Control method and control unit for controlling an intelligent vehicle

Abstract

The present application relates to a control method and a control unit for controlling an intelligent vehicle. The control method comprises the following steps: identifying the identity of a driver after the intelligent vehicle is started; determining the driving habit of the driver associated with the identity of the driver; detecting the emotion of the driver in real time during the driving of the intelligent vehicle; comparing the current emotion of the driver with the previous emotion, if the emotion of the driver becomes worse and the degree of emotion change is above a preset degree, the next step is performed; otherwise, return to the previous step; determining an emotion intervention plan according to the degree of emotion change of the driver; adjusting the emotion intervention plan according to the vehicle surrounding situation detected in real time during the driving of the intelligent vehicle, and / or adjusting the emotion intervention plan according to the driving habit of the driver to generate an emotion intervention scheme; and controlling the intelligent vehicle according to the emotion intervention scheme to intervene the emotion of the driver. The control unit is configured to perform the control method.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation vehicles, and more specifically, to a control method and control unit for controlling intelligent vehicles. Background Technology

[0002] With the increasing maturity of autonomous or intelligent driving technologies, intelligent mobility spaces (intelligent vehicles, intelligent multi-purpose vehicles, etc.) are becoming increasingly popular among consumers. This is because they not only alleviate the fatigue of passengers, especially drivers, during journeys, but also provide a pleasant and comfortable driving experience through various human-machine interaction facilities. Therefore, the user experience has become one of the key decision-making factors for consumers when purchasing intelligent mobility spaces. However, with the promotion and popularization of intelligent mobility spaces, the scenarios in which users encounter sudden dangers and complex road conditions within these spaces are inevitably increasing. Therefore, how to quickly soothe users' emotions after experiencing negative emotions due to sudden dangers and complex road conditions has become one of the current development directions for intelligent mobility spaces. However, existing intelligent mobility spaces and their emotional soothing solutions cannot soothe users' emotions in a real-time, proactive, and effective manner, and they cannot soothe users' emotions individually. This inevitably leads to users forming a deep memory of negative user experiences. Once users form a deep memory of negative user experiences, they are likely to actively spread it, resulting in a negative image of emerging intelligent mobility spaces among consumers, which negatively impacts the promotion and popularization of intelligent mobility spaces.

[0003] Therefore, there is an urgent need in this field for a technical solution that can soothe users' emotions in a real-time, proactive, effective, and individualized manner after they encounter dangerous scenarios and negative experiences. Summary of the Invention

[0004] To address the problems in the prior art, the present invention proposes a control method for controlling intelligent vehicles, comprising the following steps:

[0005] S101: Identify the driver of the intelligent vehicle after the intelligent vehicle is started;

[0006] S110: Determine the driver's driving habits in association with the driver's identity;

[0007] S201: Detect the driver's emotions in real time during the operation of the intelligent vehicle;

[0008] S202: Compare the driver's current emotion with the previous emotion. If the driver's emotion worsens and the degree of emotion change is above a preset level, then proceed to step S203; otherwise, return to step S201.

[0009] S203: Determine an emotional intervention plan based on the degree of emotional change of the driver;

[0010] S204: Adjust the emotion intervention plan according to the driver's driving habits to generate an emotion intervention program; and

[0011] S205: Control the intelligent vehicle according to the emotion intervention scheme to intervene in the driver's emotions.

[0012] Therefore, the control method according to the present invention can determine the driver's driving habits based on the driver's identity and determine an emotional intervention plan based on the degree of the driver's emotional changes. Specifically, it can adjust the emotional intervention plan according to the driving habits to generate an emotional intervention scheme to be executed by the vehicle. In this configuration, the generated emotional intervention scheme is customized for the driver and conforms to their personality. For example, if the driver's driving habits are relatively aggressive, then the driver can be considered to have strong psychological resilience and be easy to calm down; therefore, a weaker emotional intervention scheme can be generated to avoid excessive intervention in the driver's emotions. Conversely, if the driver's driving habits are relatively conservative, then the driver can be considered to have poor psychological resilience and be difficult to calm down; therefore, a stronger emotional intervention scheme can be generated to fully intervene in the driver's emotions. In short, the emotional intervention scheme generated by the control method according to the present invention is not only related to the degree of the driver's emotional changes but also to the driver's personality. Therefore, the control method according to the present invention can provide personalized and efficient emotional intervention for the driver, which helps the driver obtain a good driving experience.

[0013] The following describes several optional embodiments of the control method according to the present invention. It should be noted that new embodiments obtained by combining these embodiments in any way also fall within the protection scope of the present invention.

[0014] According to an optional embodiment of the present invention, step S204 is as follows:

[0015] The emotional intervention plan is adjusted based on the driver's driving habits and the real-time measurement of the vehicle's surroundings during the vehicle's operation to generate the emotional intervention scheme.

[0016] According to an optional embodiment of the present invention, step S110 includes the following sub-steps:

[0017] S102: Confirm the driving mode of the intelligent vehicle. If the driving mode is manual driving mode, proceed to step S111; if the driving mode is automatic or intelligent driving mode, proceed to steps S121-S123.

[0018] S111: Determine the driver's driving habits based on the driver's operational behavior measured during the operation of the intelligent vehicle, and store the driver's driving habits in association with the driver's identity;

[0019] S121: Query historical records to obtain the driver's driving habits based on the driver's identity;

[0020] S122: Obtain an automatic or intelligent driving plan based on the driver's driving habits; and

[0021] S123: Control the driving of the intelligent vehicle according to the automatic or intelligent driving scheme.

[0022] According to an optional embodiment of the present invention, step S123 further comprises:

[0023] The automatic or intelligent driving scheme is adjusted based on the real-time measurement of the vehicle's surroundings and / or the driver's mood during the vehicle's operation.

[0024] According to an optional embodiment of the present invention, the driver's driving habits are also associated with the vehicle's surrounding conditions and / or the driver's emotions as measured in real time during the operation of the intelligent vehicle.

[0025] According to an optional embodiment of the present invention, the vehicle's surrounding conditions include one or more of the following: time, weather, road conditions, and route during the intelligent vehicle's operation.

[0026] According to an optional embodiment of the present invention, step S205 is as follows:

[0027] According to the emotion intervention scheme, control one or more of the intelligent vehicle's voice system, lighting system, vehicle-machine interaction system, seat system, and chassis system to intervene in the driver's emotions.

[0028] According to an optional embodiment of the present invention, step S205 is as follows:

[0029] The driving mode of the intelligent vehicle is confirmed. If the driving mode is automatic or intelligent driving mode, the automatic or intelligent driving scheme is adjusted according to the emotion intervention scheme.

[0030] According to an optional embodiment of the present invention, the control method further includes the following step after step S205:

[0031] S301: Adjust the emotion intervention plan based on the driver's emotions measured before step S205, the emotion intervention plan, and the driver's emotions measured after step S205; and / or

[0032] S302: Store the following data:

[0033] The driving parameters of the intelligent vehicle, the driver's emotions, and the emotion intervention plan measured before step S205; and

[0034] The driving parameters of the intelligent vehicle, the driver's emotions, and the emotion intervention plan are measured after step S205.

[0035] Similarly, in order to address the problems in the prior art, the present invention also proposes a control unit for controlling an intelligent vehicle, the control unit including a memory, a processor and a computer program stored in the memory, wherein the processor is configured to execute the computer program to implement the steps of the control method as described above.

[0036] The present invention may be embodied in the illustrative embodiments shown in the accompanying drawings. However, it should be noted that the drawings are merely illustrative, and any variations contemplated under the teachings of this invention should be considered to be included within the scope of this invention. Attached Figure Description

[0037] The accompanying drawings illustrate exemplary embodiments of the invention. These drawings should not be construed as necessarily limiting the scope of the invention, wherein:

[0038] Figure 1 This is a schematic flowchart of a control method according to an embodiment of the present invention; and

[0039] Figure 2 This is a schematic flowchart of a control method according to another embodiment of the present invention. Detailed Implementation

[0040] Further features and advantages of the invention will become more apparent from the following description with reference to the accompanying drawings. Exemplary embodiments of the invention are illustrated in the drawings, and the figures are not necessarily drawn to scale. However, the invention can be implemented in many different forms and should not be construed as necessarily limited to the exemplary embodiments shown herein. Rather, these exemplary embodiments are provided merely to illustrate the invention and to convey its spirit and essence to those skilled in the art.

[0041] This invention aims to provide a control method for controlling intelligent vehicles and a control unit for executing the control method. This control method can select a suitable automatic or intelligent driving scheme for the driver, and can control various facilities on the intelligent vehicle to soothe the driver after the driver's emotions deteriorate (e.g., due to startling events such as sudden lane changes or sudden braking). Specifically, the control method according to the invention can determine the driver's driving habits (also known as driving style) based on the driver's identity and select an automatic or intelligent driving scheme accordingly. Therefore, the intelligent vehicle can automatically drive or assist the driver in driving or controlling the intelligent vehicle in a mode that better suits the driver's driving habits, thereby providing a better user experience during driving. Furthermore, the control method according to the invention can also soothe the driver's emotions based on their driving habits after the driver's emotions deteriorate, thus timely and effectively calming the driver's emotions after a deterioration, thereby reducing the negative impact of sudden events on the driver and further ensuring a good driving experience. Therefore, the control method according to the invention enables intelligent vehicles to provide more personalized services to the driver throughout the entire journey and thus optimizes the driver's user experience throughout the entire journey.

[0042] The various embodiments of the control method for an intelligent vehicle according to the present invention are described in detail below with reference to the accompanying drawings.

[0043] refer to Figure 1 A schematic flowchart of a control method according to an embodiment of the present invention is shown. Figure 1 As shown, the control method may include the following steps:

[0044] Step S101: Identify the driver's identity after the intelligent vehicle is started. For example, an iris recognition system can be installed in the intelligent vehicle. This system can collect the driver's iris, and the driver's identity can be determined by comparing the collected iris with a stored iris. Alternatively, a seat height collection system and a weight collection system can be installed in the intelligent vehicle. The seat height collection system can collect the driver's height while seated in the vehicle, and the weight collection system can collect the driver's weight. The driver's identity can also be determined by comparing the collected height and weight with stored height and weight data. Although the specific methods for determining the driver's identity have been described above, those skilled in the art will understand that other methods (e.g., facial recognition, fingerprint recognition, etc.) can be used to determine the driver's identity, and these should all be considered to fall within the protection scope of this invention.

[0045] Step S102: Determine the driving mode of the intelligent vehicle. If the driving mode is manual driving mode, proceed to step S111; if the driving mode is automatic or intelligent driving mode, proceed to steps S121-S123. For example, the driving mode can be manually set by the driver when the intelligent vehicle is started.

[0046] As described above, in manual driving mode, step S111 is executed: the driver's operational behavior is detected, and the driver's driving habits (also known as driving style) are determined based on the operational behavior, and the driving habits are stored in association with the driver's identity. Specifically, the driver's driving habits can be analyzed and determined through parameters such as maximum speed, following distance, and lane change frequency. For example, if the driver's maximum speed is high, lane change frequency is high, and following distance is close, then it can be determined that the driver has a more aggressive driving habit; conversely, if the driver's maximum speed is low, lane change frequency is low, and following distance is far, then it can be determined that the driver has a more conservative driving habit.

[0047] In particular, a driver can have multiple driving habits; in other words, a driver's driving habits can include multiple sub-driving habits. These sub-driving habits are not only associated with the driver's identity, but also further associated with specific additional information.

[0048] For example, a driver's driving habits can include multiple sub-driving habits associated with vehicle surroundings (i.e., objective parameters) such as time, weather, road conditions, route, and scenario (e.g., commuting to work, commuting to work, and traveling for leisure). For instance, sub-driving habits for leisure trips might be relatively conservative compared to sub-driving habits for commuting to work; sub-driving habits for sunny days might be relatively conservative compared to sub-driving habits for cloudy days, and so on. To determine these sub-driving habits, step S111 involves detecting not only the driver's operational behavior but also vehicle surroundings such as time, weather, road conditions, and route to determine driving habits under these vehicle surrounding conditions, i.e., sub-driving habits associated with these vehicle surrounding conditions. This makes the driving habits actually a set associated with the driver's identity, including multiple sub-driving habits associated with these vehicle surrounding conditions. Under this configuration, different driving habits of the same driver under different vehicle surrounding conditions are taken into account.

[0049] For example, a driver's driving habits may include multiple sub-driving habits associated with different emotions (i.e., subjective parameters) such as the driver's tension or relaxation. For instance, sub-driving habits under a relaxed state may be relatively conservative compared to sub-driving habits under tension. To determine these sub-driving habits, step S111 involves detecting not only the driver's operational behavior but also the driver's emotions to determine the driving habits under these emotions, i.e., the sub-driving habits associated with these emotions. This makes the driving habits actually a set associated with the driver's identity, including multiple sub-driving habits associated with the driver's different emotions. Under this configuration, different driving habits of the same driver under different emotions are taken into account.

[0050] As described above, in automatic or intelligent driving mode, step S121 is executed: querying historical records to obtain the driver's driving habits based on the driver's identity. These driving habits can be determined by analyzing the driver's operational behavior when the driver manually operates the intelligent vehicle and stored in association with the driver's identity. That is, they can be determined during step S111 and stored as historical records in the vehicle's or cloud's memory. During step S121, the driving habits associated with the driver's identity can be obtained by querying these historical records.

[0051] Specifically, when the driving habits include multiple sub-driving habits associated with vehicle surrounding conditions (i.e., objective parameters) such as time, weather, road conditions, route, and scenario (e.g., on the way to work, on the way home, on the way to travel), step S121 further includes: detecting vehicle surrounding conditions such as time, weather, road conditions, route, and scenario when the intelligent vehicle starts, and obtaining or selecting the corresponding sub-driving habits from the driving habits based on the detected vehicle surrounding conditions.

[0052] In particular, when the driving habits include multiple sub-driving habits associated with different emotions (i.e., subjective parameters) of the driver, step S121 further comprises: detecting, for example, the driver's emotions when the intelligent vehicle is started, and obtaining or selecting the corresponding sub-driving habits from the driving habits based on the detected driver emotions.

[0053] Step S122: Obtain an automatic or intelligent driving scheme based on the driver's driving habits (specifically, sub-driving habits), or in other words, select the automatic or intelligent driving scheme that matches (or best matches) the driver's driving habits (specifically, sub-driving habits) from a variety of automatic or intelligent driving schemes. Specifically, each automatic or intelligent driving scheme causes the intelligent vehicle to drive according to corresponding driving parameters (e.g., maximum speed, following distance, lane change frequency, seat angle, seat belt tension, chassis tuning parameters, interior lighting, vehicle interface display, voice broadcast parameters, etc.). Specifically, depending on the different driving parameters, the intelligent vehicle can have multiple automatic or intelligent driving schemes, each corresponding to a set of driving parameters. For example, an aggressive automatic or intelligent driving scheme has a higher maximum speed, a smaller following distance, and a higher lane change frequency when appropriate, thus suitable for drivers with aggressive driving habits, while a conservative automatic or intelligent driving scheme has a lower maximum speed, a larger following distance, and a lower lane change frequency, thus suitable for drivers with conservative driving habits. It's worth noting that different autonomous or intelligent driving schemes can essentially be considered as control units (e.g., onboard controllers, remote controllers, cloud controllers, etc.) controlling the driving habits of autonomous or assisted driving intelligent vehicles. Therefore, by selecting an autonomous or intelligent driving scheme that matches the driver's driving habits, the autonomous or intelligent driving of the intelligent vehicle can better align with those habits. For example, in autonomous driving mode, the intelligent vehicle can be driven or controlled automatically based on the driver's driving habits; in intelligent driving mode, the intelligent vehicle can be assisted in driving or controlling based on the driver's driving habits, thus providing a more comfortable driving experience.

[0054] Step S123: Control the driving of the intelligent vehicle according to the automatic or intelligent driving scheme.

[0055] Specifically, during the operation of the intelligent vehicle, factors such as time, weather, road conditions, route, scene, and surrounding conditions (i.e., objective parameters) and / or the driver's emotions (i.e., subjective parameters) are monitored in real time. The automatic or intelligent driving scheme is adjusted based on these factors. For example, if worsening road conditions are detected (e.g., increased congestion), the maximum speed, following distance, and lane-change frequency of the automatic or intelligent driving scheme will be reduced. If worsening weather is detected (e.g., rain or snow), the maximum speed will be reduced, following distance will be increased, and lane-change frequency will be reduced. If the driver's emotions worsen or deteriorate (e.g., increased tension), the maximum speed, following distance, and lane-change frequency will be reduced. Therefore, this configuration allows the intelligent vehicle to operate according to a real-time adjusted automatic or intelligent driving scheme. The introduction of this configuration improves driving safety and helps alleviate driver anxiety, thereby further contributing to providing and maintaining a positive driving experience.

[0056] As described above, regardless of whether it is in manual driving mode or automatic or intelligent driving mode, the control method according to the present invention can determine the driver's driving habits associated with their identity (in particular, the driver's sub-driving habits associated with their identity, specific vehicle surroundings, and / or emotions), and can ensure the operation of the intelligent vehicle. Therefore, steps S102, S111, and S121-S123 can be uniformly summarized into step S110: determining the driving habits associated with the driver's identity. After determining the driver's driving habits and ensuring the operation of the intelligent vehicle in step S110, the control method according to the present invention may further include the following steps.

[0057] Step S201: Real-time detection of the driver's emotions during the operation of the intelligent vehicle. Specifically, the driving parameters of the intelligent vehicle (e.g., longitudinal speed, longitudinal acceleration, longitudinal jerk, lateral speed, lateral acceleration, lateral jerk, vehicle offset from the center of the lane, etc.) and / or the driver's behavioral parameters (e.g., facial expressions, blinking frequency, gaze position, takeover driving, vehicle system function operation, body movements, voice commands, etc.) can be detected in real time during the operation of the intelligent vehicle, and the driver's emotions can be determined based on the detected driving parameters of the intelligent vehicle and / or the driver's behavioral parameters. Specifically, the driving parameters and / or the behavioral parameters can be input into an emotion assessment algorithm model stored in the vehicle controller or cloud server, and the emotion assessment algorithm model can be used to generate a signal indicating the driver's emotions. In particular, the value of this signal can reflect the driver's emotional state; for example, the better the driver's emotional state, the larger the signal value; conversely, the worse the driver's emotional state, the smaller the signal value. For example, if the lateral speed, lateral acceleration, and / or lateral jerk of an intelligent vehicle suddenly increase, it can be assumed that the vehicle intends to, is, or has already made an emergency lane change. This is likely to cause driver anxiety, especially in automatic or intelligent driving modes, potentially leading to a worsening of the driver's mood or even fright. This can generate a smaller signal indicating the driver's emotional fluctuations, with the signal decreasing as the lateral speed, lateral acceleration, and / or lateral jerk increase. Similarly, if behaviors such as frowning, clutching the head, or shouting are detected, a smaller signal indicating the driver's emotional state can also be generated. These behaviors are likely caused by a worsening of the driver's mood, and the magnitude of this signal can be related to the amplitude of the aforementioned parameters; for example, a larger frown, a more pronounced head-clutching gesture, or a louder shout results in a smaller signal. However, it should be noted that this signal is only intended to reflect the driver's emotional state through its numerical value and the degree of emotional fluctuation through its change in magnitude; therefore, the specific proportional relationships mentioned above are not mandatory.

[0058] Step S202: Compare the driver's current emotion with the previous emotion (specifically, compare the current signal indicating the emotion with the previous signal indicating the emotion). If the driver's emotion worsens (i.e., the current emotion is worse than the previous emotion, specifically, the signal indicating the emotion decreases) and the degree of emotion change (specifically, the amount of change in the signal indicating the emotion) is above a preset level, then the driver's emotion is considered to be fluctuating significantly, and their emotion is unlikely to recover to normal on its own or will take a long time. Therefore, step S203 is executed to intervene in the emotion and quickly soothe the driver's emotions. Otherwise, if the driver's emotion does not worsen, or the degree of emotion change is below the preset level, then the driver's emotion is considered not to have worsened or to have fluctuated slightly. Therefore, their emotion does not require intervention or can quickly recover to normal on its own. Therefore, no intervention is taken, and the process returns to step S201 to continue monitoring the driver's emotions. In this text, a worsening of emotion (also known as emotional deterioration) can be understood as a change in the driver's emotion toward negative emotions such as anxiety, tension, or anger.

[0059] Step S203: Determine an emotion intervention plan based on the driver's emotional state. Specifically, the strength of the emotion intervention plan can be related to the degree of emotional change (specifically, the amount of change in the signals indicating emotion). For example, if the degree of emotional change is relatively large, a relatively strong emotion intervention plan can be selected; conversely, if the degree of emotional change is relatively small, a relatively weak emotion intervention plan can be selected. Specifically, the degree of emotional change (e.g., the amount of change in the signals indicating emotion) can be input into an emotion intervention algorithm model stored in the vehicle controller or cloud server, and an emotion intervention plan can be generated through this algorithm model. Specifically, a lookup table associating various degrees of emotional change with various emotion intervention plans can be pre-generated, and then the emotion intervention plan corresponding to the current emotional change can be determined by querying or accessing this lookup table. Of course, regardless of the method, the goal is to obtain a matching emotion intervention plan based on the current emotional change so that appropriate emotional intervention can be provided to the driver in subsequent steps, thereby calming the driver's emotions as quickly as possible.

[0060] Step S204: During the intelligent vehicle's operation, real-time monitoring of the vehicle's surroundings (i.e., objective parameters) such as time, weather, road conditions, route, and scene is conducted. The emotional intervention plan is adjusted based on these surrounding conditions, and / or, based on the driver's driving habits (specifically, sub-driving habits), to generate an emotional intervention scheme. This scheme can be understood as an adjusted emotional intervention plan designed for implementation. For example, if road conditions are good and traffic is smooth, the driver's emotions are more likely to return to normal, so the intensity of the emotional intervention plan can be appropriately reduced; conversely, if road conditions are poor and traffic is congested, the driver's emotions are more difficult to return to normal, so the intensity of the emotional intervention plan can be appropriately increased. Similarly, if there are no heavy vehicles around the intelligent vehicle or they are far away, the driver's emotions are more likely to return to normal, so the intensity of the emotional intervention plan can be appropriately reduced; conversely, if there are heavy vehicles around the intelligent vehicle or they are close to it, the driver's emotions are more difficult to return to normal, so the intensity of the emotional intervention plan can be appropriately increased. For example, if a driver has aggressive driving habits, the intensity of the emotional intervention plan can be appropriately reduced. Drivers with aggressive driving habits are often experienced, skilled, and have strong psychological resilience, making it easier for them to recover emotionally. Conversely, if a driver has conservative driving habits, the intensity of the emotional intervention plan can be appropriately increased. Drivers with conservative driving habits are often inexperienced, less skilled, and have poor psychological resilience, making it more difficult for them to recover emotionally. In other words, a driver's driving habits reflect their personality, and an emotional intervention plan adjusted based on driving habits can be tailored to the driver's personality (or mindset). Therefore, in step S204, the emotional intervention plan can be adjusted not only based on the real-time vehicle surroundings (i.e., objective parameters) but also based on the driver's own driving habits (i.e., subjective parameters). This makes the emotional intervention plan obtained by adjusting the plan more adaptable to the current vehicle surroundings and the driver's personality. This further enables subsequent emotional interventions performed on the driver based on the plan to effectively, reliably, and quickly soothe the driver's emotions and restore them to normal. In particular, if the driver's sub-driving habits related to the vehicle surroundings and / or the driver's emotions are determined before step S201, then the emotional intervention plan obtained by adjusting the plan based on these sub-driving habits can more accurately adapt to the driver's personality (or mindset) related to the vehicle surroundings and / or the driver's emotions, thereby more effectively, reliably, and quickly soothing the driver's emotions and restoring them to normal.

[0061] Step S205: Execute the emotion intervention plan to control the intelligent vehicle to intervene in the driver's emotions. Emotion intervention refers to the operation of various systems on the intelligent vehicle to influence the driver's emotions. For example, the voice system on the intelligent vehicle determines the content, speed, and tone of the spoken messages according to the emotion intervention plan. Another example is the display system on the intelligent vehicle adjusting the visual presentation of screens such as the instrument panel and central control display according to the emotion intervention plan. Yet another example is the lighting system on the intelligent vehicle adjusting the effects of the headlights and ambient lighting according to the emotion intervention plan. Furthermore, the seat system on the intelligent vehicle adjusting the seat angle, seat position, and seatbelt tension according to the emotion intervention plan. Finally, the vehicle-to-everything (V2X) interaction system on the intelligent vehicle adjusting display and broadcast settings according to the emotion intervention plan. Finally, the chassis system on the intelligent vehicle performing chassis tuning according to the emotion intervention plan. In other words, the emotion intervention plan may include adjusting the intelligent vehicle's voice system, lighting system, seat system, V2X system, and chassis system, etc. While many specific methods of emotional intervention for drivers have been described above, those skilled in the art will understand that the present invention is not limited to these methods. Any emotional intervention method designed to influence a driver's emotions under the guidance of this invention should be considered within the scope of protection of this invention. Specifically, the driving mode of the intelligent vehicle can be determined in step S205. If the intelligent vehicle is in automatic or intelligent driving mode, the emotional intervention scheme may further include adjusting the automatic or intelligent driving scheme. For example, based on the emotional intervention scheme, the maximum speed of the automatic or intelligent driving scheme can be reduced, the following distance increased, and the lane-changing frequency decreased, making the automatic or intelligent driving scheme more conservative. This helps to soothe the driver's emotions after a change in mood, allowing them to return to normal as quickly as possible. Of course, if the intelligent vehicle is in manual driving mode, there is no need to adjust the automatic or intelligent driving scheme.

[0062] As described above, the control method according to the present invention can select an appropriate emotional intervention plan based on the driver's emotional changes, and can adjust the emotional intervention plan in real time based on the vehicle's surrounding conditions (i.e., objective parameters) and the driver's driving habits (i.e., subjective parameters) to generate an emotional intervention plan customized for the driver in real time. Furthermore, it can adjust the automatic or intelligent driving plan according to the emotional intervention plan. Therefore, the control method according to the present invention can promptly and effectively soothe the driver's emotions after emotional changes occur, thereby optimizing the driver's driving experience.

[0063] refer to Figure 2 A schematic flowchart of a control method according to another embodiment of the present invention is shown. Figure 1 The embodiment shown differs from the one described above in that, according to Figure 2The control method of the illustrated embodiment may further include the following steps after step S205.

[0064] Step S301: Adjust the emotion intervention plan based on the driver's emotions obtained before implementing the emotion intervention plan (i.e., step S205), the implemented emotion intervention plan, and the driver's emotions obtained after implementing the emotion intervention plan. In other words, optimize the emotion intervention plan based on the implemented emotion intervention plan and its effects. Specifically, the driver's emotions obtained before, during, and after implementing the emotion intervention plan can be used as training data input into the plan optimization algorithm model (e.g., a neural network-type plan optimization algorithm model) so that the plan optimization algorithm model can evaluate the effectiveness of the emotion intervention plan and optimize the emotion intervention plan based on the training data. For example, if the driver's emotions change little before and after implementing the emotion intervention plan, the strength of the emotion intervention plan is increased so that a stronger emotion intervention can be applied to the driver subsequently (e.g., when the driver's emotions worsen again). Furthermore, similar to the emotion assessment algorithm model and emotion intervention algorithm model mentioned above, the plan optimization algorithm model can also be stored in the vehicle controller and / or a cloud server. Of course, storing the above algorithm models on a cloud server is preferred, because with this configuration, the cloud server can obtain a large amount of training data from a large number of intelligent vehicles, making the output results of each algorithm model more accurate, reasonable and effective.

[0065] Step S302: Store the intelligent vehicle's driving parameters (e.g., longitudinal speed, longitudinal acceleration, longitudinal jerk, lateral speed, lateral acceleration, lateral jerk, vehicle offset from lane center, etc.) obtained before executing the emotional intervention plan, the driver's emotions, and the emotional intervention plan; and the intelligent vehicle's driving parameters, driver's emotions, and emotional intervention plan obtained after executing the emotional intervention plan, for example, in an onboard storage device and / or a cloud server. After executing step S302, the above data can be used as use cases in the intelligent vehicle's driving experience segment scenario library, thereby helping drivers, staff, etc., to review and understand the events that triggered the emotional intervention, the generated emotional intervention plan, the emotional intervention plan obtained by adjusting the emotional intervention plan, and the emotional intervention effects achieved by executing the emotional intervention plan, etc., thereby helping drivers improve driving skills and helping staff improve emotional intervention strategies.

[0066] As described above, the control method according to the present invention can not only intervene in the driver's emotions in a timely and effective manner after the driver's emotions deteriorate in order to soothe the driver, but also continuously learn and optimize the emotional intervention plan based on the effect of the emotional intervention, and store the executed emotional interventions and the effects achieved as use cases for the driver and other drivers to experience and learn from.

[0067] Furthermore, it should be noted that although the control method according to the present invention is described herein using an intelligent vehicle as an example, those skilled in the art will understand that the control method according to the present invention can obviously be used to control other types of intelligent mobile spaces, such as intelligent transportation vehicles. Therefore, any scheme for controlling any type of intelligent mobile space under the teachings of this invention should be considered to fall within the protection scope of this invention.

[0068] The present invention also proposes a control unit (also referred to as a control device), which includes a memory, a processor, and a computer program stored in the memory, wherein the processor is configured to execute the computer program to implement the various steps of the control method described above. This control unit may be a control unit integrated into an intelligent vehicle, a control unit integrated into a cloud server, or a control unit integrated into a remote server.

[0069] The optional but non-limiting embodiments of the control method and control unit for controlling an intelligent vehicle according to the present invention have been described in detail above with reference to the accompanying drawings. For those skilled in the art, modifications and additions to the technology and structure, as well as recombinations of features in the various embodiments, should obviously be considered to be included within the scope of the present invention without departing from the spirit and essence of this disclosure. Therefore, such modifications and additions conceivable under the teachings of this invention should be considered part of the invention. The scope of the invention includes equivalent technologies known at the time of filing and equivalent technologies not yet foreseen.

Claims

1. A control method for controlling an intelligent vehicle, comprising the following steps: S101: Identify the driver of the intelligent vehicle after the intelligent vehicle is started; S110: Determine the driver's driving habits in association with the driver's identity; S201: Detect the driver's emotions in real time during the operation of the intelligent vehicle; S202: Compare the driver's current emotion with the previous emotion. If the driver's emotion worsens and the degree of emotion change is above a preset level, then proceed to step S203; otherwise, return to step S201. S203: Determine an emotional intervention plan based on the degree of emotional change of the driver; S204: Adjust the emotion intervention plan according to the driver's driving habits to generate an emotion intervention program; and S205: Control the intelligent vehicle according to the emotion intervention plan to perform emotion intervention on the driver; Step S201 involves: During the operation of the intelligent vehicle, the driving parameters of the intelligent vehicle and the behavior parameters of the driver are detected in real time. The driving parameters of the intelligent vehicle and the behavior parameters of the driver are input into a pre-stored emotion evaluation algorithm model to obtain the driver's emotion. Step S204 involves: The emotional intervention plan is adjusted based on the driver's driving habits and the real-time measurement of the vehicle's surroundings during the vehicle's operation to generate the emotional intervention scheme. The control method further includes the following steps after step S205: S301: Adjust the emotion intervention plan based on the driver's emotions measured before step S205, the emotion intervention plan, and the driver's emotions measured after step S205; and S302: Store the following data: The driving parameters of the intelligent vehicle, the driver's emotions, and the emotion intervention plan measured before step S205; and The driving parameters of the intelligent vehicle, the driver's emotions, and the emotion intervention plan are measured after step S205.

2. The control method according to claim 1, wherein, Step S110 includes the following sub-steps: S102: Confirm the driving mode of the intelligent vehicle. If the driving mode is manual driving mode, proceed to step [step number missing]. S111; If the driving mode is automatic or intelligent driving mode, then execute steps S121-S123; S111: Determine the driver's driving habits based on the driver's operational behavior measured in real time during the operation of the intelligent vehicle, and store the driver's driving habits in association with the driver's identity; S121: Query historical records to obtain the driver's driving habits based on the driver's identity; S122: Obtain an automatic or intelligent driving scheme based on the driver's driving habits; as well as S123: Control the driving of the intelligent vehicle according to the automatic or intelligent driving scheme.

3. The control method according to claim 2, wherein, Step S123 also includes: The automatic or intelligent driving scheme is adjusted based on the real-time measurement of the vehicle's surroundings and / or the driver's mood during the vehicle's operation.

4. The control method according to any one of claims 1-3, wherein, The driver's driving habits are also related to the vehicle's surroundings and / or the driver's emotions as measured in real time during the operation of the intelligent vehicle.

5. The control method according to claim 4, wherein, The surrounding conditions of the vehicle include one or more of the following: time, weather, road conditions, and route during the intelligent vehicle's operation.

6. The control method according to claim 5, wherein, Step S205 is as follows: According to the emotion intervention scheme, control one or more of the intelligent vehicle's voice system, lighting system, vehicle-machine interaction system, seat system, and chassis system to intervene in the driver's emotions.

7. The control method according to claim 6, wherein, Step S205 is as follows: The driving mode of the intelligent vehicle is confirmed. If the driving mode is automatic or intelligent driving mode, the automatic or intelligent driving scheme is adjusted according to the emotion intervention scheme.

8. A control unit for controlling an intelligent vehicle, the control unit comprising a memory, a processor, and a computer program stored in the memory, wherein, The processor is configured to execute the computer program to implement the steps of the control method according to any one of claims 1-7.

Images