. Yet another example of the functional failure in the remote assistance system4is a defect of the communication device mounted on the autonomous driving vehicle1. When the functional failure occurs in the remote assistance system4, it is impossible to provide the remote assistance to the autonomous driving vehicle1.

Abnormalities in the remote assistance system4may include “performance degradation” in which the function of the remote assistance system4is degraded. An example of performance degradation of the remote assistance system4is significant degradation in communication speed or a throughput. Another example of performance degradation of the remote assistance system4is a significant increase in a communication delay. Yet another example of performance degradation of the remote assistance system4is degradation in internal communication speed or calculation speed in the communication ECU mounted on the autonomous driving vehicle1. When performance degradation occurs in the remote assistance system4, the accuracy of the remote assistance may be degraded.

2-2. Retracting Processing

When an abnormality occurs in the remote assistance system4, the remote assistance cannot be provided to the autonomous driving vehicle1, or the accuracy of the remote assistance is reduced. Then, when an abnormality is detected in the remote assistance system4, the autonomous driving system10executes “retracting processing” for safely retracting the autonomous driving vehicle1.

FIG.4is a conceptual diagram used for describing an example of the retracting processing according to the present embodiment. A “target retracting position PE” is a target stop position when causing the autonomous driving vehicle1to stop by the retracting processing. The target retracting position PE may be set at a safe position on a road. In the example illustrated inFIG.4, the target retracting position PE is set on a road shoulder. The autonomous driving system10controls the autonomous driving vehicle1such that the autonomous driving vehicle1travels toward the target retracting position PE and stops at the target retracting position PE. For example, the autonomous driving system10generates a target trajectory TR through which the autonomous driving vehicle1travels from a current position toward the target retracting position PE and stops at the target retracting position PE. Then, the autonomous driving system10controls the traveling of the autonomous driving vehicle1such that the autonomous driving vehicle1follows the target trajectory TR.

An area that can be used as the target retracting position PE in the retracting processing may be determined in advance. The area that can be used as the target retracting position PE in the retracting processing is hereinafter referred to as a “stop candidate area AC”.

FIG.5is a conceptual diagram used for describing an example of the stop candidate area AC. In order to describe the stop candidate area AC, first, a “stop prohibited area AX” will be described. The stop prohibited area AX is an area in which parking and stopping of a vehicle are prohibited, and is determined in advance by the Road Traffic Act and the like. In the example illustrated inFIG.5, the stop prohibited area AX includes a crosswalk and an area having a predetermined width on the surroundings of the crosswalk. The stop prohibited area AX may include an intersection and an area having a predetermined width on the surroundings of the intersection. In addition, the stop prohibited area AX also includes an area before firefighting equipment and the like.

The stop candidate area AC is selected from among areas other than the stop prohibited area AX on the road. Typically, the stop candidate area AC is a part of an area other than the stop prohibited area AX. For example, the stop candidate area AC is selected from the viewpoint of ensuring the safety of the stopped autonomous driving vehicle1. As illustrated inFIG.5, the stop candidate area AC may be an area relatively close to a road edge. The stop candidate area AC may be set to include a road shoulder or a roadside zone.

As illustrated inFIG.6, a priority may be set in the stop candidate area AC. A high priority area ACH is a stop candidate area AC having a relatively high priority, and a low priority area ACL is a stop candidate area AC having a relatively low priority. In the example illustrated inFIG.6, straight sections are set as the high priority areas ACHs and a curved section is set as the low priority area ACL.

The stop candidate area AC and the stop prohibited area AX are registered in advance in, for example, map information. In the retracting processing, the autonomous driving system10may set the target retracting position PE such that the target retracting position PE is included in the stop candidate area AC. When the priority is set in the stop candidate area AC, the autonomous driving system10sets the target retracting position PE such that the target retracting position PE is included in the stop candidate area AC having a priority as high as possible.

2-3. Retracting Margin Section

As described above, when an abnormality is detected in the remote assistance system4, the autonomous driving system10executes the retracting processing. However, it is not always required to cause the autonomous driving vehicle1to make an emergency stop immediately after an abnormality is detected in the remote assistance system4. This is because, in a situation where the remote assistance is not required, the autonomous driving system10can continue the autonomous driving as usual. In other words, it is not required to cause the autonomous driving vehicle1to rush to change lanes or suddenly decelerate even when an abnormality is detected in the remote assistance system4. With the present embodiment, the autonomous driving system10sets the target retracting position PE also in consideration of a possibility that the remote assistance may be required.

The position at which the remote assistance for the autonomous driving vehicle1may be required is hereinafter referred to as a “specific position PS”. For example, the specific position PS is an intersection, as illustrated inFIG.3. As another example, the specific position PS may be a position other than an operational design domain (ODD), which is an area in which the autonomous driving is possible. As yet another example, the specific position PS may include a construction section, a traffic congestion section, an accident occurrence position, and the like. Typically, the specific position PS is registered in advance in the map information. Alternatively, information on the specific position PS, such as a traffic congestion section or an accident occurrence position, may be acquired in real time.

FIG.7illustrates an example of a situation of the autonomous driving at the timing when the abnormality is detected in the remote assistance system4. The current position and a destination of the autonomous driving vehicle1are represented by reference signs “P1” and “DST”, respectively. A target route RT from the current position P1to the destination DST of the autonomous driving vehicle1is set by the autonomous driving system10. The autonomous driving system10controls the autonomous driving vehicle1such that it heads toward the destination DST along the target route RT.

At a time point at which the abnormality is detected in the remote assistance system4, specific positions PS exist on the target route RT to the destination DST. In the example illustrated inFIG.7, a plurality of specific positions PS1, PS2, PS3exists on the target route RT. The autonomous driving system10sets any specific position PS on the target route RT as a “limit position PL” (seeFIG.8). A section of the target route RT from the current position P1to the limit position PL is hereinafter referred to as a “retracting margin section XE” (seeFIG.8). The autonomous driving system10selects the target retracting position PE from the retracting margin section XE. In other words, the autonomous driving system10selects the target retracting position PE such that the target retracting position PE is included in the retracting margin section XE.

FIG.8is a conceptual diagram used for describing an example of the limit position PL and the retracting margin section XE. In the example illustrated inFIG.8, the limit position PL is a first specific position PS1closest to the current position P1on the target route RT. The retracting margin section XE is a section from the current position P1to the first specific position PS1. The target retracting position PE is selected from the retracting margin section XE. The target retracting position PE does not have to be around the current position P1and may be before the first specific position PS1(the limit position PL). Therefore, the target retracting position PE can be set such that the autonomous driving vehicle1can stop with a margin. In other words, it is possible to execute the retracting processing with a margin. Consequently, the safety of the autonomous driving vehicle1and vehicles in its vicinity can be improved.

Further, in the example illustrated inFIG.8, the autonomous driving vehicle1stops at the target retracting position PE without passing through any specific position PS. Therefore, no situation where the remote assistance is required occurs. As such, it is possible to preemptively avoid a situation where the remote assistance is required but the remote assistance cannot be received.

FIG.9is a conceptual diagram used for describing another example of the limit position PL and the retracting margin section XE. The limit position PL is not limited to only the first specific position PS1closest to the current position P1. For example, when the current position P1is immediately before the first specific position PS1, a second specific position PS2after the first specific position PS1may be set as the limit position PL. The retracting margin section XE is a section from the current position P1to the second specific position PS2. The target retracting position PE is selected from the retracting margin section XE. As such, it is not required to cause the autonomous driving vehicle1to rush to change lanes or suddenly decelerate in order to stop before the first specific position PS1. In other words, it is possible to execute the retracting processing with a margin.

In the example illustrated inFIG.9, the autonomous driving vehicle1is required to pass through the first specific position PS1. The first specific position PS1is a position at which the remote assistance may be required, but the remote assistance is not always required at the first specific position PS1. When the remote assistance is not required at the first specific position PS1, the autonomous driving vehicle1can pass through the first specific position PS1by normal autonomous driving. Even when the remote assistance is required at the first specific position PS1, in a case where the abnormality in the remote assistance system4is “performance degradation”, the remote assistance is possible, albeit slow. In this case as well, the autonomous driving vehicle1can pass through the first specific position PS1. Thereafter, the autonomous driving vehicle1stops at the target retracting position PE before passing through the second specific position PS2. As compared with a case where the autonomous driving vehicle1has to travel to the destination DST, the number of specific positions PS which the autonomous driving vehicle1passes through is reduced. Since the number of specific positions PS which the autonomous driving vehicle1passes through is reduced, a probability that the remote assistance is required is reduced as a whole. Therefore, an influence of the abnormality in the remote assistance system4is at least reduced.

2-4. Advantageous Effect

As described above, with the present embodiment, when an abnormality is detected in the remote assistance system4, the target retracting position PE is set in consideration of the specific position PS at which the remote assistance can be required. Specifically, any specific position PS on the target route RT to the destination DST is set as the limit position PL. Then, the target retracting position PE is set such that the target retracting position PE is included in the target route RT (the retracting margin section XE) from the current position P1of the autonomous driving vehicle1to the limit position PL.

The target retracting position PE does not have to be around the current position P1and may be before the limit position PL. Therefore, the target retracting position PE can be set such that the autonomous driving vehicle1can stop with a margin. In other words, it is possible to execute the retracting processing with a margin. Consequently, the safety of the autonomous driving vehicle1and vehicles in its vicinity can be improved.

Further, it is not required that the autonomous driving is ended around the current position P1, and the autonomous driving can be continued to the target retracting position PE. Consequently, the continuity of the autonomous driving can be improved.

Further, any specific position PS on the target route RT is set as the limit position PL. Thus, as compared with the case where the autonomous driving vehicle1has to travel to the destination DST, the number of specific positions PS which the autonomous driving vehicle1passes through is reduced. Since the number of specific positions PS which the autonomous driving vehicle1passes through is reduced, the probability that the remote assistance is required is reduced as a whole. Therefore, an influence caused by the abnormality in the remote assistance system4is at least reduced.

The first specific position PS1closest to the current position P1on the target route RT may be set as the limit position PL. In this case, the autonomous driving vehicle1stops at the target retracting position PE without passing through any specific position PS. Therefore, no situation where the remote assistance is required occurs. As such, it is possible to preemptively avoid a situation where the remote assistance is required but cannot be received.

Hereinafter, the autonomous driving system10according to the present embodiment will be described in more detail.

3. Example of Autonomous Driving System

3-1. Configuration Example

The autonomous driving system10controls the autonomous driving vehicle1. Typically, the autonomous driving system10is mounted on the autonomous driving vehicle1. Alternatively, at least a part of the autonomous driving system10may be arranged in an external device outside the autonomous driving vehicle1and remotely control the autonomous driving vehicle1. In other words, the autonomous driving system10may be arranged in the autonomous driving vehicle1and the external device in a distributed manner.

FIG.10is a block diagram illustrating a configuration example of the autonomous driving system10according to the present embodiment. The autonomous driving system10includes a sensor group20, a traveling device30, a communication device40, and a control device100.

The sensor group20is mounted on the autonomous driving vehicle1. The sensor group20includes a vehicle state sensor, the recognition sensor, a position sensor, and the like. The vehicle state sensor detects a state of the autonomous driving vehicle1. Examples of the vehicle state sensor include a vehicle speed sensor, a yaw rate sensor, a lateral acceleration sensor, and a steering angle sensor. The recognition sensor detects a situation in the vicinity of the autonomous driving vehicle1. Examples of the recognition sensor include a camera, a laser imaging detection and ranging (LIDAR), and radar. The position sensor detects the position and the azimuth of the autonomous driving vehicle1. Examples of the position sensor include a global positioning system (GPS) sensor.

The traveling device30is mounted on the autonomous driving vehicle1. The traveling device30includes a steering device, a drive device, and a braking device. The steering device steers wheels. For example, the steering device includes a power steering (electric power steering: EPS) device. The drive device is a power source that generates a drive force. Examples of the drive device include an engine, an electric motor, and an in-wheel motor. The braking device generates a braking force.

The communication device40communicates with the outside of the autonomous driving vehicle1. For example, the communication device40communicates with the remote assistance device2via the communication network3(seeFIGS.1and2). The communication device40may communicate with the management server. The communication device40may execute V2I communication (road-to-vehicle communication) with infrastructure in the vicinity. The communication device40may execute V2V communication (vehicle-to-vehicle communication) with surrounding vehicles. The communication device40includes a communication ECU, a communication module, a transmission/reception circuit, and the like.

The control device100controls the autonomous driving vehicle1. The control device100includes one or more processors110(hereinafter, simply referred to as the processor110) and one or more storage devices120(hereinafter, simply referred to as the storage device120). The processor110executes various processes. For example, the processor110includes a central processing unit (CPU). The storage device120stores various pieces of information. Examples of the storage device120include a volatile memory, a non-volatile memory, a hard disk drive (HDD), and a solid-state drive (SSD). The control device100may include one or more ECUs. A part of the control device100may be an information processing device outside the autonomous driving vehicle1.

An autonomous driving control program PROG is a computer program for controlling the autonomous driving vehicle1. When the processor110executes the autonomous operation control program PROG, various processes by the control device100are implemented. The autonomous operation control program PROG is stored in the storage device120. Alternatively, the autonomous driving control program PROG may be recorded on a computer-readable recording medium.

3-2. Driving Environment Information

The driving environment information200indicates a driving environment of the autonomous driving vehicle1. The driving environment information200is stored in the storage device120.

FIG.11is a block diagram illustrating an example of the driving environment information200. The driving environment information200includes map information210, specific position information220, retracting area information230, vehicle state information240, surrounding situation information250, vehicle position information260, and distribution information270.

The map information210includes a general navigation map. The map information210may indicate a lane arrangement, a road shape, and the like. The map information210may include position information, such as a signal, and a sign. The processor110acquires the map information of a required area from the map database. The map database may be stored in a predetermined storage device mounted on the autonomous driving vehicle1, or may be stored in an external management server. In the latter case, the processor110communicates with the management server and acquires required map information.

The specific position information220indicates a specific position PS at which the remote assistance for the autonomous driving vehicle1may be required. For example, the specific position information220is created in advance. The specific position information220may be included in the map information210. As described below, the specific position information220may be added in real time.

The retracting area information230indicates the positions of the stop candidate areas AC and the stop prohibited area AX (seeFIG.5). The retracting area information230may indicate priorities of the stop candidate areas AC (seeFIG.6). The retracting area information230is created in advance. The retracting area information230may be included in the map information210.

The vehicle state information240is information indicating the state of the autonomous driving vehicle1. The processor110acquires the vehicle state information240from the vehicle state sensor.

The surrounding situation information250is information indicating the situation in the vicinity of the autonomous driving vehicle1. The processor110acquires the surrounding situation information250using the recognition sensor. For example, the surrounding situation information250includes image information IMG captured by a camera. The surrounding situation information250further includes object information on an object in the vicinity of the autonomous driving vehicle1. Examples of the object include a pedestrian, a bicycle, other vehicles (a preceding vehicle, a parked vehicle, and the like), a road configuration (a white line, a curb, a guardrail, a wall, a median, a roadside structure, and the like), a sign, and an obstacle. The object information indicates a relative position and relative speed of the object with respect to the autonomous driving vehicle1.

The vehicle position information260is information indicating the position of the autonomous driving vehicle1. The processor110acquires the vehicle position information260from a detection result by the position sensor. Further, the processor110may acquire highly accurate vehicle position information260using a well-known self-position estimation process (localization) in which the object information and the map information210are used.

The distribution information270includes road traffic information, construction section information, traffic regulation information, and the like. The processor110receives the distribution information270from an information provision server or roadside infrastructure via the communication device40.

The processor110can grasp the construction section, the traffic congestion section, the accident occurrence position, and the like, based on the distribution information270. In this case, the processor110may add a construction section, a traffic congestion section, an accident occurrence position, and the like to the specific position information220.

3-3. Vehicle Traveling Control, Autonomous Driving Control

The processor110executes “vehicle traveling control” that controls the traveling of the autonomous driving vehicle1. The vehicle traveling control includes steering control, acceleration control, and deceleration control. The processor110executes the vehicle traveling control by controlling the traveling device30(the steering device, the drive device, and the braking device). Specifically, the processor110executes the steering control by controlling the steering device. Further, the processor110executes the acceleration control by controlling the drive device. Further, the processor110executes the deceleration control by controlling the braking device.

Further, the processor110executes the autonomous driving control based on the driving environment information200. In more detail, the processor110sets the target route RT to the destination DST based on the map information210and the like. Then, the processor110executes the vehicle traveling control such that the autonomous driving vehicle1heads toward the destination DST along the target route RT based on the driving environment information200.

In more detail, the processor110generates a traveling plan of the autonomous driving vehicle1based on the driving environment information200. Examples of the traveling plan include maintaining the current traveling lane, changing lanes, and avoiding an obstacle. Further, the processor110generates a target trajectory TR required for the autonomous driving vehicle1to travel according to the traveling plan. The target trajectory TR includes a target position and target speed. Then, the processor110executes the vehicle traveling control such that the autonomous driving vehicle1follows the target route RT and the target trajectory TR.

3-4. Processing Associated with Remote Assistance

During the autonomous driving, the processor110determines whether the remote assistance by the remote operator is required. Typically, a situation where the remote assistance by the remote operator is required is a situation where the autonomous driving is difficult. For example, when at least one of the above-described recognition processing, action determination processing, and timing determination processing is difficult, the processor110determines that the remote assistance by the remote operator is required.

Upon determining that the remote assistance is required, the processor110transmits the remote assistance request REQ to the remote assistance device2via the communication device40. The remote assistance request REQ requests the remote operator to execute the remote assistance to the autonomous driving vehicle1.

Further, the processor110transmits the vehicle information VCL to the remote assistance device2via the communication device40. The vehicle information VCL includes at least a part of the driving environment information200. For example, the vehicle information VCL includes the image information IMG captured by a camera. The vehicle information VCL may include the object information. The vehicle information VCL may include the vehicle state information240or the vehicle position information260. The vehicle information VCL may include results of the recognition processing, the action determination processing, and the timing determination processing.

Further, the processor110receives an operator instruction INS from the remote assistance device2via the communication device40. The operator instruction INS is an instruction to the autonomous driving vehicle1input by the remote operator. Upon receiving the operator instruction INS, the processor110executes the vehicle traveling control according to the received operator instruction INS.

4. Processing Flow at Time of Occurrence of Abnormality in Remote Assistance System

FIG.12is a flowchart illustrating a processing example by the autonomous driving system10according to the present embodiment. In particular,FIG.12illustrates a processing flow associated with a time of an occurrence of an abnormality in the remote assistance system4.

4-1. Step S100

In step S100, the processor110determines the presence/absence of an abnormality in the remote assistance system4that provides the remote assistance to the autonomous driving vehicle1. For example, the remote assistance system4includes the remote assistance device2, the communication network3, and a communication device40of the autonomous driving system10.

Abnormalities in the remote assistance system4include a “functional failure” in which a function of the remote assistance system4is lost. For example, the processor110monitors a communication situation (for example, a throughput or the communication speed) with the remote assistance device2. When the communication with the remote assistance device2is disrupted, the processor110determines that the functional failure has occurred in the remote assistance device2or the communication network3. As another example, the communication device40(for example, the communication ECU) of the autonomous driving system10has a self-diagnosis function. Using the self-diagnosis function, the processor110can detect a functional failure of the communication device40.

Abnormalities in the remote assistance system4may include “performance degradation” in which the function of the remote assistance system4is degraded. For example, the processor110monitors the communication situation (for example, a throughput, the communication speed, or the communication delay) with the remote assistance device2. When the throughput or the communication speed is lower than a first threshold value, the processor110determines that performance degradation has occurred in the remote assistance system4. As another example, when the communication delay exceeds a second threshold value, the processor110determines that performance degradation has occurred in the remote assistance system4.

When no abnormality is detected in the remote assistance system4(step S100: No), the process ends in this cycle. On the other hand, when an abnormality is detected in the remote assistance system4(step S100: Yes), the process proceeds to step S200.

4-2. Step S200

In step S200, the processor110determines whether the specific position PS exists on the target route RT from the current position P1to the destination DST. The target route RT is set and grasped by the processor110. The specific position PS is obtained from the specific position information220. Therefore, the processor110can determine whether the specific position PS exists on the target route RT based on the specific position information220.

When the specific position PS exists on the target route RT (step S200: Yes), the process proceeds to step S300. On the other hand, when no specific position PS exists on the target route RT (step S200: No), the process proceeds to step S400.

4-3. Step S300

In step S300, the processor110sets any specific position PS on the target route RT as the limit position PL based on the target route RT and the specific position information220. Thereafter, the process proceeds to step S500. Hereinafter, some examples of step S300will be described.

4-3-1. Example 1

FIG.13is a flowchart illustrating an example 1 of step S300.

In step S310, the processor110acquires the first specific position PS1closest to the current position P1on the target route RT. The current position P1is obtained from the vehicle position information260. The specific position PS is obtained from the specific position information220. The processor110can acquire the first specific position PS1based on the specific position information220and the vehicle position information260.

In step S340, the processor110sets the first specific position PS1as the limit position PL.

With the example 1, it is possible to stop the autonomous driving vehicle1without causing it to pass through any specific position PS.

4-3-2. Example 2

FIG.14is a flowchart illustrating an example 2 of step S300. Step S310is the same as that in the case of the example 1.

In step S330, the processor110determines whether the first specific position PS1satisfies a permitted condition. The permitted condition is set from the viewpoint of whether it is possible to stop the autonomous driving vehicle1without causing it to rush to change lanes or suddenly decelerate.

For example, the permitted condition includes at least one of the following conditions (A) and (B):

Condition (A): A distance between the current position P1and the first specific position PS1is equal to or greater than a predetermined distance threshold value.

Condition (B): A vehicle control amount that is required to stop the autonomous driving vehicle1at a position before the first specific position PS1is equal to or lower than a control amount threshold value. Here, examples of the vehicle control amount include deceleration and a steering amount.

The current position P1of the autonomous driving vehicle1is obtained from the vehicle position information260. The current speed of the autonomous driving vehicle1is obtained from the vehicle state information240. Kinetic performance of the autonomous driving vehicle1is given in advance as information. The processor110determines whether the first specific position PS1satisfies the permitted condition based on the current position P1, the first specific position PS1, the current vehicle speed, the kinetic performance, and the like.

When the first specific position PS1satisfies the permitted condition (step S330: Yes), the process proceeds to step S340. In step S340, the processor110sets the first specific position PS1as the limit position PL.

On the other hand, when the first specific position PS1does not satisfy the permitted condition (step S330: No), the process proceeds to step S350. In step S350, the processor110acquires a second specific position PS2different from the first specific position PS1on the target route RT based on the specific position information220. For example, the second specific position PS2is a specific position PS after the first specific position PS1when viewed from the current position P1(seeFIG.9). Thereafter, the process proceeds to step S360.

In step S360, the processor110sets the second specific position PS2as the limit position PL.

With the example 2, it is possible to stop the autonomous driving vehicle1without causing it to rush to change lanes or suddenly decelerate.

4-3-3. Example 3

FIG.15is a flowchart illustrating an example 3 of step S300. Step S310is the same as that in the case of the example 1.

In step S320, the processor110determines whether the abnormality in the remote assistance system4is a functional failure or performance degradation.

When the abnormality detected in the remote assistance system4is a functional failure (step S320: Yes), the process proceeds to step S340. In step S340, the processor110sets the first specific position PS1as the limit position PL.

On the other hand, when the abnormality in the remote assistance system4is performance degradation (step S320: No), the process proceeds to step S330. Step S330and steps thereafter are the same as those in the case of the example 2.

With the example 3, when the abnormality in the remote assistance system4is performance degradation, it is possible to expand a selection range of the target retracting position PE. In other words, when the abnormality in the remote assistance system4is performance degradation, it is possible to alleviate a condition imposed on the target retracting position PE.

4-4. Step S400

In step S400, the processor110sets the destination DST as the limit position PL. Thereafter, the process proceeds to step S500.

4-5. Step S500

In step S500, the processor110sets the target retracting position PE based on the limit position PL. Specifically, the processor110acquires a section of the target route RT from the current position P1to the limit position PL as the retracting margin section XE. Then, the processor110selects the target retracting position PE from the retracting margin section XE. In other words, the processor110sets the target retracting position PE such that the target retracting position PE is included in the retracting margin section XE.

The processor110sets the target retracting position PE such that the autonomous driving vehicle1can actually stop based on the current position P1, the vehicle speed, the kinetic performance, and the like of the autonomous driving vehicle1.

When setting the target retracting position PE, the processor110may further refer to the retracting area information230. The retracting area information230indicates the positions of the stop candidate areas AC and the stop prohibited area AX. The processor110sets the target retracting position PE in the stop candidate area AC that is included in the retracting margin section XE except for the stop prohibited area AX. The retracting area information230may indicate the priorities of the stop candidate areas AC. In that case, the processor110sets the target retracting position PE such that the target retracting position PE is included in the stop candidate area AC having a priority as high as possible.

4-6. Step S600

In step S600, the processor110executes the vehicle traveling control such that the autonomous driving vehicle1travels toward the target retracting position PE and stops at the target retracting position PE. For example, the processor110generates a target trajectory TR through which the autonomous driving vehicle1travels from the current position P1toward the target retracting position PE and stops at the target retracting position PE. Then, the autonomous driving system10executes the vehicle traveling control such that the autonomous driving vehicle1follows the target trajectory TR (seeFIG.4).