Vehicle control system, vehicle control method, and vehicle control program

Abstract

A vehicle control system according to the present disclosure comprises: an acquisition unit that acquires environment information indicating the surrounding environment of a vehicle; a drive control unit that controls autonomous driving of the vehicle on the basis of the environment information acquired by the acquisition unit; a detection unit that detects a manual driving vehicle traveling around the vehicle; and a restriction unit that, when the result of the detection by the detection unit indicates that a manual driving vehicle is traveling around the vehicle, restricts a traveling condition of the autonomous driving.

Description

Vehicle control system, vehicle control method, and vehicle control program

[0001] The present disclosure relates to a vehicle control system, a vehicle control method, and a vehicle control program.

[0002] With the advancement of recognition devices such as sensors and AI (Artificial Intelligence) technology, automation is being increasingly used in many aspects of daily life. For example, the technology for autonomous driving of automobiles is improving and is approaching human capabilities. Therefore, for example, a technology has been proposed that controls the driving of an autonomous vehicle so as to support the driving of a manually driven vehicle when a manually driven vehicle is detected.

[0003] Japanese Patent Application Laid-Open No. 2020-35155

[0004] However, autonomous driving presents various challenges. For example, traditionally, autonomous vehicles are expected to travel at legally permitted speeds based on the forward recognition capabilities of their onboard recognition devices. The performance target for recognition devices is at least equivalent to human recognition, ensuring safety on roads operated by ordinary drivers. Therefore, one approach to introducing autonomous driving to society is to limit the driving conditions to low speeds or to restricted driving environments when recognition capabilities are not perfect. Meanwhile, as the control performance of recognition devices and autonomous driving improves, it is becoming technically possible for autonomous vehicles to significantly surpass the environmental recognition capabilities of conventional humans. One example is the use of technologies such as infrared and millimeter-wave radar, which allows autonomous vehicles to recognize their driving environment at much greater distances than humans can, even in fog, bad weather, and darkness, enabling safe driving in conditions beyond the capabilities of a manual driver. As a result, if an autonomous vehicle were to drive while only taking into account conventional legal conditions, it could lead to an extremely dangerous situation in which surrounding manually driven vehicles would be drawn into similar high-speed driving.

[0005] Therefore, the present disclosure proposes a vehicle control system, a vehicle control method, and a vehicle control program that can prevent surrounding manually driven vehicles from getting into dangerous situations due to the effects of autonomous driving.

[0006] In order to solve the above problems, one embodiment of a vehicle control system according to the present disclosure includes an acquisition unit that acquires environmental information indicating the environment around the vehicle, a driving control unit that controls automatic driving of the vehicle based on the environmental information acquired by the acquisition unit, a detection unit that detects manually driven vehicles traveling around the vehicle, and a restriction unit that restricts the driving conditions for automatic driving when a manually driven vehicle is traveling around the vehicle as a result of detection by the detection unit.

[0007] 1 is a block diagram showing an example of a schematic configuration of a vehicle control system according to an embodiment. FIG. 2 is a flowchart showing an example of the flow of a vehicle control process according to an embodiment. FIG. 3 is a diagram explaining an example of driving control according to a situation according to an embodiment. FIG. 4 is a flowchart showing another example of the flow of a vehicle control process according to an embodiment. FIG. 5 is a diagram showing an example of a table constituting driving control data according to an embodiment. FIG. 6 is a diagram showing an example of a table constituting driving control data according to an embodiment. FIG. 7 is a diagram showing an example of a table constituting driving control data according to an embodiment. FIG. 8 is a flowchart showing another example of the flow of a vehicle control process according to an embodiment. FIG. 9 is a flowchart showing an example of the flow of a vehicle control process according to an embodiment. FIG. 10 is a diagram explaining an example of driving control according to a situation when catching up with a leading vehicle according to an embodiment. FIG. 11 is a flowchart showing an example of the flow of a vehicle control process according to an embodiment. FIG. 12 is a diagram showing an example of a configuration of an information processing system according to an embodiment. FIG. 13 is a block diagram showing an example of a schematic functional configuration of a vehicle control system to which the present technology can be applied. FIG. 14 is a diagram showing an example of a sensing area by a vehicle control system to which the present technology can be applied. FIG. 15 is a hardware configuration diagram showing an example of a computer that realizes the functions of a vehicle control system according to the present disclosure.

[0008] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the following embodiments, the same components are designated by the same reference numerals, and redundant description will be omitted.

[0009] The present disclosure will be described in the following order of items: 1. Introduction 1-1. Coexistence issues and best coexistence measures when machine performance exceeds human capability 1-2. Safety when manually driven vehicles and autonomously driven vehicles are mixed 1-3. Psychological characteristics of humans 2. Embodiments 2-1. Configuration of a vehicle control system according to an embodiment 2-2. Example of vehicle control processing according to an embodiment 2-3. Brake control according to an embodiment 2-4. Following driving and platooning according to an embodiment 2-5. Situation-specific driving control according to an embodiment 2-6. Example of vehicle control processing according to an embodiment 2-7. Another example of vehicle control processing according to an embodiment 2-8. Another example of vehicle control processing according to an embodiment 2-9. Another example of vehicle control processing according to an embodiment 2-10. Another example of vehicle control processing according to an embodiment 2-11. Example of configuration of an information processing system according to an embodiment 3. Other embodiments 3-1. Configuration of a moving body 4. Effects of a vehicle control system according to the present disclosure 5. Hardware configuration

[0010] (1. Introduction) First, the technical content that is the premise leading to the technology of the present disclosure will be described.

[0011] (1-1. Coexistence Issues and Optimal Coexistence Measures When Machine Performance Exceeds Human Capabilities) It is expected that the use of AI will increase in many aspects of daily life. At the same time, ethical issues have been raised about entrusting decisions to AI. Furthermore, issues have been raised about using AI to perform mechanical decision-making, which will allow processes to be performed faster than humans. For example, in financial transactions, if AI can process data faster than humans, it is expected that there will be many situations where human decisions will be outperformed by the mechanical decisions made by AI. Therefore, if the use of AI is unconditionally permitted in applications where it competes with humans, without imposing any caps on its use, it could cause inconvenience in social activities. For example, profits from financial transactions such as investments can only be earned through market fluctuations. If transactions are conducted with the aim of maximizing profits, society will be run on transactions that were not anticipated in traditional social activities, such as deliberately carrying out separate processes to encourage market fluctuations and then instantly detecting mistakes in other people's judgments and executing transactions under advantageous conditions, which will create an unnecessary and excessively competitive society.

[0012] When humans coexist in an operational system where all decisions are made by AI, if human judgments and actions are unable to keep up with the AI's decisions made by the machine, people may find themselves oppressed by the restrictive behavior of their daily lives, forced to act in accordance with the actions and decisions of the AI.

[0013] In other words, when people use AI in social activities, actions and decisions that prioritize AI efficiency will prevail. Social participants who do not assume collaboration with AI will be forced to live their lives competing with the AI's decisions, and a distorted society in which calm behavior is not tolerated will become the norm.

[0014] Of course, from various perspectives, there are ethical issues surrounding the use of AI, and the debate on the extent to which control and use that rely solely on practical processing will be acceptable in the future has only just begun. The use of AI may be regulated in the future. Many decisions will be made politically, and it is highly unlikely that laws reflecting public opinion in the name of competition will be able to regulate its use.

[0015] With the improved performance of recognition devices in autonomous driving, autonomous vehicles can utilize the performance of their recognition devices to travel at high speeds where a normal driver would slow down for safety reasons if they were driving manually based on visual observation. For example, the standard for introducing autonomous driving is set to a range where the autonomous vehicle's criteria for determining safe driving are within a range that reduces the number of accidents compared to manual driving by a conventional driver. Even if an autonomous vehicle exceeds the recognition ability of a manual driver of the surrounding environment, if it is operated solely based on this standard, there is a risk that the use of AI will be left unchecked even in autonomous driving, just like the general issues with AI mentioned above, and that this will cause many new problems.

[0016] If we limit ourselves to vehicles such as automobiles, they are means of transportation that, due to their weight and operational speed, carry the risk of harming others depending on how they are used. Fortunately, for this reason, many regulations have been put in place for vehicle operation approvals. It is anticipated that control of vehicles will shift to AI and other machines themselves, and the key issue will be how to introduce mechanisms for restricting their use and operation.

[0017] Due to their weight and the speed at which they are driven, cars pose a risk of becoming weapons capable of harming members of society. Therefore, compared to creative activities, profit maximization in finance, and semi-free use in the media, cars pose an extremely high risk of causing direct harm to others, and many social restrictions have been imposed by law on their use. The reason for this is that when new technology is introduced into society with an emphasis solely on technical performance, the risk of direct harm to other road users increases if used improperly. Furthermore, road traffic is an important element that forms the foundation of social activity. In order to ensure the flow of people and goods without accidents or congestion, various regulations, operational rules, and legal provisions have been established for road traffic.

[0018] To facilitate smooth social activities, it is essential to mitigate the risks associated with the use of these new technologies and ensure their balanced operation. Efforts to achieve this balance with social activities are being made in the form of restrictions on use in various situations. Most of these are based on legal and other regulations that people understand and abide by when using vehicles, thereby achieving a certain level of balance with social safety and deriving the benefits of using cars. For example, various restrictions and driving rules are set according to the situation, such as speed limits, restrictions on inadvertent sudden deceleration when slowing down on snowy roads, and restrictions on load weight and passenger numbers, and users are able to make appropriate decisions based on these restrictions.

[0019] Traditionally, these limits are adjusted based on a person's direct visual observation of the environment, balancing this with their own driving control abilities and the situations they encounter at different times during their journey, and are applied to driving in a variety of everyday environments.

[0020] No matter how much the autonomous driving performance of self-driving vehicles improves, gentle driving control that takes into consideration the surroundings is necessary to gain social acceptance. The driving control that is the standard in this case is not the sensing and control limits of the vehicle's onboard equipment, but the driving conditions and limits of the manual drivers around them who share the same space, the road, and in particular the average driving of an ordinary passenger car in the given environment. When visual visibility, which is the driver's primary means of environmental awareness, is reduced due to factors such as night, rain, or snowstorms, drivers of ordinary passenger cars will slow down to ensure safety. This can be considered a typical example of driver-initiated inhibitory control.

[0021] It is desirable for automated vehicle systems to mimic the characteristics of human drivers. To control an automated vehicle to avoid excessively high speeds while traveling on a road, it may be advisable to have it follow other vehicles for a while and analyze their characteristics. When an automated vehicle is traveling independently, direct driving control prioritizing technical performance will not immediately lead to manual drivers following at high speeds. Therefore, it is possible for the automated vehicle to catch up with a manually driven vehicle that is carefully controlling its own speed while continuing to maintain high-speed control. In such cases, it is desirable to gradually decelerate and maintain a sufficient distance between the vehicles in front, reducing speed to avoid creating a sense of pressure, and then, after understanding the driving control characteristics of the vehicle in front, take the lead and overtake without adversely affecting the following vehicle.

[0022] (1-2. Safety when manually driven vehicles and autonomous vehicles coexist) In the future, it is expected that roads will be home to a mixture of manually driven vehicles operated by a driver and autonomous vehicles that control their driving automatically. With technological advances in sensors and other recognition devices, autonomous vehicles will be equipped with advanced recognition devices that can monitor the situation over long distances. In a road environment where manually driven vehicles and autonomous vehicles coexist, if only autonomous vehicles equipped with advanced recognition devices are able to travel at high speeds due to their superiority, the safety of surrounding manually driven vehicles will be compromised.

[0023] When autonomous vehicles control their driving in autonomous driving mode, it is technically possible for them to increase the upper speed limit within a range deemed safe based on the performance of the recognition devices such as sensors and steering devices installed on the vehicle itself. However, if autonomous vehicles operating at high speeds exceeding human driving ability are mixed on public roads, they will pose a danger to surrounding vehicles. Therefore, especially when there are manually driven vehicles mixed in the surrounding vehicles, it is desirable for the vehicle to be equipped with a function that calculates the conditions under which a manual driver can drive safely and deliberately limits the driving conditions within that range.

[0024] The technology disclosed herein relates to a control technology that determines safer, slower, and more controlled driving conditions than a manually driven vehicle would maintain, based on the situation of surrounding vehicles, instead of uniquely determining driving conditions based on the performance of the installed technology as a prerequisite for vehicle control.

[0025] To achieve safer driving through autonomous driving, autonomous vehicles are expected to be equipped with recognition devices such as infrared sensors, millimeter-wave radar, and high-resolution image sensors that are far more capable of perceiving the driving environment than humans can visually perceive with visible light, and to have performance that far exceeds human environmental recognition capabilities.As a result, autonomous vehicles are expected to achieve faster and safer autonomous driving than manual drivers, even in conditions where maintaining safety would be difficult with manual driving, such as heavy rain, fog, blizzards, and darkness, or where driving speed would need to be drastically reduced.

[0026] However, if an autonomous vehicle continues to drive at high speeds, there is a risk that surrounding manual drivers will be influenced to drive at unreasonable speeds, compromising the safety of the road environment as a whole. Therefore, autonomous vehicles require control technology that suppresses vehicle control from a different perspective than conventional technology.

[0027] (1-3. Human psychological characteristics) As humans have evolved, they have an instinctive psychology that basically leads them to live in collective societies, and so they unconsciously make decisions that lean towards collective behavior. This basic behavioral psychology is combined with judgments based on each individual's situation, leading to everyday behavioral decisions.

[0028] Whether an individual's ethical judgment or situational judgment allows them to distance themselves from this herd mentality and make a different decision, or whether they conform to the group, often depends on the situation. In either case, human behavior is often influenced by the surrounding circumstances. Therefore, if an autonomous vehicle drives differently from a regular manually driven vehicle, there is a real risk that it will affect other drivers, and countermeasures are necessary.

[0029] The technology disclosed herein relates to control that should be installed in autonomous vehicles, which is based on this fundamental human psychology and is required to prevent ordinary manual drivers from causing unsafe vehicle control.

[0030] However, just because people naturally have a tendency to behave in groups and conform, it does not necessarily mean that everyone will behave in accordance with those around them. People often take actions that they do not want to take because the risks of conforming are obvious. It is not always the case that a general manual driver will begin following the vehicle in all control situations. Therefore, the technology disclosed herein may set a judgment threshold to determine the degree of impact associated with following driving, and then determine whether to consider the conformity risk.

[0031] (2. Embodiment) (2-1. Configuration of Vehicle Control System According to Embodiment) Next, an example of a vehicle control system to which the technology of the present disclosure is applied will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of a schematic configuration of a vehicle control system 100 according to an embodiment. The vehicle control system 100 is a system that controls a vehicle 200.

[0032] The vehicle control system 100 includes a communication unit 110, a storage unit 120, a control unit 130, a detection unit 140, and a display unit 145. Note that the configuration shown in Fig. 1 is a functional configuration and may differ from the hardware configuration. Furthermore, the functions of the vehicle control system 100 may be distributed and implemented in multiple physically separated devices.

[0033] The communication unit 110 is realized by, for example, a network interface controller or a network interface card (NIC). The communication unit 110 may be a universal serial bus (USB) interface configured by a USB host controller, a USB port, etc. The communication unit 110 may also be a wired interface or a wireless interface. For example, the communication unit 110 may be a wireless communication interface using a wireless LAN system or a cellular communication system. The communication unit 110 functions as a communication means or a transmission means of the vehicle control system 100. For example, the communication unit 110 is connected to a network N by wire or wirelessly and transmits and receives information to and from other information processing terminals, etc. via the network N.

[0034] The storage unit 120 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 stores various data. For example, the storage unit 120 stores various programs including a vehicle control program. The storage unit 120 may store a learning device (image recognition model) that has learned to detect various vehicles, a learning device (autonomous driving model) that has learned the driving operation of the vehicle 200, data related to detected objects, etc. The storage unit 120 may also store map data for executing autonomous driving, etc. The map data stores road information such as legal speed limits for roads in various locations. The storage unit 120 also includes driving control data 121 for executing autonomous driving.

[0035] The driving control data 121 is data used to control the automatic driving. The driving control data 121 will be described in detail later.

[0036] The detection unit 140 detects various types of information related to the vehicle 200. Specifically, the detection unit 140 detects the environment around the vehicle 200, location information of the vehicle 200, information related to devices connected to the vehicle 200, etc. The detection unit 140 may be interpreted as a recognition device such as a sensor that detects various types of information.

[0037] For example, the detection unit 140 is a sensor, or a so-called camera, that has a function of capturing an image of the surroundings of the vehicle 200. For example, the detection unit 140 is realized by a ToF camera, a stereo camera, a monocular camera, a lensless camera, or the like.

[0038] The detection unit 140 may also include a sensor for measuring the distance to an object inside the vehicle 200 or around the vehicle 200. For example, the detection unit 140 may be a LiDAR (Light Detection and Ranging) system that reads the three-dimensional structure of the environment surrounding the vehicle 200. LiDAR detects the distance to an object and its relative speed by irradiating a laser beam, such as an infrared laser, onto the surrounding object and measuring the time it takes for the beam to reflect and return. The detection unit 140 may also be a ranging system using millimeter-wave radar. The detection unit 140 may also include a depth sensor for acquiring depth data.

[0039] The detection unit 140 also includes a sensor for measuring driving information of the vehicle 200 and the behavior of the vehicle 200. For example, the detection unit 140 detects the behavior of the vehicle 200. For example, the detection unit 140 is an acceleration sensor that detects the acceleration of the vehicle, a gyro sensor that detects the behavior, an IMU (Inertial Measurement Unit), or the like.

[0040] In addition, the detection unit 140 may include a microphone that collects sounds around the vehicle 200, an illuminance sensor that detects the illuminance around the vehicle 200, a humidity sensor that detects the humidity around the vehicle 200, a geomagnetic sensor that detects the magnetic field at the location of the vehicle 200, etc.

[0041] The display unit 145 is a mechanism for outputting various types of information. The display unit 145 is, for example, a liquid crystal display. For example, the display unit 145 displays risk information to be provided to the user. The display unit 145 may also serve as a processing unit for accepting various operations from a user or the like who uses the vehicle 200. For example, the display unit 145 may accept input of various types of information via key operations, a touch panel, or the like.

[0042] The control unit 130 is realized, for example, by a central processing unit (CPU), a micro processing unit (MPU), or the like executing a program (for example, a vehicle control program according to the present disclosure) stored in the storage unit 120 using a random access memory (RAM) or the like as a work area. The control unit 130 is a controller, and may be realized, for example, by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0043] The control unit 130 has an acquisition unit 131, an operation control unit 132, a detection unit 133, and a restriction unit 134, and realizes or executes the functions and actions of information processing described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in Fig. 1, and may be any other configuration as long as it performs the information processing described below.

[0044] The acquisition unit 131 acquires environmental information indicating the environment around the vehicle 200. Specifically, the acquisition unit 131 acquires, as environmental information, the environment around the vehicle 200 and location information of the location of the vehicle 200 via the detection unit 140 or the like. For example, the acquisition unit 131 acquires image data of the area around the vehicle 200 and distance information to surrounding objects. The acquisition unit 131 also acquires driving information of the vehicle 200 and the behavior of the vehicle 200. The acquisition unit 131 also acquires the illuminance and humidity around the vehicle 200. The acquisition unit 131 also acquires road information such as the legal speed limit of the road on which the vehicle 200 is located from map data. Note that the acquisition unit 131 may perform image recognition on an image of the area around the vehicle 200, identify road signs appearing in the image, and acquire road information such as the legal speed limit.

[0045] The acquisition unit 131 may acquire environmental information from other vehicles by performing vehicle-to-vehicle communication with the other vehicles via the communication unit 110. The vehicle-to-vehicle communication may be V2V (Virtual to Virtual) communication or V2X (Vehicle to X) communication that directly communicates with the other vehicles, or V2N (Vehicle to Network) communication that communicates with the other vehicles via a network such as a mobile phone network.

[0046] The driving control unit 132 controls the autonomous driving of the vehicle 200 based on the environmental information acquired by the acquisition unit 131. For example, the driving control unit 132 determines driving conditions for autonomous driving based on the environmental information acquired by the acquisition unit 131. For example, the driving control unit 132 controls each part of the vehicle 200 based on the determined driving conditions for autonomous driving, and controls the autonomous driving of the vehicle 200. For example, the driving control unit 132 determines operation information for driving and operating the vehicle 200 using an autonomous driving model based on the environmental information and the driving conditions for autonomous driving, and controls each part of the vehicle 200 based on the determined operation information to realize the autonomous driving of the vehicle 200. Note that the driving control unit 132 may use any method as long as it can realize the autonomous driving of the vehicle 200.

[0047] The detection unit 133 detects manually driven vehicles traveling around the vehicle 200. Specifically, the detection unit 133 detects manually driven vehicles traveling around the vehicle 200 based on the environmental information acquired by the acquisition unit 131. For example, the detection unit 133 detects vehicles traveling around the vehicle 200 from the image data acquired by the acquisition unit 131, and detects the manually driven vehicles traveling around. The vehicle is configured to be able to identify from the outside whether it is in an autonomous driving state, in order to distinguish violations such as inattentive driving by the driver of a manually driven vehicle from acts that are permitted during autonomous driving, and to enable enforcement. For example, the detection unit 133 detects vehicles other than those identified as being in an autonomous driving state, among the vehicles traveling around the vehicle 200, as manually driven vehicles.

[0048] The detection unit 133 may perform vehicle-to-vehicle communication with vehicles traveling around the vehicle 200 via the communication unit 110 to identify whether the vehicle is in autonomous driving or not, and may detect vehicles other than those identified as being in autonomous driving as manually driven vehicles. The detection unit 133 may use any method as long as it can detect manually driven vehicles traveling around the vehicle 200.

[0049] Here, the performance of recognition devices such as the detection unit 140 and the control performance of the automatic driving by the driving control unit 132 have improved, and the vehicle 200 can be driven safely under conditions that exceed the capabilities of a manual driver, even when normal visual manual driving requires slowing down for safety. For example, the vehicle 200 can recognize the driving environment far farther than a human can, even in fog, bad weather, or darkness, and can be driven safely under conditions that exceed the capabilities of a manual driver.

[0050] However, in a road environment where both manually and autonomous vehicles coexist, if autonomous vehicles take advantage of their superiority and drive at high speeds, the safety of surrounding manually driven vehicles will be compromised.

[0051] Therefore, when the result of detection by the detection unit 133 indicates that a manually driven vehicle is traveling around the vehicle 200, the restriction unit 134 restricts the driving conditions for autonomous driving. Specifically, when a manually driven vehicle is traveling around the vehicle 200, the restriction unit 134 derives driving conditions under which the manually driven vehicle can travel safely, based on the environmental information acquired by the acquisition unit 131. The restriction unit 134 restricts the driving conditions for autonomous driving to driving conditions that are slower and more safely than the manually driven vehicle would maintain, based on the status of the manually driven vehicles around the vehicle 200. The driving conditions for autonomous driving that are to be restricted may be any conditions that increase safety. For example, the restriction unit 134 may restrict the speed to a low level or restrict the driving conditions to increase the vehicle distance from surrounding vehicles.

[0052] For example, when a manually driven vehicle is traveling around vehicle 200, the limiting unit 134 determines the weather from the environmental information acquired by the acquisition unit 131 and derives driving conditions under which the manually driven vehicle can travel safely based on the determined weather. For example, the limiting unit 134 derives an upper limit of the driving conditions under which the manually driven vehicle can travel safely, and limits the driving conditions for automated driving to within the derived upper limit. For example, the limiting unit 134 derives a safe speed at which the manually driven vehicle can travel safely in fog, bad weather, or darkness. The limiting unit 134 derives a slower safe speed in situations requiring more careful driving, such as fog, bad weather, or darkness. The limiting unit 134 limits the driving speed for automated driving to within the derived safe speed. This allows vehicle 200 to prevent surrounding manually driven vehicles from traveling at similar high speeds due to being lured by vehicle 200 in fog, bad weather, or darkness.

[0053] Note that the range in which an autonomous vehicle can recognize and make judgments about its surroundings is often limited to certain vehicles that are very close, and the range may be expanded to include the range of vehicle-to-vehicle communication that directly communicates with other vehicles, such as V2V or V2X. For example, when a manually driven vehicle is traveling within the range of vehicle-to-vehicle communication that directly communicates with vehicle 200, restriction unit 134 may restrict the autonomous driving conditions.

[0054] Furthermore, the targets of the synchronization risk determination may be limited to vehicles traveling around the automatically driven vehicle, such as adjacent vehicles in front, behind, to the left, right, etc. For example, the restriction unit 134 may restrict the driving conditions for the automatically driven vehicle when manually driven vehicles are traveling in front, behind, to the left, or to the right of the vehicle 200.

[0055] Note that just because the manually driven drivers of surrounding vehicles have a psychological synchronization issue does not necessarily mean that the vehicle's driving conditions must be reduced to the driving conditions that a manually driven driver driving the corresponding section can adopt. For example, just because a visually impaired pedestrian is nearby does not mean that the vehicle's walking style needs to be adjusted to match that of the pedestrian. Rather, the vehicle may assist the pedestrian by holding a hand and providing advice at key points that require caution. It is also possible for the vehicle to pass by at a walking speed similar to that of a physically impaired person by extending a hand for assistance. For example, the limiting unit 134 may evaluate the behavior of the manually driven vehicle and relax the restrictions on the driving conditions within a range in which the behavior of the manually driven vehicle is stable. For example, the limiting unit 134 may increase the safe speed within a range in which the behavior of the manually driven vehicle is stable.

[0056] (2-2. Example of Vehicle Control Processing According to Embodiment) An example of the flow of the vehicle control processing according to the embodiment will be described using Fig. 2. Fig. 2 is a flowchart showing an example of the flow of the vehicle control processing according to the embodiment. The vehicle control processing shown in Fig. 2 is executed when autonomous driving is started.

[0057] The acquisition unit 131 acquires environmental information indicating the environment around the vehicle 200 (step S10). Specifically, the acquisition unit 131 acquires, as environmental information, the environment around the vehicle 200 and location information of the location of the vehicle 200 via the detection unit 140, etc. For example, the acquisition unit 131 acquires image data of the area around the vehicle 200 and distance information to surrounding objects. The acquisition unit 131 also acquires driving information and behavior of the vehicle 200. The acquisition unit 131 also acquires illuminance and humidity around the vehicle 200. The acquisition unit 131 also acquires road information such as the legal speed limit of the road on which the vehicle 200 is located.

[0058] The driving control unit 132 identifies driving conditions for autonomous driving based on the environmental information acquired by the acquisition unit 131 (step S11). For example, the driving control unit 132 identifies the legal speed limit of the road on which the vehicle 200 is located from road information. The driving control unit 132 also identifies a recognition range around the vehicle 200 from the environmental information. For example, the driving control unit 132 identifies a recognition range around the vehicle 200 recognized by the camera, LiDAR, millimeter-wave radar, and depth sensor constituting the detection unit 140 based on the environmental information. The driving control unit 132 identifies driving conditions for autonomous driving based on the identified recognition range. The driving control unit 132 identifies driving conditions for autonomous driving by determining that the wider the recognition range is within a range below the legal speed limit, the higher the speed at which autonomous driving is possible. Note that the driving conditions for autonomous driving may be determined corresponding to the road.

[0059] The limiting unit 134 derives driving conditions under which the manually-driven vehicle can travel safely, based on the environmental information acquired by the acquiring unit 131 (step S12). For example, when manually-driven vehicles are traveling around vehicle 200, the limiting unit 134 determines the weather from the environmental information acquired by the acquiring unit 131, and derives driving conditions under which the manually-driven vehicle can travel safely, based on the determined weather. For example, the limiting unit 134 derives a safe speed under which the manually-driven vehicle can remain safe in the event of fog, bad weather, or darkness. More specifically, vehicle 200 predicts the paths of surrounding static and dynamic objects that have been recognized and detected based on conditions, including road characteristics such as road surface freezing and weather-related driving conditions, based on local road map information acquired in advance, and estimates the range of risk of conflict from the range of predicted paths that vehicle 200 can take if no intentional collision avoidance operations are performed.Taking into account the predicted avoidance actions of competitors, vehicle 200 performs control planning based on a control plan that ensures a sufficient safe distance margin exceeding a specified value so that even if vehicle 200 proceeds on the predicted path, it will not collide with other recognized road space competitors due to path conflict, and also takes into account condition judgments to prevent the vehicle from slipping due to road surface conditions, etc., and performs vehicle control planning.

[0060] The detection unit 133 detects manually driven vehicles traveling around the vehicle 200 (step S13). The restriction unit 134 determines whether manually driven vehicles traveling around the vehicle 200 have been detected (step S14).

[0061] If a manually driven vehicle is not detected (step S14: No), the driving control unit 132 controls each part of the vehicle 200 based on the determined driving conditions for automatic driving, controls the automatic driving of the vehicle 200 (step S15), and proceeds to step S19 described below.

[0062] If a manually driven vehicle is detected (step S14: Yes), the restriction unit 134 determines whether a restriction on autonomous driving is necessary (step S16). For example, the restriction unit 134 determines whether the driving conditions for autonomous driving include conditions that exceed the driving conditions under which a manually driven vehicle can safely drive. For example, the restriction unit 134 determines whether the speed of the driving conditions for autonomous driving exceeds a safe speed. If such conditions are exceeded, the restriction unit 134 determines that a restriction on autonomous driving is necessary.

[0063] The limiting unit 134 determines whether or not the autonomous driving needs to be restricted based on the result of step S16 (step S17). If the autonomous driving does not need to be restricted (step S17: No), the process proceeds to step S15 described above.

[0064] If it is necessary to restrict the autonomous driving (step S17: Yes), the restriction unit 134 restricts the driving conditions for the autonomous driving (step S18). For example, the restriction unit 134 restricts the upper speed limit for the autonomous driving to the derived safe speed. The driving control unit 132 controls each part of the vehicle 200 based on the restricted driving conditions for the autonomous driving, and controls the autonomous driving of the vehicle 200.

[0065] The driving control unit 132 determines whether to end the autonomous driving (step S19). For example, if an instruction to end the autonomous driving is given, such as an instruction to end the autonomous driving (step S19: Yes), the driving control unit 132 determines that the autonomous driving has ended and ends the processing. On the other hand, if an instruction to end the autonomous driving has not been given (step S19: No), the processing proceeds to step S10 described above.

[0066] As a result, the vehicle control system 100 according to the embodiment can prevent surrounding manually driven vehicles from being placed in a dangerous situation due to the influence of automatic driving. In particular, it is expected to be effective in preventing unsafe high-speed driving beyond the manual driving ability of surrounding manually driven drivers, which may occur due to an unconscious driving psychology that synchronizes with surrounding vehicles.

[0067] (2-3. Brake control according to the embodiment) By utilizing the advanced environmental recognition capabilities of autonomous driving to drive while sharing data with surrounding vehicles about situations in which it is possible to drive safely at high speeds, there are cases in which the surrounding vehicles will be able to drive safely and smoothly at higher speeds than in cases in which no information is available.

[0068] The driving characteristics of surrounding vehicles affect how they maintain a safe distance from the vehicle. Therefore, autonomous vehicles need to provide safer control depending on how the following vehicle maintains a safe distance from the vehicle. In other words, when a following vehicle is driving too close in poor forward visibility, the autonomous vehicle may need to brake more suddenly than planned, resulting in a collision risk due to the following vehicle's insufficient braking. In such cases, the autonomous vehicle may intentionally apply sudden braking even if the vehicle is able to maintain a safe distance from the leading vehicle, thereby warning the following vehicle of the risk of a rear-end collision if the following vehicle does not maintain a safe distance. The autonomous vehicle may also change its driving conditions, such as by increasing the distance from the leading vehicle to allow for gentler braking. Furthermore, if the sudden braking applied to warn the driver creates a high risk of a rear-end collision, the autonomous vehicle may ease the sudden braking to the extent possible to avoid a rear-end collision with the leading vehicle. To achieve this, the host vehicle must first maintain a large inter-vehicle distance from the vehicle in front. For example, when the inter-vehicle distance between vehicle 200 and a following vehicle following vehicle 200 is shorter than a predetermined allowable distance, the driving control unit 132 may implement sudden braking control within a range that avoids a rear-end collision in order to alert the driver. The driving control unit 132 sets the allowable distance to a distance at which the following vehicle can stop by normal braking, depending on the speed of the following vehicle. The driving control unit 132 sets a longer allowable distance as the speed of the following vehicle increases. Furthermore, when the inter-vehicle distance between vehicle 200 and a following vehicle following vehicle 200 is shorter than a predetermined allowable distance, the driving control unit 132 may change the driving conditions of the autonomous driving so that the following vehicle brakes more gently. For example, the driving control unit 132 may change the driving conditions of the autonomous driving so that the following vehicle brakes earlier. For example, in the case where the driving control unit 132 starts braking when the inter-vehicle distance to the vehicle ahead is within the start distance during automatic driving, the driving control unit 132 changes the start distance at which braking starts to be started to be started to be increased. Furthermore, after suddenly applying brakes to alert the driver, the driving control unit 132 may ease the brake control to a range that avoids a rear-end collision with the vehicle ahead, etc.

[0069] (2-4. Following driving and platooning according to embodiments) Humans are born with a natural tendency for collective behavior and social behavior psychology. As a result, drivers drive their vehicles so as to follow the flow of surrounding vehicles within a range that they feel is safe. This is because, in many cases, following the steady flow of surrounding vehicles prevents disruptions to surrounding traffic, resulting in safe driving.

[0070] On the other hand, from a purely technical perspective, autonomous vehicles are expected to be equipped with many advanced recognition devices. As long as they can safely control their driving while strictly adhering to traffic speed limits, autonomous vehicles are not currently required to restrict their driving control. By equipping autonomous vehicles with advanced and superior recognition devices, they will be able to more clearly see the road environment in the distance, enabling safer driving at higher speeds compared to conventional manual drivers who rely on visual information. Furthermore, when a vehicle is safely driving autonomously on a public road, nearby manually driven vehicles may be tempted to follow, leading ordinary drivers to follow at higher speeds than if they were driving alone. In particular, unless manual drivers consciously suppress their speeds, there is a high risk of unconsciously engaging in high-speed driving.

[0071] Following behind a vehicle in front can occur to the extent that drivers instinctively feel it is safe to follow the vehicle in front. However, following behind a vehicle in front does not necessarily guarantee safety, and there is no guarantee that these conditions may be lost at any time. If this happens, the safe driving conditions will be lost while driving, and the driver of the following vehicle will find themselves in a situation where they are required to respond in a way that exceeds their own control capabilities. If they are unable to respond quickly in this situation, they may, in the worst case scenario, cause an accident due to a hasty response.

[0072] To prevent such a situation from occurring, even autonomous vehicles need to conservatively restrict their driving conditions. Thanks to improvements in recognition devices and autonomous driving control performance, autonomous vehicles may, in principle, be able to safely control their driving independently even in conditions far exceeding human environmental recognition and control capabilities. However, if autonomous vehicles determine their driving conditions based solely on their own performance, surrounding manually driven vehicles may be induced to drive at high speeds, potentially resulting in accidents. Therefore, autonomous vehicles may calculate upper limits that allow drivers of surrounding manually driven vehicles to safely control their vehicles and moderate the autonomous driving driving conditions. For example, the limiting unit 134 may derive upper limits for driving conditions under which surrounding manually driven vehicles can safely drive, and limit the autonomous driving driving conditions to within the derived upper limits.

[0073] In addition, there may be cases where a driver attempts to follow a vehicle without having the ability to do so. Therefore, as an additional safety function, a simpler evaluation of the following vehicle's stable following driving may be performed.

[0074] When a manually driven following vehicle attempts to follow the vehicle, the autonomously driven vehicle may evaluate the stability of the driving control of the following vehicle, and if it is determined that the following vehicle is clearly driving at an unreasonable high speed, causing a lack of steering stability, or that the steering control is becoming rough and deviating from normal steering, causing instability, the autonomously driven vehicle may further restrict the driving of the host vehicle. For example, the restriction unit 134 may evaluate the driving stability of the manually driven vehicle following the vehicle 200 based on the environmental information acquired by the acquisition unit 131, and may restrict the driving conditions for autonomous driving if the stability is low. For example, the restriction unit 134 may evaluate the driving of the following vehicle, and may restrict the driving conditions for autonomous driving if the steering stability is lacking or the following vehicle deviates from its lane.

[0075] Drivers can also be broadly divided into those who are conservative and those who prefer aggressive, aggressive driving.

[0076] Many drivers prioritize safety without overconfidence in their driving ability. For example, when encountering crosswinds or reduced visibility, many drivers become cautious to avoid an accident, slowing down and increasing the distance between their vehicle and the vehicle in front to create a safer situation, and instead of continuing to drive at high speeds, they try to drive at a moderate speed.

[0077] On the other hand, there are drivers who are overconfident or who do not hesitate to engage in reckless and adventurous driving because they are in a hurry. There are also drivers who, while not necessarily aggressive, will take risks, such as competing with nearby vehicles traveling at high speeds, or driving close to autonomous vehicles even in environments with slightly reduced visibility. Among these drivers, there are those who, in an attempt to take advantage of the safety offered by autonomous vehicles at high speeds, will act like remoras and follow autonomous vehicles as a lead vehicle, and then continue driving at high speeds.

[0078] Many drivers aim to travel within a set time, and often do not allow more time than normal or necessary. If surrounding vehicles are traveling at a higher speed, drivers psychologically feel that they can travel safely by driving in sync with the surrounding vehicles. Therefore, when drivers drive in sync with surrounding vehicles, they tend to speed up and drive at a higher speed, even if they would not do so if they were driving alone.

[0079] It is not possible to regulate the driving psychology of ordinary drivers or to give instructions from self-driving vehicles traveling on the road. Even if a self-driving vehicle is equipped with communication devices such as V2V, it would be strange for it to give instructions.

[0080] Therefore, when there are ordinary manually driven vehicles driving around, the autonomous vehicle will need to limit driving conditions and drive conservatively so that such conformity psychology or competitive psychology does not affect the driver's psychology of the manually driven vehicle and induce reckless driving.

[0081] So, is it always necessary to drive conservatively? There are exceptions. That is when conditions are right for multiple vehicles to travel in a convoy, like a family of spot-billed ducks, by electronically pairing and establishing an electronic connection with surrounding vehicles through vehicle-to-vehicle communication.

[0082] Unless features like electronically coupled platooning are enabled, manually driven vehicles may lose their ability to continue driving reliably immediately after a sudden switch to manual driving, such as by breaking up the following behavior. As a result, driving at higher speeds is not recommended.

[0083] Electronically linked platooning is possible when an autonomous vehicle is paired with a following vehicle that follows behind it and drives in lead vehicle mode. Even manually driven vehicles that drive in platoon with the vehicle in front and are equipped with the function of semi-automatic following behind the lead vehicle while being protected by the lead vehicle should be able to follow the following vehicle without any problems, as long as safety is ensured, even if the leading autonomous vehicle drives more aggressively at higher speeds than when driven manually, as long as the vehicle has the ability to follow the following vehicle.

[0084] Therefore, in platooning, after confirming the stability of inter-vehicle communications, which is an essential condition for safe platooning, a handshake is used to confirm whether the following vehicle wishes to select a follow mode in which it follows the leading vehicle, and after this confirmation handshake, pairing is performed and then the manually driven vehicle switches to follow driving. Platooning with an autonomous vehicle as the leading vehicle does not put the following vehicle at risk even when traveling at speeds faster than normal manual driving, so it may be implemented as one of the usage modes.

[0085] (2-5. Situation-specific driving control according to the embodiment) By utilizing the onboard sensors, an autonomous vehicle equipped with high-performance sensors can travel faster than a human manually driven vehicle even in situations where a human driver cannot clearly see the road ahead, such as darkness, poor visibility in fog, or heavy rain. On the other hand, an autonomous vehicle does not necessarily have to drive as if a manual driver were driving when there are other vehicles around.

[0086] What is important is to ensure that surrounding vehicles are not induced to inappropriately try to take advantage of the performance of the self-driving vehicle by following it unreasonably, provided that there is no risk of this and that certain safety guarantees can be confirmed in advance.

[0087] In other words, in a vehicle equipped with advanced sensing and autonomous driving functions, driving control may be performed according to the situation. An autonomous vehicle may perform driving control according to the situation. Examples of driving control according to the situation are shown below in 1 to 4.

[0088] 1. If there are no other vehicles in the driving environment, the vehicle can drive independently with autonomous conditions set. 2. If there are vehicles nearby, the vehicle is equipped with equipment capable of platooning and is equipped with an autonomous driving function, and only if the vehicle is capable of platooning and safely releasing from platooning, the lead vehicle (the vehicle in front) will set an upper limit for high-speed driving in accordance with the capabilities of its onboard equipment, within the range of the upper speed limit set as deemed safe even if the following vehicle is released mid-way. 3. If a nearby manually driven vehicle attempts to approach, and if the approaching vehicle does not request platooning, and if the vehicle is confirmed to be driving in synchronization with the vehicle's own vehicle, the degree of synchronization will be evaluated based on the timing of the vehicle's acceleration and deceleration in relation to the vehicle's acceleration and deceleration, and if the vehicle is confirmed to be following the vehicle's own vehicle, the vehicle will suppress aggressive driving conditions that are possible with its advanced recognition capabilities, and will drive at a speed that is lowered to the average speed level of a manually driven driver under the applicable conditions. 4. The system evaluates the behavior of the manually driven following vehicle, such as distance between vehicles and lane keeping, and evaluates the driver's ability to respond to the situation.If the conditions are met such that safety can be ensured even if the vehicle is being followed, the vehicle will be driven at a speed higher than that of simple manual driving conditions.

[0089] The above 1 to 4 are merely examples, and driving control is not limited to these depending on the situation.

[0090] For example, when there are no other vehicles in the vicinity, the driving control unit 132 controls the autonomous driving of the vehicle 200 under the upper limit driving conditions allowed in the road environment on which the vehicle is traveling, based on the environmental information acquired by the acquisition unit 131. For example, the driving control unit 132 controls the autonomous driving of the vehicle 200 at the upper limit speed allowed depending on the weather, within the speed limit of the road on which the vehicle is traveling.

[0091] Furthermore, when another vehicle is present in the vicinity and platooning with the vehicle is possible, the driving control unit 132 pairs with the vehicle to establish an electronic connection and controls the autonomous driving of the vehicle 200 to platoon with the vehicle. The restriction unit 134 evaluates the risk of pairing disconnection and limits the autonomous driving conditions according to the risk. For example, the restriction unit 134 limits the autonomous driving speed lower the higher the risk of pairing disconnection. Note that if the risk of disconnection is high, the driving control unit 132 may not perform platooning because there is a risk that a mid-transit release will occur and the following vehicle may suddenly switch to manual driving, which is dangerous. For example, the driving control unit 132 may not perform platooning if the risk of disconnection is above a tolerance value.

[0092] Furthermore, when another vehicle is present around vehicle 200 and is confirmed to be following the other vehicle during autonomous driving by the driving control unit 132, the restriction unit 134 may derive upper limits of driving conditions under which the other vehicle can safely drive and restrict the driving conditions for autonomous driving to within the derived upper limits. For example, when another vehicle is present around vehicle 200, the restriction unit 134 evaluates the degree of synchronization of the acceleration / deceleration timing of the other vehicle with that of vehicle 200, and determines that the other vehicle is following vehicle 200 if the degree of synchronization is equal to or greater than a predetermined value. The restriction unit 134 may evaluate the driving stability of the other vehicle around vehicle 200 based on the environmental information acquired by the acquisition unit 131, and restrict the driving conditions for autonomous driving if the stability is low. For example, the restriction unit 134 may evaluate the driving of the other vehicle around vehicle 200 and restrict the driving conditions for autonomous driving if the steering stability is poor or if the vehicle deviates from its lane. Furthermore, if the result of evaluating the stability is that the conditions are met that can be considered to ensure safety, the limiting unit 134 may set the driving conditions for automatic driving to conditions that are faster than the driving conditions for manual driving.

[0093] (2-6. Example of Vehicle Control Processing According to Embodiment) An example of driving control according to different situations according to the embodiment will be described using FIG. 3. FIG. 3 is a diagram illustrating an example of driving control according to different situations according to the embodiment. In FIG. 3, speed is shown on the horizontal axis, and the speeds of the autonomously driven vehicle and surrounding manually driven vehicles are schematically shown by blocks 160 to 165.

[0094] Block 160 shows the speed of the autonomous vehicle when, for example, there are no other vehicles around the autonomous vehicle and the autonomous vehicle is autonomously driving alone, in the above-mentioned situation 1. When an autonomous vehicle is autonomously driving alone, it can travel at the highest speed.

[0095] Block 161 shows the speeds of the automated and manually driven vehicles in a situation similar to the above-described situation 2, where a platoon is being conducted with an automated vehicle as the lead vehicle and a manually driven vehicle as the follower, and the risk of pairing disconnection is low. The leading automated and manually driven vehicles can travel at high speeds within a safe range. Block 162 shows the speeds of the automated and manually driven vehicles in a situation similar to the above-described situation 2, where a platoon is being conducted with an automated vehicle as the lead vehicle and a manually driven vehicle as the follower, and the risk of pairing disconnection is high. Because the risk of pairing disconnection in the platoon is high and there is a risk at the time of release, the leading automated and manually driven vehicles can travel within a maximum speed range that is considered safe. In situation 2, the maximum speed is changed at the time of release depending on the risk.

[0096] Block 163 shows the speed of the autonomous vehicle when, for example, the autonomous vehicle is not electronically coupled to the manually driven vehicle and confirms that the manually driven vehicle is following, in the above-mentioned situation 3. The autonomous vehicle reduces its speed to the average speed level of the manually driven vehicle to prevent the manually driven vehicle from unreasonably following the manually driven vehicle. Block 164 shows the speed of the manually driven vehicle when, for example, the autonomous vehicle is not electronically coupled to the manually driven vehicle and confirms that the manually driven vehicle is following the autonomous vehicle, in the above-mentioned situation 3. Although the manually driven vehicle is following the autonomous vehicle, because the autonomous vehicle is driving at a reduced speed, it is possible to prevent the speed from exceeding the manual driving capabilities.

[0097] Block 165 shows the speed of a manually driven vehicle when prioritizing safety to avoid an accident. Since the manually driven vehicle is driving conservatively, prioritizing safety to avoid an accident, it is traveling at a slower speed than blocks 160 to 164.

[0098] (2-7. Another Example of Vehicle Control Processing According to Embodiment) Another example of the flow of the vehicle control processing according to the embodiment will be described with reference to Fig. 4. Fig. 4 is a flowchart showing another example of the flow of the vehicle control processing according to the embodiment. The vehicle control processing shown in Fig. 4 is executed when starting driving control depending on the situation.

[0099] The acquisition unit 131 acquires environmental information indicating the environment around the vehicle 200 (step S20). Specifically, the acquisition unit 131 acquires, as environmental information, the environment around the vehicle 200 and location information of the location of the vehicle 200 via the detection unit 140, etc. For example, the acquisition unit 131 acquires image data of the area around the vehicle 200 and distance information to surrounding objects. The acquisition unit 131 also acquires driving information of the vehicle 200 and the behavior of the vehicle 200. The acquisition unit 131 also acquires the illuminance and humidity around the vehicle 200. The acquisition unit 131 also acquires road information such as the legal speed limit of the road on which the vehicle 200 is located. Furthermore, manual driving risk information for the destination route may be acquired in advance from captured map data and updated weather data related to a predetermined driving location, and a driving speed condition or the like that is estimated to allow a manual driver to safely drive the route may be calculated based on the difficulty level of the road ahead.

[0100] The detection unit 133 detects a manually driven vehicle traveling around the vehicle 200 (step S21). The restriction unit 134 determines whether a manually driven vehicle traveling around the vehicle 200 has been detected (step S22). If a manually driven vehicle has not been detected (step S22: No), the driving control unit 132 transitions to independent autonomous driving (step S23).

[0101] On the other hand, if a manually driven vehicle is detected (step S22: Yes), the driving control unit 132 pairs with the manually driven vehicle and establishes an electronic connection. The restriction unit 134 evaluates the risk at the time of release of the manually driven vehicle (step S24). For example, based on environmental information, the restriction unit 134 derives the upper limit of driving conditions under which the manually driven vehicle can safely drive when released from platooning or following driving in the road environment in which the vehicle 200 is traveling, as a risk at the time of release. For example, the restriction unit 134 derives the upper limit speed within the range of conditions under which release is permitted when platooning or following driving is performed with a manually driven vehicle. For example, the road environment in which the vehicle 200 is traveling is assumed to be such that safe driving is possible at 120 km / h for a standalone autonomous vehicle, that driving is possible at 30 km / h for a manually driven vehicle, and that safe driving is possible up to approximately 80 km / h when platooning. If platooning is interrupted, safe handover and deceleration are not possible at 80 km / h. If a manually driven vehicle is traveling at a speed of about 50 km / h, it can quickly decelerate independently to 30 km / h even if the platooning is interrupted. In this case, the limiting unit 134 derives 50 km / h as the upper speed limit.

[0102] The driving control unit 132 determines whether a platooning request has been received from a nearby manually driven vehicle (step S25). If a platooning request has been received (step S25: Yes), the restriction unit 134 evaluates the risk of disconnecting the pairing (step S26).

[0103] The driving control unit 132 determines whether platooning is possible with surrounding manually driven vehicles (step S27). If the risk of disconnection is equal to or greater than an allowable value, the driving control unit 132 determines that platooning is impossible, and if the risk of disconnection is less than the allowable value, the driving control unit 132 determines that platooning is possible. If platooning is possible (step S27: Yes), the driving control unit 132 transitions to platooning with surrounding manually driven vehicles (step S28).

[0104] On the other hand, if a request for platooning has not been received (step S25: No) or if platooning is not possible (step S27: No), the restriction unit 134 determines whether any manually driven vehicles traveling nearby are following (step S29). The restriction unit 134 evaluates the degree of synchronization of the acceleration / deceleration timing of the manually driven vehicles with respect to the acceleration / deceleration of vehicle 200, and if the degree of synchronization is equal to or greater than a predetermined value, determines that the manually driven vehicles are following.

[0105] If a manually driven vehicle is following (step S29: Yes), the driving control unit 132 transitions to following driving with the manually driven vehicle traveling nearby (step S30). On the other hand, if a manually driven vehicle is not following (step S29: No), the process transitions to the above-mentioned step S23, where the driving control unit 132 transitions to independent automatic driving.

[0106] The driving control unit 132 determines whether to end the autonomous driving (step S31). For example, if an instruction to end the autonomous driving is given, such as an instruction to end the autonomous driving (step S31: Yes), the driving control unit 132 determines that the autonomous driving has ended and ends the processing. On the other hand, if an instruction to end the autonomous driving has not been given (step S31: No), the processing proceeds to step S20 described above.

[0107] As a result, the vehicle control system 100 according to the embodiment can prevent surrounding manually driven vehicles from getting into dangerous situations due to the influence of the autonomous driving of the vehicle 200. For example, when a manually driven vehicle platoons with the vehicle 200, a safe release handover from the platoon can be performed, thereby preventing dangerous situations. Furthermore, when a manually driven vehicle follows the vehicle 200, it travels at an average speed level of the manual driver, thereby preventing dangerous situations. Furthermore, by platooning with the vehicle 200, the manually driven vehicle can travel at high speeds within a safe range.

[0108] However, because autonomous vehicles are always at risk of being followed by nearby manually driven vehicles, forcing them to operate under the same average driving conditions as ordinary manually driven vehicles would would prevent the various high-performance cognitive functions they are equipped with for safety from fully functioning, which could result in the benefits of autonomous vehicles being difficult to discover and their widespread adoption thwarting. It would be more beneficial for many road users to realize the benefits of using autonomous vehicles.

[0109] Therefore, an autonomous vehicle may use several conditions to distinguish between vehicles that can follow the surrounding vehicle, and depending on the distinguished vehicle classification, perform dynamic decision control to determine whether to operate the vehicle on the lower limit suppression side or to raise the suppression limit to a more free high-speed control side condition.

[0110] There is a tendency for surrounding vehicles to either engage in follow-up or continue driving independently without attempting to follow. One such tendency is that when a vehicle prioritizes safe travel and continues autonomous driving control at low speeds regardless of whether other vehicles are nearby, it is likely that the driver is intentionally continuing to drive at low speeds in an effort to safely reach their destination without being distracted by their surroundings. In this case, one way to determine whether a certain tendency exists is to look for behavioral changes such as accelerating and matching the speed of the target vehicle when the vehicle catches up with the target vehicle and begins to drive ahead. If the vehicle attempts to follow the target vehicle, unless the electronic connection is robustly established, the vehicle will be at risk of disconnection after the follow-up. In this case, the autonomous vehicle will need to slow down and transition to lower-limit control, which simulates a non-consensual following situation without electronic connection. However, if there is no attempt to achieve synchronized following or an electronic coupling request, and the vehicle in question abandons following early without changing its behavior and continues independent driving control, it can be assumed that it is not affecting the driving control or decisions of the following vehicle. In this case, the autonomous vehicle may allow control at the upper limit of the vehicle's own vehicle's onboard equipment performance.

[0111] In this way, autonomous vehicles can prevent surrounding vehicles from unduly increasing their speed by taking into account the situation of surrounding vehicles. Furthermore, autonomous vehicles can travel at higher speeds in line with the capabilities of their onboard equipment, as long as it does not adversely affect the surrounding area.

[0112] In addition, surrounding drivers' driving characteristics and perceptions of safety vary greatly from one driver to another, and their sense of safety regarding driving also differs, even among young people who have just obtained their driver's license and the elderly. Furthermore, the characteristics of the region in which the driver lives, as well as differences in attitudes toward safety and seasonal and weather conditions, can cause fluctuations in the standard that surrounding drivers consider safe. For this reason, it is desirable for an autonomous vehicle to set target values ​​for driving conditions used to control autonomous driving as fixed values ​​in the driving control data 121, but rather to set them as condition selection parameters such as a look-up table (LUT) and use them dynamically or selectively in accordance with local conditions and road safety conditions. The driving control data 121 may be configured as multiple table data.

[0113] An example of calculating visibility distance according to weather and road conditions will be described using Figures 5A to 5D. Figures 5A to 5D are diagrams showing an example of tables constituting the driving control data 121 according to the embodiment. The driving control data 121 includes tables 121a to 121d shown in Figures 5A to 5D.

[0114] Table 121a shown in FIG. 5A stores effective visibility limit distances Iv [m] for various weather conditions, ranging from good visibility to poor visibility. For example, when the weather is a snowstorm, the effective visibility limit distance Iv is set to 300 [m] when visibility is at its best, and to 30 [m] when visibility is at its worst. Table 121a shown in FIG. 5A also shows patterns of safe following distances for vehicles such as passenger cars, which are used as guides for various weather conditions. For example, the safe following distance for a snowstorm is 50 [m], the safe following distance for fog is 70 [m], the safe following distance for rain is 100 [m], the safe following distance for nighttime is 150 [m], and the safe following distance for backlight is 70 [m].

[0115] 5B stores weight values ​​Rf1 of risk factors specified by map data for each road condition. For example, a road with good visibility has a weight value Rf1 of 1, and a school zone has a weight value Rf1 of 0.8.

[0116] 5C stores road-specific risk factor weights RF2 for each road type. For example, a standard road has a weight Rf2 of 1, and a road with an oncoming passing lane has a weight Rf2 of 0.8.

[0117] 5D stores risk factor weights RF3 for each season, taking seasonal dependency into consideration. For example, the weight Rf3 for normal seasons is set to 1, and the weight Rf3 for frozen roads is set to 0.7.

[0118] The driving control unit 132 determines the driving conditions for autonomous driving based on the environmental information acquired by the acquisition unit 131. For example, the driving control unit 132 determines the weather, road conditions, road type, and season from the environmental information, and derives the driving conditions for autonomous driving based on the determined weather, road conditions, road type, and season. For example, the driving control unit 132 estimates the expected visibility limit distance, taking into account each risk factor, as Iv x Rf1 x Rf2 x Rf3, for example. As a result, when the weight values ​​Rf1 to Rf3 for the road conditions, road type, and season are low, the expected visibility limit distance is estimated to be shorter than the effective visibility limit distance Iv. The driving control unit 132 calculates and applies a general-purpose maximum safe driving speed from the expected visibility limit distance of the following vehicle. This enables safer driving control operation.

[0119] 5A to 5D are merely examples, and the configuration of the driving control data 121 is not limited to these. The method of deriving the driving conditions is also merely an example, and is not limited to these.

[0120] If an autonomous vehicle is capable of recognizing the distant environment using sensors other than cameras and is using these capabilities to drive, it can estimate the visible field of view that a person can see from camera images. In cases where it is difficult to estimate the field of view because there is no target scene to estimate the field of view by evaluating camera images, it is possible to compensate by temporarily operating the vehicle under standard or safe conditions as default values.

[0121] The above situation assumes that there is one nearby vehicle that may attempt to follow the vehicle. When driving on a public road, it is easy to imagine a situation where many vehicles are driving around the vehicle. When driving alone, each vehicle typically assesses the surrounding conditions and balances its actions, making comprehensive decisions regarding speed, distance, etc. On the other hand, on roads where overtaking is difficult, if a slower vehicle is traveling in that section, a vehicle approaching it from behind catches up, and the overtaken vehicles gradually form a continuous line, forming a group. Whether they like it or not, vehicles in a group are forced to follow the group, whether they like it or not. There would be no problem if all drivers in the group were happy with the situation and willing to accept it. However, each driver's car usage and travel purposes are different. Some drivers will take risks in order to reach their destination quickly, while others prioritize their own driving ability and reaching their destination safely, and therefore will not go with the flow and will instead prioritize driving slowly.

[0122] If a vehicle operating in autonomous mode is forced to constantly drive at a slow and restrained speed out of consideration for attempts by nearby manually driven vehicles to follow unreasonably, the autonomous vehicle may be seen as a "slow driver." If control that is excessively biased toward slow speeds is used as a design specification, a bottleneck will be created, and other vehicles behind it that encounter a difficult situation to overtake will be put into a kind of trap, and will be seen as control that does not consider the impact on surrounding vehicles, and this excessively restrained control may irritate surrounding drivers, even if only indirectly.

[0123] As a result, if a vehicle falls into this trap, it may take the form of a reckless overtaking maneuver as a way to break away from the slow-moving group, which could lead to a different type of unsafe situation.

[0124] Taking these circumstances into consideration, it is effective to parameterize target conditions and enable variable settings to enable dynamic suppression control in autonomous vehicle control. Depending on the capabilities of the vehicle's onboard situation assessment processing device, when pairing a single vehicle or a small number of similar vehicles, if a vehicle is overtaken from behind and the vehicles are connected in a group of a certain number of vehicles or more, such as a line of vehicles, the system can notify the driver of the situation and, if the vehicle is not responding due to autonomous driving, implement the necessary control to yield to the following vehicle and break up the unnecessary line of vehicles. This can help resolve the situation where some drivers who are in a hurry are trapped in a group of vehicles behind the vehicle. Furthermore, vehicles in a group of vehicles that are not overtaking can be considered vehicles seeking peace of mind by following behind the vehicle and leading the way with advanced sensing. Therefore, after completing the yielding procedure, the system continues normal driving in accordance with driving conditions that are considered safe by ordinary drivers, which tend to be slower.

[0125] (2-8. Another Example of Vehicle Control Processing According to Embodiment) Another example of the flow of the vehicle control processing according to the embodiment will be described with reference to Fig. 6. Fig. 6 is a flowchart showing another example of the flow of the vehicle control processing according to the embodiment. The vehicle control processing shown in Fig. 6 is executed when starting driving control depending on the situation.

[0126] The driving control unit 132 determines whether driving conditions have deteriorated based on the environmental information acquired by the acquisition unit 131 (step S40). Typically, in autonomous driving, the driving environment is captured by a camera. Cameras are particularly suited to lane information recognition processing, and images are continuously captured. However, as visual visibility deteriorates, while distant objects become difficult to see, it becomes possible to confirm objects at greater distances using infrared light, which does not rely on visual inspection. For example, the driving control unit 132 determines that driving conditions have deteriorated due to reduced visibility based on data acquired as environmental information from a camera, LiDAR, or millimeter-wave radar.

[0127] The driving control unit 132 determines the risk of manual driving (step S41). During manual driving, the driver may be cautious and limit the vehicle speed below the road's legal speed. Examples of major factors that contribute to this include poor visibility, such as poor visibility at night, fog, heavy rain, or snowstorms. Also, drivers often reduce their speed as a preventative measure when the road surface is slippery or they are driving downhill. When there are vehicles nearby, drivers generally limit their driving speed to a level that does not disturb the surrounding vehicles and that they consider safe. While stable following-up driving poses few challenges, if the driver catches up with the vehicle and loses sight of it while driving at high speed, and transitions to manual driving alone, it can be dangerous if the driver is unable to visually confirm the situation before slowing down completely. Therefore, it is necessary to predict within a certain range and suppress the speed to prevent this from happening. For example, the driving control unit 132 derives the upper limit speed at which a manually driven vehicle can travel safely when releasing from platoon driving and following driving in the road environment in which the vehicle 200 is traveling, as a risk of manual driving.

[0128] The driving control unit 132 calculates a natural inter-vehicle spacing during manual driving (step S42). The driving control unit 132 calculates an inter-vehicle spacing that allows the manual driving driver to drive safely. Since the purpose of the driving condition assessment during manual driving is not to actually perform manual driving itself, it is not necessary to calculate conditions that satisfy all conditions, and the calculation may be performed by referring to conditions such as an LUT.

[0129] The driving control unit 132 determines whether there are surrounding vehicles and whether to cooperate with the automatic driving (step S43). Typically, an automatically driven vehicle is equipped with advanced recognition equipment, which allows it to recognize the environment better than a human being, enabling it to safely follow surrounding vehicles and most commonly catch up with slower vehicles. Since an automatically driven vehicle may be subjected to unreasonable approaching driving by a manually driven vehicle in an attempt to follow the surrounding vehicle, it is required to constantly recognize nearby approaching vehicles, evaluate the risk of switching to synchronized driving, and make appropriate judgments and responses.

[0130] The driving control unit 132 suppresses the driving speed (step S44). The main target of the suppression control is suppression of the upper limit speed. However, other suppression control may be performed, such as starting early deceleration before a curve or before a section with poor visibility, or turning on brake lights in conjunction with engine braking or electric regenerative braking to prompt the following vehicle to recognize the deceleration early, even if the predetermined deceleration has not been achieved. Furthermore, if the following vehicle continuously detects a behavior detection that does not attempt to maintain an appropriate inter-vehicle distance despite the vehicle's deceleration control, the driving control unit 132 may intentionally further decelerate or stop on the shoulder of the road to yield to the following vehicle, and other strategic control may be performed.

[0131] The driving control unit 132 takes into consideration the following vehicle when there is a following vehicle (step S45). For example, if it is determined that the following vehicle is not maintaining a sufficient safe distance based on the expected visibility, in addition to braking for controlling the host vehicle, it may apply gentle braking to alert the driver of the following vehicle to dangerously approach too close. By warning the driver, it is possible to prevent the following vehicle from approaching too close to the host vehicle. For this reason, if there is a vehicle ahead of the host vehicle, it may maintain an extra distance from the leading vehicle in advance. If the following vehicle approaches too close, it may generally be warned. However, if the following vehicle does not respond appropriately and approaches too close, resulting in a risk of a rear-end collision, it may slow down its deceleration and temporarily use the extra space between the leading vehicle and the following vehicle as an absorption buffer. Maintaining an extra distance between the following vehicle and the host vehicle may prevent the following vehicle from colliding with the host vehicle from rear-end.

[0132] The driving control unit 132 performs preventive processing to prevent excessively slow driving from causing large groups of vehicles behind (step S46). For example, the driving control unit 132 controls the vehicle to yield to the following vehicle. Platooning or following at excessively slow speeds can cause a group of vehicles, including the following vehicle, to form behind the vehicle, holding the following vehicle back. To prevent large groups of vehicles behind, appropriate measures are needed, including automatic, semi-automated, or manual driving. Without such measures, if the autonomous vehicle is designed to always stay at the front of the group and be perceived as an obstruction to other vehicles rushing to secure a spot, this can significantly disrupt social acceptability and potentially lead to the possibility of the vehicle becoming a target of harassment or other attacks from other drivers. By performing preventive processing to prevent large groups, the vehicle can be prevented from becoming a target of attack.

[0133] As a result, the vehicle control system 100 according to the embodiment can suppress rear-end collisions from following vehicles. Also, the vehicle control system 100 according to the embodiment can prevent a large group of vehicles behind.

[0134] Incidentally, when an autonomous vehicle equipped with advanced recognition functions is traveling in a situation where visibility is poor and it is difficult to see its surroundings, if there is a vehicle nearby that is traveling safely in fully autonomous driving, there is a possibility that the vehicle in question will attempt to follow it.

[0135] If vehicle-to-vehicle communications are in place, electronic communications can be robust between vehicles within the travel section, and the risk of disconnection can be sufficiently reduced, then it will be possible to electronically couple vehicles wirelessly, with an autonomous vehicle acting as a lead vehicle, and travel at a speed that ensures that the connection is maintained safely. In this case, there will be no unnecessary disconnection of the electronic connection, and even if a vehicle breaks away from the lead connection, it will be necessary to safely transition to low-speed travel, and measures will need to be taken in advance to ensure a series of safe transitions to platooning.

[0136] Furthermore, if the risk of the electronic coupling being severed is high, it is desirable to perform proactive restrained driving that allows safe driving even if the electronic coupling with the following vehicle is severed. In other words, the leading vehicle traveling in autonomous driving mode needs to control its driving from the beginning to maintain a speed that serves as a guideline for safe driving of a standard manual vehicle when traveling independently, in preparation for the possibility that the temporarily connected following vehicle may accidentally lose its electronic coupling. For example, the limiting unit 134 may derive an upper limit for the driving conditions under which surrounding manually driven vehicles can travel safely, and limit the driving conditions for autonomous driving to within the derived upper limit.

[0137] Furthermore, if a disconnection occurs, it may be necessary to drive at a slower speed to deal with the risk associated with transitioning to fully manual driving, depending on the risk level, etc. Therefore, for example, the restriction unit 134 may evaluate the risk of disconnection of the electronic connection established with surrounding manually driven vehicles, and may restrict the driving conditions for automated driving with electronic coupling as the risk increases. For example, the restriction unit 134 may restrict the driving conditions for automated driving with electronic coupling to a slower speed as the risk increases.

[0138] (2-9. Another Example of Vehicle Control Processing According to Embodiment) Another example of the flow of the vehicle control processing according to the embodiment will be described with reference to Fig. 7. Fig. 7 is a flowchart showing an example of the flow of the vehicle control processing according to the embodiment. The vehicle control processing shown in Fig. 7 is executed when starting when overtaking a leading vehicle.

[0139] The driving control unit 132 classifies the vehicle ahead as either a single vehicle traveling at low speed, a manually driven vehicle, or a vehicle equipped with electronic coupling (step S50). Based on the classification result, the driving control unit 132 determines whether the vehicle ahead is (1) a single vehicle traveling at low speed, (2) a manually driven vehicle, or (3) a vehicle equipped with electronic coupling (step S51).

[0140] If the vehicle in front is (1) a single vehicle traveling at a low speed, the driving control unit 132 passes the vehicle in front that it has caught up with (step S52) and ends the process.

[0141] On the other hand, if the leading vehicle is (2) a manually driven vehicle, the driving control unit 132 calculates driving conditions for suppressed autonomous driving, such as following the leading vehicle that has been overtaken (step S53). For example, the driving control unit 132 calculates driving conditions such as maintaining a large inter-vehicle distance from the leading vehicle and gradual acceleration and deceleration, so as to follow the leading vehicle without creating a sense of pressure. If the vehicle is caught up with a vehicle from behind and the vehicles are lined up in a group of more than a certain number of vehicles, such as a string of vehicles, the driving control unit 132 performs control to yield to the vehicle behind (step S54). After yielding, the driving control unit 132 resumes autonomous driving and updates the driving conditions for autonomous driving (step S55). The driving control unit 132 determines whether there is a leading vehicle in front of the vehicle 200 (step S56). If there is a leading vehicle (step S56: Yes), the process proceeds to step S53 described above.

[0142] On the other hand, if there is no preceding vehicle (step S56: No), the driving control unit 132 transitions to independent automatic driving, resumes independent driving (step S57), and ends the processing.

[0143] On the other hand, if the leading vehicle is (3) a vehicle equipped with electronic coupling, the driving control unit 132 establishes an electronic coupling by pairing with the leading vehicle that has been overtaken (step S58). The driving control unit 132 determines whether the electronic coupling with the leading vehicle is robust (step S59).

[0144] If the electronic coupling is not robust (step S59: No: (3)-2), the driving control unit 132 reduces the speed and drives the platoon (step S60). If the vehicle is caught up with from behind and the vehicles are connected in a group of more than a certain number of vehicles, such as a string of vehicles, the driving control unit 132 performs control to give way to the vehicle behind (step S61). After giving way, the driving control unit 132 resumes autonomous driving and updates the driving conditions for autonomous driving (step S62). The driving control unit 132 determines whether or not there is a leading vehicle in front of the vehicle 200 (step S63). If there is a leading vehicle (step S63: Yes), the process proceeds to step S58 described above.

[0145] On the other hand, if there is no leading vehicle (step S63: No), the driving control unit 132 proceeds to the above-mentioned step S57, resumes independent driving, and ends the processing.

[0146] On the other hand, if the electronic coupling is robust (step S59: Yes: (3)-1), the driving control unit 132 drives the platoon at a high speed (step S64). The driving control unit 132 determines whether the electronic coupling with the vehicle in front has been released (step S65). If the electronic coupling has not been released (step S65: No), the process proceeds to step S64.

[0147] If the electronic connection is released (step S65: Yes), the process ends.

[0148] 8 is a diagram illustrating an example of cruise control according to a situation when the host vehicle catches up with a leading vehicle according to the embodiment. FIG. 8 illustrates cruise control according to a situation when the leading vehicle that the host vehicle catches up with is (1) a single vehicle traveling at a low speed, (2) a manually driven vehicle, or (3) a vehicle equipped with electronic coupling. FIG. 8 also illustrates cruise control according to a situation when the leading vehicle (3) is an electronic coupling-equipped vehicle, divided into a case where the electronic coupling is robust ((3)-1) and a case where the electronic coupling is not robust ((3)-2).

[0149] When the vehicle ahead that the host vehicle 200 has overtaken is (1) a single vehicle traveling at a low speed, the host vehicle 200 passes the vehicle without slowing down. This allows the host vehicle 200 to move at a high speed.

[0150] Furthermore, when the vehicle ahead that vehicle 200 has overtaken is (2) a manually driven vehicle, vehicle 200 follows the vehicle ahead under the suppressed automatic driving conditions. This allows vehicle 200 to follow the vehicle ahead without creating a sense of pressure, and prevents the vehicle ahead from becoming in a dangerous situation. Furthermore, when vehicle 200 is following a group of vehicles with a certain number of vehicles or more behind it, vehicle 200 yields to the vehicles behind. This allows vehicle 200 to prevent dangerous situations, such as the vehicles behind making unreasonable overtaking attempts.

[0151] Furthermore, if the vehicle ahead of the vehicle 200 is a vehicle equipped with (3) electronic coupling and the electronic coupling is robust ((3)-1), the vehicle 200 will travel in a convoy at a high speed. This allows the vehicle 200 to travel at a high speed.

[0152] Furthermore, if the vehicle 200 has overtaken is a vehicle equipped with (3) electronic coupling and the electronic coupling is not robust ((3)-2), the vehicle 200 reduces its speed and travels in a convoy. By traveling in a convoy with the vehicle in front, the vehicle 200 can travel stably with the vehicle in front, and can prevent the vehicle in front from getting into a dangerous situation. Furthermore, if the vehicle 200 is following a group of vehicles with a certain number of vehicles or more behind it, the vehicle 200 will yield to the vehicle behind. This allows the vehicle 200 to prevent dangerous situations, such as the vehicle behind making unreasonable overtaking attempts.

[0153] Meanwhile, the slower-speed driving conditions that average drivers consider safe vary depending on various conditions, such as the temperament of local drivers, whether it is a rush hour, weather conditions, and local traffic alerts or warnings that are influenced by weather phenomena in the relevant section. For this reason, individual driving conditions, such as slower-speed driving conditions, may not be set as specific fixed values ​​in the vehicle design, but may be changeable depending on the conditions. As described above, in order to avoid inducing dangerous driving behaviors in other vehicles by not matching the driving behaviors of other drivers around the vehicle, it is desirable to apply and set individual driving conditions according to the acquisition of information necessary for estimating the driving conditions. If the information determining the individual driving conditions cannot be obtained externally, such as over the air (OTA), the setting with the closest confidence level for matching the conditions may be determined using a locally stored LUT and used for control.

[0154] Once the presence of a manually driven vehicle is confirmed by determining whether there are any manually driven vehicles in the vicinity, and the manually driven vehicle is safely following behind using considerate driving control, or if a request is received from the following vehicle to take the lead, the system will switch to platooning control and establish an electronic wireless connection with the following vehicle, allowing the following manually driven vehicle to safely follow behind and allowing the leading vehicle to exercise faster driving control than if the following vehicle were driving manually alone.

[0155] In platooning, all vehicles do not need to be equipped with the same advanced autonomous driving control capabilities as self-driving vehicles. The prerequisites for using platooning are that they have the means to transmit information necessary for control with their own vehicle through vehicle-to-vehicle communication, that the own vehicle and the following vehicles can form a platoon and drive safely, and that a safe communication physical layer link can be established through negotiation between them via V2V.

[0156] However, the allowable speed range may be determined based on the evaluation value of the communication error correction and the robustness level of the physical link.Furthermore, speed conditions may be selectively applied according to the risk of the following vehicle attempting to follow and road conditions.

[0157] High-speed driving is possible if a robust electronic connection can be established and there is little risk of the electronic connection being disconnected even under high-speed driving conditions.On the other hand, under conditions where the reliability of vehicle-to-vehicle communication is low and there is a risk of a trailing vehicle losing control when transitioning to safe independent manual driving if the electronic connection is suddenly disconnected at high speed, the possibility of endangering the trailing vehicle cannot be eliminated even during platooning, so conservative speed suppression control may be implemented.

[0158] Incidentally, when driving in bad weather and with poor visibility, if a following vehicle approaches a leading vehicle, assuming that the vehicle in front has generally ensured a high degree of safety by predicting its course using the sensing functions of its advanced recognition equipment, there is a risk of a rear-end collision if the leading vehicle suddenly brakes.

[0159] It is necessary to reduce this risk, but to do so without using vehicle-to-vehicle communication, etc., some ingenuity is required in controlling the vehicle. Examples of ingenuity in controlling the vehicle are shown in 1 and 2 below.

[0160] 1. When the driver recognizes that a vehicle approaching from behind is driving too close, the driver's actions to encourage the following vehicle to increase the distance to a level that the following vehicle feels is safe are unconsciously found within a comfortable compromise that balances comfort and risk, and the driver acts while making that adjustment, adjusting the distance while balancing the risk of losing sight of the vehicle in front that the driver relies on by keeping too much distance, the risk of the following vehicle overtaking the vehicle behind by leaving too much distance and getting cut in between the vehicle in front, and the risk of getting too close to the vehicle in front and crashing into it. This happens when the following vehicle is too close and instinctively perceives it as a risk and unconsciously increases the distance between the vehicles. Therefore, instead of gentle braking when your vehicle needs to brake, you can instead suddenly slow down to a level that will avoid a rear-end collision, which can make the driver of the following vehicle feel a sense of risk when they let their guard down for a moment.However, if excessively sudden braking is applied, the driver of the following vehicle will not be able to grasp the situation and will have to make unnecessary steering adjustments or apply sudden, unreasonable braking in an attempt to avoid the situation, which can lead to an accident where the following vehicle spins out of control.

[0161] 2. Even if the following vehicle is unable to brake in time, the system will set a longer distance between the vehicle and the vehicle in front in advance, ensuring a sufficient buffer distance between the vehicle and the vehicle in front. If the following vehicle becomes too close and is unable to keep up with the deceleration of the vehicle, the system will reduce the deceleration of the vehicle to the extent that a rear-end collision with the vehicle in front can be avoided.

[0162] (2-10. Another Example of Vehicle Control Processing According to Embodiment) Another example of the flow of the vehicle control processing according to the embodiment will be described with reference to Fig. 9. Fig. 9 is a flowchart showing an example of the flow of the vehicle control processing according to the embodiment. The vehicle control processing shown in Fig. 9 is executed when the process starts when overtaking a leading vehicle.

[0163] The driving control unit 132 identifies a recommended inter-vehicle distance (step S70). For example, the driving control unit 132 identifies the recommended inter-vehicle distance based on the road on which the vehicle 200 is located, the weather, the traveling speed, and the like, based on the environmental information acquired by the acquisition unit 131. When there is a traveling vehicle following behind, the driving control unit 132 evaluates the risk of approaching the vehicle when braking based on the inter-vehicle distance between the vehicle and the following vehicle (step S71).

[0164] The driving control unit 132 determines whether or not excessive approaching of the following vehicle has occurred (step S72) based on the environmental information acquired by the acquisition unit 131. If excessive approaching has not occurred (step S72: No), the process proceeds to step S70 described above.

[0165] On the other hand, if excessive approach has occurred (step S72: Yes), the driving control unit 132 performs sudden braking control within a range that avoids a rear-end collision in order to alert the driver (step S73).

[0166] The driving control unit 132 determines whether the collision risk with the following vehicle is high (step S74). For example, the driving control unit 132 determines that the collision risk is high when the following vehicle is unable to catch up due to sudden braking by the vehicle itself. If the collision risk is high (step S74: Yes), the driving control unit 132 ensures a sufficient inter-vehicle distance to serve as a buffer between the vehicle and the leading vehicle (step S75), and proceeds to step S76. On the other hand, if the collision risk is low (step S74: No), the driving control unit 132 proceeds to step S76.

[0167] The driving control unit 132 monitors the response delay of the following vehicle based on the timing of acceleration / deceleration of the following vehicle relative to the acceleration / deceleration of the host vehicle, etc., based on the environmental information acquired by the acquisition unit 131 (step S76). The driving control unit 132 determines whether excessive approaching of the following vehicle has occurred (step S77). If excessive approaching has not occurred (step S77: No), the processing ends.

[0168] On the other hand, if excessive approach has occurred (step S77: Yes), the driving control unit 132 reviews the driving to avoid a rear-end collision (terminating the processing in step S78). For example, the driving control unit 132 reduces the deceleration of the host vehicle to an extent that a rear-end collision with the vehicle in front can be avoided. Furthermore, the driving control unit 132 performs control to give way to the following vehicle.

[0169] As a result, the vehicle control system 100 according to the embodiment can alert the driver to the risk of a rear-end collision when the following vehicle comes too close. Furthermore, when the risk of a collision with the following vehicle is high, the vehicle control system 100 according to the embodiment can suppress a rear-end collision with the following vehicle by ensuring a sufficient inter-vehicle distance to serve as a buffer between the driver's own vehicle and the vehicle in front and reducing the deceleration of the vehicle within a range that can avoid a rear-end collision with the vehicle in front.

[0170] (2-11. Example of configuration of information processing system according to embodiment) In the above description, an example has been shown in which the vehicle 200 executes the processing according to the embodiment, but such processing may also be executed by the devices constituting the information processing system 1 according to the embodiment working together. The information processing system 1 according to the embodiment will be described with reference to FIG. 10. FIG. 10 is a diagram showing an example of the configuration of the information processing system 1 according to the embodiment.

[0171] The network N is a general term for a network that connects the devices that make up the information processing system 1. For example, the network N is the Internet, a mobile phone communication network, or the like.

[0172] The vehicle 200 is an example of a vehicle control system according to the present disclosure, and is a moving body equipped with an information processing function, such as an automobile. The vehicle 200 executes the various information processes described above.

[0173] The terminal 50 is, for example, an information processing terminal owned by the driver of the vehicle 200, and is, for example, a smartphone, a tablet terminal, or a wearable device such as a smart watch. The cloud server 150 is a server device used by an administrator or the like who manages the vehicle 200. The cloud server 150 provides various information to the vehicle 200, accepts requests from the vehicle 200, and executes various information processes, for example. The communication base station 40 is a base station that relays communications when connecting the network N and each device.

[0174] The devices constituting the information processing system 1 according to this embodiment may cooperate to function as the vehicle control system of the present disclosure.

[0175] (3. Other Embodiments) The processing according to each of the above-described embodiments may be implemented in various different forms other than the above-described embodiments.

[0176] (3-1. Configuration of the Mobile Body) For example, the vehicle 200 may be realized by an autonomous mobile body (automobile) that performs automatic driving. In that case, the vehicle 200 may have the configurations shown in FIGS. 11 and 12 in addition to the configuration shown in FIG. 1. Note that the units shown below may be included in the units shown in FIG. 1, for example.

[0177] That is, the vehicle control system 100 of the vehicle 200 according to the present technology can also be configured as the following vehicle control system 11. Fig. 11 is a block diagram showing an example of a schematic functional configuration of the vehicle control system 11 to which the present technology can be applied. Fig. 12 is a diagram showing an example of a sensing area by the vehicle control system to which the present technology can be applied.

[0178] The vehicle control system 11 is provided in the vehicle 200 and performs processing related to driving assistance and automatic driving of the vehicle 200.

[0179] The vehicle control system 11 includes a vehicle control ECU (Electronic Control Unit) 21, a communication unit 22, a map information storage unit 23, a GNSS (Global Navigation Satellite System) receiving unit 24, an external recognition sensor 25, an in-vehicle sensor 26, a vehicle sensor 27, a recording unit 28, a driving assistance / autonomous driving control unit 29, a DMS (Driver Monitoring System) 30, an HMI (Human Machine Interface) 31, and a vehicle control unit 32.

[0180] The vehicle control ECU 21, communication unit 22, map information storage unit 23, GNSS receiving unit 24, external recognition sensor 25, in-vehicle sensor 26, vehicle sensor 27, recording unit 28, cruise assist / autonomous driving control unit 29, DMS 30, HMI 31, and vehicle control unit 32 are connected to each other so as to be able to communicate with each other via a communication network 41. The communication network 41 is configured by an in-vehicle communication network or bus conforming to a digital two-way communication standard such as CAN (Controller Area Network), LIN (Local Interconnect Network), LAN (Local Area Network), FlexRay (registered trademark), or Ethernet (registered trademark). Different communication networks 41 may be used depending on the type of data being communicated; for example, CAN is used for data related to vehicle control, and Ethernet is used for large-volume data. In addition, each part of the vehicle control system 11 may be directly connected without going through the communication network 41, using wireless communication intended for communication over relatively short distances, such as near field communication (NFC) or Bluetooth (registered trademark).

[0181] In the following description, when each unit of the vehicle control system 11 communicates via the communication network 41, the description of the communication network 41 will be omitted. For example, when the vehicle control ECU 21 and the communication unit 22 communicate via the communication network 41, it will simply be described that the vehicle control ECU 21 and the communication unit 22 communicate with each other.

[0182] The vehicle control ECU 21 is configured by various processors such as a CPU (Central Processing Unit) and an MPU (Micro Processing Unit), and controls the entire or part of the functions of the vehicle control system 11.

[0183] The communication unit 22 communicates with various devices inside and outside the vehicle, other vehicles, servers, base stations, etc., and transmits and receives various data. At this time, the communication unit 22 can communicate using a plurality of communication methods.

[0184] The following provides an overview of communication with the outside of the vehicle that can be performed by the communication unit 22. The communication unit 22 communicates with a server (hereinafter referred to as an external server) or the like on an external network via a base station or an access point using a wireless communication method such as 5G (fifth generation mobile communication system), LTE (Long Term Evolution), or DSRC (Dedicated Short Range Communications). The external network with which the communication unit 22 communicates is, for example, the Internet, a cloud network, or a network specific to a carrier. The communication method used by the communication unit 22 to communicate with the external network is not particularly limited as long as it is a wireless communication method that enables digital two-way communication at a communication speed equal to or higher than a predetermined distance.

[0185] Furthermore, for example, the communication unit 22 can communicate with a terminal located near the vehicle using P2P (Peer To Peer) technology. The terminal located near the vehicle can be, for example, a terminal worn by a mobile object moving at a relatively low speed, such as a pedestrian or a bicycle, a terminal installed at a fixed position in a store, or an MTC (Machine Type Communication) terminal. Furthermore, the communication unit 22 can also perform V2X communication. V2X communication refers to communication between one's own vehicle and others, such as vehicle-to-vehicle (Vehicle-to-Vehicle) communication with other vehicles, vehicle-to-infrastructure (Vehicle-to-Infrastructure) communication with roadside devices or the like, vehicle-to-home (Vehicle-to-Home) communication with a home, and vehicle-to-pedestrian (Vehicle-to-Pedestrian) communication with a terminal or the like carried by a pedestrian.

[0186] The communication unit 22 can receive, for example, a program for updating software that controls the operation of the vehicle control system 11 from the outside (over the air). The communication unit 22 can also receive, for example, map information, traffic information, information about the surroundings of the vehicle 200, and the like from the outside. Furthermore, for example, the communication unit 22 can transmit information about the vehicle 200 and information about the surroundings of the vehicle 200 to the outside. Information about the vehicle 200 that the communication unit 22 transmits to the outside includes, for example, data indicating the state of the vehicle 200, the recognition result by the recognition unit 73, and the like. Furthermore, for example, the communication unit 22 performs communication corresponding to a vehicle emergency notification system such as e-call.

[0187] The following provides an overview of communication with the vehicle interior that can be performed by the communication unit 22. The communication unit 22 can communicate with each device in the vehicle using, for example, wireless communication. The communication unit 22 can wirelessly communicate with each device in the vehicle using a communication method that enables digital bidirectional communication at a predetermined communication speed or higher via wireless communication, such as wireless LAN, Bluetooth, NFC, or WUSB (Wireless USB). However, the communication unit 22 can also communicate with each device in the vehicle using wired communication. For example, the communication unit 22 can communicate with each device in the vehicle using wired communication via a cable connected to a connection terminal (not shown). The communication unit 22 can communicate with each device in the vehicle using a communication method that enables digital two-way communication at a communication speed higher than a predetermined level via wired communication, such as USB (Universal Serial Bus), HDMI (High-Definition Multimedia Interface) (registered trademark), or MHL (Mobile High-Definition Link).

[0188] Here, the in-vehicle device refers to, for example, a device in the vehicle that is not connected to the communication network 41. Possible in-vehicle devices include, for example, a mobile device or wearable device carried by a passenger such as a driver, and an information device brought into the vehicle and temporarily installed therein.

[0189] For example, the communication unit 22 receives electromagnetic waves transmitted by a road traffic information and communication system (VICS (Vehicle Information and Communication System) (registered trademark)) such as a radio beacon, an optical beacon, or FM multiplex broadcasting.

[0190] The map information storage unit 23 stores one or both of a map acquired from an external source and a map created by the vehicle 200. For example, the map information storage unit 23 stores a three-dimensional high-precision map, a global map that is less accurate than a high-precision map and covers a wide area, and the like.

[0191] Examples of high-precision maps include dynamic maps, point cloud maps, and vector maps. A dynamic map is a map consisting of four layers of dynamic information, quasi-dynamic information, quasi-static information, and static information, and is provided to the vehicle 200 from an external server or the like. A point cloud map is a map made up of a point cloud (point group data). Here, a vector map refers to a map adapted to an ADAS (Advanced Driver Assistance System) in which traffic information such as the positions of lanes and traffic lights is associated with a point cloud map.

[0192] The point cloud map and the vector map may be provided, for example, from an external server or the like, or may be created by the vehicle 200 based on sensing results from the radar 52, the LiDAR 53, or the like as a map for matching with a local map described later, and stored in the map information storage unit 23. Furthermore, when a high-precision map is provided from an external server or the like, map data of, for example, an area of ​​several hundred meters square regarding the planned route along which the vehicle 200 will travel is acquired from the external server or the like in order to reduce communication capacity.

[0193] The GNSS receiver 24 receives GNSS signals from GNSS satellites and acquires position information of the vehicle 200. The received GNSS signals are supplied to the driving assistance / autonomous driving control unit 29. Note that the GNSS receiver 24 is not limited to a method using GNSS signals, and may acquire position information using a beacon, for example.

[0194] The external recognition sensor 25 includes various sensors used to recognize the situation outside the vehicle 200, and supplies sensor data from each sensor to each part of the vehicle control system 11. The type and number of sensors included in the external recognition sensor 25 are arbitrary.

[0195] For example, the external recognition sensor 25 includes a camera 51, a radar 52, a LiDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging) 53, and an ultrasonic sensor 54. Without being limited to this, the external recognition sensor 25 may be configured to include one or more types of sensors selected from the camera 51, the radar 52, the LiDAR 53, and the ultrasonic sensor 54. The number of cameras 51, radars 52, LiDARs 53, and ultrasonic sensors 54 is not particularly limited as long as the number is a number that can be realistically installed on the vehicle 200. Furthermore, the types of sensors included in the external recognition sensor 25 are not limited to this example, and the external recognition sensor 25 may include other types of sensors. Examples of sensing areas of the sensors included in the external recognition sensor 25 will be described later.

[0196] The imaging method of the camera 51 is not particularly limited as long as it is an imaging method that allows distance measurement. For example, cameras using various imaging methods such as a ToF (Time Of Flight) camera, a stereo camera, a monocular camera, and an infrared camera can be applied as needed to the camera 51. However, the camera 51 may simply acquire an image without distance measurement.

[0197] Furthermore, for example, the external recognition sensor 25 may include an environmental sensor for detecting the environment of the vehicle 200. The environmental sensor is a sensor for detecting the environment such as weather, climate, brightness, etc., and may include various sensors such as a raindrop sensor, a fog sensor, a sunlight sensor, a snow sensor, and an illuminance sensor.

[0198] Furthermore, for example, the external recognition sensor 25 includes a microphone used to detect sounds around the vehicle 200 and the location of sound sources.

[0199] The interior sensor 26 includes various sensors for detecting information inside the vehicle, and supplies sensor data from each sensor to each unit of the vehicle control system 11. The types and number of the various sensors included in the interior sensor 26 are not particularly limited as long as they are the number that can be realistically installed in the vehicle 200.

[0200] For example, the interior sensor 26 may include one or more types of sensors selected from the group consisting of a camera, radar, a seating sensor, a steering wheel sensor, a microphone, and a biometric sensor. The camera included in the interior sensor 26 may be a camera using any of a variety of imaging methods capable of measuring distances, such as a Time of Flight (ToF) camera, a stereo camera, a monocular camera, or an infrared camera. The camera included in the interior sensor 26 may also be a camera simply for acquiring captured images, regardless of distance measurement. The biometric sensor included in the interior sensor 26 may be provided, for example, on a seat, steering wheel, or the like, and detect various types of biometric information of a passenger, such as a driver.

[0201] The vehicle sensor 27 includes various sensors for detecting the state of the vehicle 200, and supplies sensor data from each sensor to each unit of the vehicle control system 11. The types and number of the various sensors included in the vehicle sensor 27 are not particularly limited as long as they are the number that can be realistically installed on the vehicle 200.

[0202] For example, the vehicle sensor 27 includes a speed sensor, an acceleration sensor, an angular velocity sensor (gyro sensor), and an inertial measurement unit (IMU (Inertial Measurement Unit)) that integrates these sensors. For example, the vehicle sensor 27 includes a steering angle sensor that detects the steering angle of the steering wheel, a yaw rate sensor, an accelerator sensor that detects the amount of accelerator pedal operation, and a brake sensor that detects the amount of brake pedal operation. For example, the vehicle sensor 27 includes a rotation sensor that detects the number of rotations of the engine or motor, an air pressure sensor that detects tire air pressure, a slip ratio sensor that detects tire slip ratio, and a wheel speed sensor that detects the rotation speed of the wheels. For example, the vehicle sensor 27 includes a battery sensor that detects the remaining battery charge and temperature, and an impact sensor that detects external impacts.

[0203] The recording unit 28 includes at least one of a non-volatile storage medium and a volatile storage medium, and stores data and programs. The recording unit 28 is used, for example, as an EEPROM (Electrically Erasable Programmable Read Only Memory) and a RAM (Random Access Memory). The storage medium may be a magnetic storage device such as a hard disk drive (HDD), a semiconductor storage device, an optical storage device, or a magneto-optical storage device. The recording unit 28 records various programs and data used by each component of the vehicle control system 11. For example, the recording unit 28 includes an EDR (Event Data Recorder) or a DSSAD (Data Storage System for Automated Driving), and records information about the vehicle 200 before and after an event such as an accident, and biometric information acquired by the in-vehicle sensor 26.

[0204] The driving assistance / automated driving control unit 29 controls driving assistance and automatic driving of the vehicle 200. For example, the driving assistance / automated driving control unit 29 includes an analysis unit 61, an action planning unit 62, and an operation control unit 63.

[0205] The analysis unit 61 performs an analysis process of the vehicle 200 and the surrounding situation. The analysis unit 61 includes a self-position estimation unit 71, a sensor fusion unit 72, and a recognition unit 73.

[0206] The self-position estimation unit 71 estimates the self-position of the vehicle 200 based on the sensor data from the external recognition sensor 25 and the high-precision map stored in the map information storage unit 23. For example, the self-position estimation unit 71 generates a local map based on the sensor data from the external recognition sensor 25 and matches the local map with the high-precision map to estimate the self-position of the vehicle 200. The position of the vehicle 200 is based on, for example, the center of the rear wheel pair axle.

[0207] The local map is, for example, a three-dimensional high-precision map or an occupancy grid map created using a technology such as SLAM (Simultaneous Localization and Mapping). The three-dimensional high-precision map is, for example, the point cloud map described above. The occupancy grid map is a map in which the three-dimensional or two-dimensional space around the vehicle 200 is divided into grids of a predetermined size and the occupancy state of objects is indicated on a grid-by-grid basis. The occupancy state of objects is indicated, for example, by the presence or absence of an object and its probability of existence. The local map is also used, for example, in the detection process and recognition process of the situation outside the vehicle 200 by the recognition unit 73.

[0208] The self-position estimation unit 71 may estimate the self-position of the vehicle 200 based on the GNSS signal and sensor data from the vehicle sensor 27 .

[0209] The sensor fusion unit 72 performs sensor fusion processing to obtain new information by combining multiple different types of sensor data (for example, image data supplied from the camera 51 and sensor data supplied from the radar 52). Methods for combining different types of sensor data include integration, fusion, and association.

[0210] The recognition unit 73 executes a detection process for detecting the situation outside the vehicle 200 and a recognition process for recognizing the situation outside the vehicle 200 .

[0211] For example, the recognition unit 73 performs detection processing and recognition processing of the situation outside the vehicle 200 based on information from the external recognition sensor 25, information from the self-position estimation unit 71, information from the sensor fusion unit 72, and the like.

[0212] Specifically, for example, the recognition unit 73 performs detection processing and recognition processing of objects around the vehicle 200. The object detection processing is, for example, processing to detect the presence or absence, size, shape, position, movement, etc. of an object. The object recognition processing is, for example, processing to recognize attributes such as the type of object, or to identify a specific object. However, the detection processing and the recognition processing are not necessarily clearly separated, and may overlap.

[0213] For example, the recognition unit 73 detects objects around the vehicle 200 by performing clustering to classify a point cloud based on sensor data from the LiDAR 53, the radar 52, or the like into clusters of points. This allows the presence, size, shape, and position of objects around the vehicle 200 to be detected.

[0214] For example, the recognition unit 73 performs tracking to follow the movement of clusters of point clouds classified by clustering, thereby detecting the movement of objects around the vehicle 200. As a result, the speed and traveling direction (movement vector) of the objects around the vehicle 200 are detected.

[0215] For example, the recognition unit 73 detects or recognizes vehicles, people, bicycles, obstacles, structures, roads, traffic lights, traffic signs, road markings, etc. from the image data supplied from the camera 51. Furthermore, the recognition unit 73 may recognize the type of object around the vehicle 200 by performing recognition processing such as semantic segmentation.

[0216] For example, the recognition unit 73 can perform recognition processing of traffic rules around the vehicle 200 based on the map stored in the map information storage unit 23, the estimation result of the self-position by the self-position estimation unit 71, and the recognition result of the recognition unit 73 of objects around the vehicle 200. Through this processing, the recognition unit 73 can recognize the positions and states of traffic signals, the contents of traffic signs and road markings, the contents of traffic regulations, and lanes that can be traveled.

[0217] For example, the recognition unit 73 can perform a recognition process of the environment around the vehicle 200. The surrounding environment to be recognized by the recognition unit 73 may include weather, temperature, humidity, brightness, and road surface conditions.

[0218] The behavior planning unit 62 creates a behavior plan for the vehicle 200. For example, the behavior planning unit 62 creates the behavior plan by performing route planning and route following processing.

[0219] Global path planning is a process for planning a rough route from the start to the goal. This route planning is called trajectory planning, and also includes local path planning, which is a process for generating a trajectory that allows the vehicle 200 to proceed safely and smoothly in the vicinity of the vehicle 200, taking into account the motion characteristics of the vehicle 200 on the route planned by the route planning. Path planning may be classified as long-term path planning, and trajectory generation may be classified as short-term path planning or local path planning. A safety-priority path represents a concept similar to trajectory generation, short-term path planning, or local path planning.

[0220] Path following is a process of planning an operation for safely and accurately traveling along a route planned by a route plan within a planned time. The behavior planning unit 62 can, for example, calculate a target speed and a target angular velocity of the vehicle 200 based on the results of this path following process.

[0221] The operation control unit 63 controls the operation of the vehicle 200 in order to realize the action plan created by the action planning unit 62 .

[0222] For example, the operation control unit 63 controls the steering control unit 81, the brake control unit 82, and the drive control unit 83 included in the vehicle control unit 32 described later, to perform acceleration / deceleration control and direction control so that the vehicle 200 travels along the trajectory calculated by the trajectory plan. For example, the operation control unit 63 performs cooperative control with the aim of realizing ADAS functions such as collision avoidance or impact mitigation, following driving, vehicle speed maintenance driving, collision warning for the host vehicle, and lane departure warning for the host vehicle. For example, the operation control unit 63 performs cooperative control with the aim of automatic driving, which autonomously drives without driver operation.

[0223] The DMS 30 performs processes such as authenticating the driver and recognizing the driver's state based on the sensor data from the in-vehicle sensors 26 and input data input to the HMI 31 (described later). In this case, the driver's state to be recognized by the DMS 30 may include, for example, physical condition, level of alertness, level of concentration, level of fatigue, line of sight, level of intoxication, driving operation, and posture.

[0224] The DMS 30 may be configured to perform authentication processing for passengers other than the driver and recognition processing for the conditions of the passengers. Furthermore, for example, the DMS 30 may be configured to perform recognition processing for the conditions inside the vehicle based on sensor data from the in-vehicle sensor 26. Possible conditions inside the vehicle to be recognized include, for example, temperature, humidity, brightness, and odor.

[0225] The HMI 31 inputs various data and instructions, and presents various data to the driver and the like.

[0226] The following provides an overview of data input via the HMI 31. The HMI 31 includes input devices for a person to input data. The HMI 31 generates input signals based on data, instructions, and the like input via the input devices and supplies the signals to each component of the vehicle control system 11. The HMI 31 includes, as input devices, controls such as a touch panel, buttons, switches, and levers. The HMI 31 may also include input devices that allow information to be input by voice, gestures, or other means other than manual operation. Furthermore, the HMI 31 may use, as input devices, externally connected devices such as a remote control device using infrared or radio waves, or a mobile or wearable device compatible with the operation of the vehicle control system 11.

[0227] The presentation of data by the HMI 31 will be briefly described. The HMI 31 generates visual information, auditory information, and tactile information for the occupant or the outside of the vehicle. The HMI 31 also performs output control, controlling the output, output content, output timing, output method, etc. of each of the generated information. The HMI 31 generates and outputs, as visual information, information indicated by images or lights, such as an operation screen, a status display of the vehicle 200, a warning display, and a monitor image showing the situation around the vehicle 200. The HMI 31 also generates and outputs, as auditory information, information indicated by sounds, such as voice guidance, warning sounds, and warning messages. The HMI 31 also generates and outputs, as tactile information, information imparted to the occupant's sense of touch by, for example, force, vibration, movement, etc.

[0228] Examples of the output device from which the HMI 31 outputs visual information include a display device that presents visual information by displaying an image on its own, and a projector device that presents visual information by projecting an image. The display device may be a device that displays visual information within the field of view of the occupant, such as a head-up display, a transmissive display, or a wearable device with an AR (Augmented Reality) function, in addition to a display device having a normal display. The HMI 31 may also use display devices such as a navigation device, an instrument panel, a CMS (Camera Monitoring System), an electronic mirror, or a lamp provided in the vehicle 200 as output devices that output visual information.

[0229] As an output device for the HMI 31 to output auditory information, for example, an audio speaker, a headphone, or an earphone can be applied.

[0230] For example, a haptic element using haptic technology can be applied as an output device for outputting tactile information from the HMI 31. The haptic element is provided on a part of the vehicle 200 that an occupant comes into contact with, such as a steering wheel or a seat.

[0231] The vehicle control unit 32 controls each unit of the vehicle 200. The vehicle control unit 32 includes a steering control unit 81, a brake control unit 82, a drive control unit 83, a body system control unit 84, a light control unit 85, and a horn control unit 86.

[0232] The steering control unit 81 detects and controls the state of the steering system of the vehicle 200. The steering system includes, for example, a steering mechanism including a steering wheel, an electric power steering, etc. The steering control unit 81 includes, for example, a control unit such as an ECU that controls the steering system, and an actuator that drives the steering system.

[0233] The brake control unit 82 detects and controls the state of the brake system of the vehicle 200. The brake system includes, for example, a brake mechanism including a brake pedal, an antilock brake system (ABS), a regenerative brake mechanism, etc. The brake control unit 82 includes, for example, a control unit such as an ECU that controls the brake system.

[0234] The drive control unit 83 detects and controls the state of the drive system of the vehicle 200. The drive system includes, for example, an accelerator pedal, a drive force generating device for generating drive force such as an internal combustion engine or a drive motor, and a drive force transmission mechanism for transmitting the drive force to the wheels. The drive control unit 83 includes, for example, a control unit such as an ECU that controls the drive system.

[0235] The body system control unit 84 detects and controls the states of the body system systems of the vehicle 200. The body system systems include, for example, a keyless entry system, a smart key system, a power window device, a power seat, an air conditioning system, an airbag, a seat belt, a shift lever, etc. The body system control unit 84 includes, for example, a control unit such as an ECU that controls the body system systems.

[0236] The light control unit 85 detects and controls the states of various lights of the vehicle 200. Examples of lights to be controlled include headlights, backlights, fog lights, turn signals, brake lights, projections, and bumper displays. The light control unit 85 includes a control unit such as an ECU that controls the lights.

[0237] The horn control unit 86 detects and controls the state of the car horn of the vehicle 200. The horn control unit 86 includes, for example, a control unit such as an ECU that controls the car horn.

[0238] Fig. 12 is a diagram showing an example of a sensing area by the camera 51, radar 52, LiDAR 53, ultrasonic sensor 54, etc. of the external recognition sensor 25 in Fig. 11. Note that Fig. 12 schematically shows the vehicle 200 as seen from above, with the left end side being the front end (front) side of the vehicle 200 and the right end side being the rear end (rear) side of the vehicle 200.

[0239] Sensing area 201F and sensing area 201B show examples of sensing areas of the ultrasonic sensors 54. Sensing area 201F covers the periphery of the front end of the vehicle 200 with a plurality of ultrasonic sensors 54. Sensing area 201B covers the periphery of the rear end of the vehicle 200 with a plurality of ultrasonic sensors 54.

[0240] The sensing results in the sensing area 201F and the sensing area 201B are used, for example, for parking assistance for the vehicle 200.

[0241] Sensing area 202F to sensing area 202B show examples of sensing areas of a short-range or medium-range radar 52. Sensing area 202F covers a position farther in front of the vehicle 200 than sensing area 201F. Sensing area 202B covers a position farther in the rear of the vehicle 200 than sensing area 201B. Sensing area 202L covers the rear periphery of the left side of the vehicle 200. Sensing area 202R covers the rear periphery of the right side of the vehicle 200.

[0242] The sensing results in sensing area 202F are used, for example, to detect vehicles, pedestrians, and the like that are present in front of vehicle 200. The sensing results in sensing area 202B are used, for example, for a collision prevention function behind vehicle 200. The sensing results in sensing area 202L and sensing area 202R are used, for example, to detect objects in blind spots on the sides of vehicle 200.

[0243] Sensing area 203F to sensing area 203B show examples of sensing areas sensed by camera 51. Sensing area 203F covers a position farther in front of vehicle 200 than sensing area 202F. Sensing area 203B covers a position farther in the rear of vehicle 200 than sensing area 202B. Sensing area 203L covers the periphery of the left side of vehicle 200. Sensing area 203R covers the periphery of the right side of vehicle 200.

[0244] The sensing results in sensing area 203F can be used for, for example, recognition of traffic lights and traffic signs, lane departure prevention assistance systems, and automatic headlight control systems. The sensing results in sensing area 203B can be used for, for example, parking assistance and surround view systems. The sensing results in sensing area 203L and sensing area 203R can be used for, for example, surround view systems.

[0245] Sensing area 204 shows an example of the sensing area of ​​LiDAR 53. Sensing area 204 covers a position farther ahead of vehicle 200 than sensing area 203F. On the other hand, sensing area 204 has a narrower range in the left-right direction than sensing area 203F.

[0246] The sensing results in the sensing area 204 are used to detect objects such as surrounding vehicles, for example.

[0247] A sensing area 205 shows an example of the sensing area of ​​the long-range radar 52. The sensing area 205 covers a position further ahead of the vehicle 200 than the sensing area 204. On the other hand, the sensing area 205 has a narrower range in the left-right direction than the sensing area 204.

[0248] The sensing results in the sensing area 205 are used for, for example, adaptive cruise control (ACC), emergency braking, collision avoidance, and the like.

[0249] The sensing areas of the cameras 51, radar 52, LiDAR 53, and ultrasonic sensors 54 included in the external recognition sensor 25 may have various configurations other than those shown in FIG. 12 . Specifically, the ultrasonic sensors 54 may also sense the sides of the vehicle 200, and the LiDAR 53 may sense the rear of the vehicle 200. The installation positions of the sensors are not limited to the above-described examples. The number of each sensor may be one or more.

[0250] (3-2. Other) Of the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0251] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0252] Furthermore, the above-described embodiments and modified examples can be combined as appropriate to the extent that the processing content is not inconsistent. Furthermore, in the above-described embodiments, an automobile is used as an example of a moving body, but the information processing of the present disclosure can also be applied to moving bodies other than automobiles. For example, the moving body may be a small vehicle such as a motorcycle or a tricycle, a large vehicle such as a bus or a truck, or an autonomous moving body such as a robot or a drone. Furthermore, the vehicle 200 does not necessarily have to be integrated with the moving body, but may be a cloud server or the like that acquires information from the moving body via the network N and determines the removal range based on the acquired information.

[0253] Furthermore, the effects described in this specification are merely examples and are not limiting, and other effects may also be present.

[0254] (4. Effects of the vehicle control system according to the present disclosure) As described above, the vehicle control system according to the present disclosure (vehicle control system 100) has an acquisition unit (acquisition unit 131), a driving control unit (driving control unit 132), a detection unit (detection unit 133), and a restriction unit (restriction unit 134). The acquisition unit acquires environmental information indicating the environment around the vehicle. The driving control unit controls the autonomous driving of the vehicle (vehicle 200) based on the environmental information acquired by the acquisition unit. The detection unit detects manually driven vehicles traveling around the vehicle. If, as a result of detection by the detection unit, a manually driven vehicle is traveling around the vehicle, the restriction unit restricts the driving conditions for autonomous driving. This allows the vehicle control system to prevent surrounding manually driven vehicles from getting into dangerous situations due to the effects of autonomous driving.

[0255] Furthermore, when manually driven vehicles are traveling around the vehicle, the restriction unit derives driving conditions under which the manually driven vehicle can travel safely, based on the environmental information acquired by the acquisition unit, and restricts the driving conditions for autonomous driving to the derived driving conditions. As a result, the vehicle control system restricts the driving conditions for autonomous driving to safe driving conditions for manually driven vehicles, thereby preventing surrounding manually driven vehicles from being put into dangerous situations due to the effects of autonomous driving.

[0256] Furthermore, when manually driven vehicles are traveling around the vehicle, the restriction unit determines the weather from the environmental information acquired by the acquisition unit, derives driving conditions under which the manually driven vehicle can travel safely based on the determined weather, and restricts the driving conditions for autonomous driving to the derived driving conditions. As a result, the vehicle control system restricts the driving conditions for autonomous driving to safe driving conditions for manually driven vehicles based on the weather, thereby preventing surrounding manually driven vehicles from being put into dangerous situations due to the effects of autonomous driving.

[0257] The limiting unit also derives upper limits of driving conditions under which the manually driven vehicle can be driven safely and limits the driving conditions for autonomous driving to within the derived upper limits. As a result, the vehicle control system limits the driving conditions for autonomous driving to within the upper limits of driving conditions under which the manually driven vehicle can be driven safely, thereby preventing surrounding manually driven vehicles from being put into dangerous situations due to the effects of autonomous driving.

[0258] The limiting unit also derives a speed at which the manually-driven vehicle can travel safely and limits the autonomously-driven vehicle's travel speed to within the derived speed. This allows the vehicle control system to limit the autonomously-driven vehicle's travel speed to a speed at which the manually-driven vehicle can travel safely, thereby preventing surrounding manually-driven vehicles from being put into dangerous situations due to the effects of autonomous driving.

[0259] In addition, the limiting unit limits the driving conditions for the autonomous driving when manually driven vehicles are traveling in front of, behind, on the left or right of the vehicle, thereby enabling the vehicle control system to prevent the manually driven vehicles in front of, behind, on the left or right of the vehicle from getting into a dangerous situation due to the effects of the autonomous driving.

[0260] The limiting unit also evaluates the behavior of the manually driven vehicle and relaxes the restrictions on driving conditions within a range in which the behavior of the manually driven vehicle is stable. This allows the vehicle control system to prevent the manually driven vehicle from getting into dangerous situations due to the effects of autonomous driving, even when the restrictions on driving conditions are relaxed.

[0261] Furthermore, when a manually driven vehicle is following the vehicle, the restriction unit evaluates the driving stability of the following manually driven vehicle based on the environmental information acquired by the acquisition unit, and restricts the driving conditions for autonomous driving if the stability is low. This allows the vehicle control system to prevent the manually driven vehicle following the vehicle from getting into a dangerous situation.

[0262] Furthermore, if the distance between the vehicle and the following vehicle is shorter than a predetermined allowable distance, the driving control unit performs sudden braking within a range that will avoid a rear-end collision. This allows the vehicle control system to alert the following vehicle that the distance between the vehicle and the following vehicle is short.

[0263] Furthermore, when the distance between the vehicle and the following vehicle is shorter than a predetermined allowable distance, the driving control unit changes the driving conditions of the autonomous driving so that the following vehicle applies brakes more gently. This allows the vehicle control system to prevent a rear-end collision from the following vehicle.

[0264] The driving control unit also changes the driving conditions of the autonomous driving system to apply the brakes earlier, which allows the vehicle control system to prevent rear-end collisions with following vehicles.

[0265] Furthermore, when there is another vehicle nearby and platooning with that vehicle is possible, the driving control unit pairs with that vehicle to establish an electronic connection and controls the autonomous driving of the vehicle to platoon with that vehicle. The restriction unit evaluates the risk of disconnecting the pairing and restricts the driving conditions of the autonomous driving according to the risk. This allows the vehicle control system to prevent the other vehicle from being put into a dangerous situation when the pairing with the other vehicle is disconnected.

[0266] (5. Hardware Configuration) The information devices such as the vehicle control system according to the present disclosure described above are realized by a computer 1000 configured as shown in FIG. 13, for example. FIG. 13 is a hardware configuration diagram showing an example of the computer 1000 that realizes the functions of the vehicle control system according to the present disclosure. The vehicle control system according to the present disclosure will be described below using a vehicle 200 according to an embodiment as an example. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM (Read Only Memory) 1300, a HDD (Hard Disk Drive) 1400, a communication interface 1500, and an input / output interface 1600. The components of the computer 1000 are connected by a bus 1050.

[0267] The CPU 1100 operates and controls each component based on programs stored in the ROM 1300 or the HDD 1400. For example, the CPU 1100 loads the programs stored in the ROM 1300 or the HDD 1400 into the RAM 1200 and executes processing corresponding to the various programs.

[0268] The ROM 1300 stores boot programs such as a Basic Input Output System (BIOS) executed by the CPU 1100 when the computer 1000 is started, and programs that depend on the hardware of the computer 1000 .

[0269] HDD 1400 is a computer-readable recording medium that non-temporarily records programs executed by CPU 1100 and data used by such programs. Specifically, HDD 1400 is a recording medium that records a vehicle control program according to the present disclosure, which is an example of program data 1450.

[0270] The communication interface 1500 is an interface for connecting the computer 1000 to an external network 1550 (e.g., the Internet). For example, the CPU 1100 receives data from other devices and transmits data generated by the CPU 1100 to other devices via the communication interface 1500.

[0271] The input / output interface 1600 is an interface for connecting the input / output device 1650 and the computer 1000. For example, the CPU 1100 receives data from an input device such as a keyboard or a mouse via the input / output interface 1600. The CPU 1100 also transmits data to an output device such as a display, a speaker, or a printer via the input / output interface 1600. The input / output interface 1600 may also function as a media interface for reading programs and the like recorded on a predetermined recording medium. Examples of media include optical recording media such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disc), magneto-optical recording media such as an MO (Magneto-Optical Disk), tape media, magnetic recording media, and semiconductor memories.

[0272] For example, when computer 1000 functions as vehicle 200 according to the embodiment, CPU 1100 of computer 1000 executes a vehicle control program loaded onto RAM 1200 to realize functions of control unit 130, etc. Also, HDD 1400 stores the vehicle control program according to the present disclosure and data in storage unit 120. Note that CPU 1100 reads and executes program data 1450 from HDD 1400, but as another example, these programs may be acquired from another device via external network 1550.

[0273] The present technology can also be configured as follows. (1) A vehicle control system having an acquisition unit that acquires environmental information indicating the environment around a vehicle, a driving control unit that controls autonomous driving of the vehicle based on the environmental information acquired by the acquisition unit, a detection unit that detects manually driven vehicles traveling around the vehicle, and a restriction unit that restricts driving conditions for autonomous driving when the detection by the detection unit determines that manually driven vehicles are traveling around the vehicle. (2) The vehicle control system described in (1) above, in which, when manually driven vehicles are traveling around the vehicle, the restriction unit derives driving conditions under which the manually driven vehicle can travel safely based on the environmental information acquired by the acquisition unit, and restricts driving conditions for autonomous driving to the derived driving conditions. (3) The vehicle control system described in (1) or (2) above, in which, when manually driven vehicles are traveling around the vehicle, the restriction unit determines weather from the environmental information acquired by the acquisition unit, derives driving conditions under which the manually driven vehicle can travel safely based on the determined weather, and restricts driving conditions for autonomous driving to the derived driving conditions. (4) The vehicle control system according to (2) or (3), wherein the restriction unit derives upper limits of driving conditions under which the manually driven vehicle can travel safely, and restricts the driving conditions for automated driving to within the derived upper limits. (5) The vehicle control system according to any one of (2) to (4), wherein the restriction unit derives a speed at which the manually driven vehicle can travel safely, and restricts the driving speed for automated driving to within the derived speed. (6) The vehicle control system according to any one of (1) to (5), wherein the restriction unit restricts the driving conditions for automated driving when manually driven vehicles travel in front of, behind, on the left or right of the vehicle. (7) The vehicle control system according to any one of (1) to (6), wherein the restriction unit evaluates the behavior of the manually driven vehicle, and relaxes the restrictions on the driving conditions within a range in which the behavior of the manually driven vehicle is stable. (8) The vehicle control system according to any one of (1) to (7), wherein, when a manually driven vehicle is driving behind the vehicle, the restriction unit evaluates the driving stability of the following manually driven vehicle based on the environmental information acquired by the acquisition unit, and restricts the driving conditions for automatic driving if the stability is low.(9) The vehicle control system according to any one of (1) to (8), wherein the driving control unit implements sudden braking control within a range that avoids a rear-end collision when the inter-vehicle distance between the vehicle and a following vehicle following the vehicle is shorter than a predetermined allowable distance. (10) The vehicle control system according to any one of (1) to (9), wherein the driving control unit changes the driving conditions of the autonomous driving so that the following vehicle applies the brakes more gradually when the inter-vehicle distance between the vehicle and a following vehicle following the vehicle is shorter than a predetermined allowable distance. (11) The vehicle control system according to (10), wherein the driving control unit changes the driving conditions of the autonomous driving so that the following vehicle applies the brakes earlier. (12) The vehicle control system according to any one of (1) to (11), wherein, when another vehicle is present in the vicinity and platooning with the vehicle is possible, the driving control unit pairs with the vehicle to establish an electronic connection and controls the autonomous driving of the vehicle to platoon with the vehicle, and the restriction unit evaluates a risk of disconnection of the pairing and restricts the driving conditions of the autonomous driving in accordance with the risk. (13) The vehicle control system according to any one of (1) to (12), wherein, when another vehicle is present in the vicinity and is confirmed to be following the vehicle during autonomous driving of the vehicle by the driving control unit, the restriction unit derives an upper limit of driving conditions under which the other surrounding vehicles can travel safely and restricts the driving conditions of the autonomous driving to within the derived upper limit. (14) A vehicle control method including: a computer acquiring environmental information indicating the environment around a vehicle, controlling automatic driving of the vehicle based on the acquired environmental information, detecting manually driven vehicles traveling around the vehicle, and restricting driving conditions for automatic driving when the result of the detection shows that manually driven vehicles are traveling around the vehicle. (15) A vehicle control program causing a computer to function as: an acquisition unit that acquires environmental information indicating the environment around the vehicle, a driving control unit that controls automatic driving of the vehicle based on the environmental information acquired by the acquisition unit, a detection unit that detects manually driven vehicles traveling around the vehicle, and a restriction unit that restricts driving conditions for automatic driving when the result of detection by the detection unit shows that manually driven vehicles are traveling around the vehicle.

[0274] REFERENCE SIGNS LIST 1 Information processing system 40 Communication base station 50 Terminal 200 Vehicle 110 Communication unit 120 Storage unit 130 Control unit 131 Acquisition unit 132 Driving control unit 133 Detection unit 134 Restriction unit 140 Detection unit 145 Display unit 150 Cloud server

Claims

1. A vehicle control system having an acquisition unit that acquires environmental information indicating the environment around a vehicle; a driving control unit that controls automatic driving of the vehicle based on the environmental information acquired by the acquisition unit; a detection unit that detects manually driven vehicles traveling around the vehicle; and a restriction unit that restricts the driving conditions for automatic driving when a manually driven vehicle is traveling around the vehicle as a result of detection by the detection unit.

2. A vehicle control system as described in claim 1, wherein, when a manually driven vehicle is driving around the vehicle, the restriction unit derives driving conditions under which the manually driven vehicle can drive safely based on the environmental information acquired by the acquisition unit, and restricts the driving conditions for automated driving to the derived driving conditions.

3. A vehicle control system as described in claim 1, wherein the restriction unit, when a manually driven vehicle is driving around the vehicle, determines the weather from the environmental information acquired by the acquisition unit, derives driving conditions under which the manually driven vehicle can drive safely based on the determined weather, and restricts the driving conditions for automated driving to the derived driving conditions.

4. A vehicle control system as described in claim 2, wherein the limiting unit derives upper limits of driving conditions under which the manually driven vehicle can be driven safely, and limits the driving conditions for automated driving to within the derived upper limits.

5. A vehicle control system according to claim 2, wherein the limiting unit derives a speed at which the manually driven vehicle can travel safely, and limits the travel speed of the automatically driven vehicle to within the derived speed.

6. The vehicle control system according to claim 1, wherein the restriction unit restricts the driving conditions of the automatic driving when the manually driven vehicle is driving in front of, behind, to the left or right of the vehicle.

7. The vehicle control system according to claim 1, wherein the restriction unit evaluates the behavior of the manually driven vehicle and relaxes restrictions on driving conditions within a range in which the behavior of the manually driven vehicle is stable.

8. A vehicle control system as described in claim 1, wherein, when a manually driven vehicle is driving following the vehicle, the restriction unit evaluates the driving stability of the following manually driven vehicle based on the environmental information acquired by the acquisition unit, and restricts the driving conditions for automatic driving if the stability is low.

9. A vehicle control system as described in claim 1, wherein the driving control unit performs sudden braking control within a range that avoids a rear-end collision when the inter-vehicle distance between the vehicle and a following vehicle following the vehicle is shorter than a predetermined allowable distance.

10. A vehicle control system as described in claim 1, wherein the driving control unit changes the driving conditions of the automatic driving so that the braking of the following vehicle is gentler when the inter-vehicle distance between the vehicle and the following vehicle is shorter than a predetermined allowable distance.

11. The vehicle control system according to claim 10, wherein the driving control unit changes the driving conditions of the automatic driving so that the brakes are applied at an earlier timing.

12. The vehicle control system of claim 1, wherein the driving control unit, when there is another vehicle nearby and platooning with the vehicle is possible, pairs with the vehicle to establish an electronic connection and controls the autonomous driving of the vehicle to platoon with the vehicle, and the restriction unit evaluates the risk of disconnecting the pairing and restricts the autonomous driving conditions according to the risk.

13. A vehicle control system as described in claim 1, wherein, when another vehicle is present in the vicinity and is confirmed to be following the other vehicle during automatic driving of the vehicle by the driving control unit, the restriction unit derives an upper limit of the driving conditions under which the other surrounding vehicles can drive safely, and restricts the driving conditions for automatic driving to within the derived upper limit.

14. A vehicle control method including: a computer acquiring environmental information indicating the environment around a vehicle; controlling automatic driving of the vehicle based on the acquired environmental information; detecting manually driven vehicles traveling around the vehicle; and restricting the driving conditions for automatic driving if the detection result indicates that manually driven vehicles are traveling around the vehicle.

15. A vehicle control program that causes a computer to function as: an acquisition unit that acquires environmental information indicating the environment around a vehicle; a driving control unit that controls automatic driving of the vehicle based on the environmental information acquired by the acquisition unit; a detection unit that detects manually driven vehicles traveling around the vehicle; and a restriction unit that restricts the driving conditions for automatic driving when, as a result of detection by the detection unit, a manually driven vehicle is traveling around the vehicle.

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