Passenger preference-based real-time vehicle control method and profile service platform

Abstract

A passenger preference-based real-time vehicle control method and a profile service platform are disclosed. The passenger preference-based real-time vehicle control method may comprise the steps of: managing a vehicle driving preference profile for each passenger; and controlling vehicle driving parameters according to a passenger on the basis of the preference profile of the passenger.

Description

Passenger Preference-Based Real-Time Vehicle Control Method and Profile Service Platform

[0001] The following description concerns vehicle control technology based on passenger preferences.

[0002] Car rental services or car sharing services are gaining popularity for users who have a heavy burden of vehicle maintenance costs or need to use a vehicle temporarily.

[0003] For example, Korean Patent Publication No. 10-2021-0050218 (published on May 7, 2021) discloses a technology for effectively sharing an event video captured by a preceding vehicle at the point where the event occurred with multiple following vehicles on the path where the event occurred.

[0004] The limitations of shared vehicle technology are as follows.

[0005] 1) Limitations in terms of vehicle control

[0006] In car-sharing services, passenger discomfort arises due to varying driving styles among drivers. In particular, electric vehicles face issues with reduced ride comfort, such as motion sickness caused by initial acceleration. Not only is control over safety risks like sudden acceleration and speeding inadequate, but there is also a lack of vehicle control functions that reflect passenger preferences.

[0007] 2) Limitations in terms of service platform

[0008] There is no system to register or manage individual passenger driving preference patterns, and a differentiated pricing system based on driving style is lacking. Furthermore, controlling driving characteristics in conjunction with autonomous driving software is difficult, and a preference matching system between drivers and passengers is currently absent.

[0009] 3) Technical Issues

[0010] Real-time integration between the vehicle's in-vehicle control system and external service platforms is required, and the standardization of control interfaces across various vehicle models and manufacturers is necessary. Furthermore, it is essential to ensure flexible connectivity with autonomous driving systems, and technologies for the quantitative measurement of passenger preferences and control parameter conversion are required.

[0011] 4) Market Environment Analysis

[0012] The rapid growth of the car-sharing economy is increasing the demand for differentiated services, and the expansion of electric vehicle adoption is highlighting the need for new forms of ride comfort control. Premium demand for passenger-customized services is on the rise, and preparations for new services, such as robotaxis driven by advancements in autonomous driving technology, are necessary.

[0013] It is possible to provide a vehicle control system based on passenger preferences and an intelligent matching platform based on driving style similarity.

[0014] A real-time vehicle control method based on passenger preference, performed by a computer device comprising at least one processor, comprising: a step of generating and managing a preference profile that quantifies a passenger's preference for acceleration intensity, deceleration intensity, and cornering speed during vehicle driving; and a step of controlling vehicle driving parameters linked to a vehicle ECU (electronic control unit) by monitoring compliance with the preference profile based on real-time driving data collected from a sensor of the passenger's vehicle according to the passenger.

[0015] According to one aspect, the managing step may include a step of learning a preference profile of the passenger based on the passenger's boarding history.

[0016] According to another aspect, the managing step may include a step of recommending a preference profile of the passenger based on the passenger's boarding history.

[0017] According to another aspect, the managing step may include the step of setting a preference profile for the passenger according to the passenger's boarding situation.

[0018] According to another aspect, the managing step may include the step of providing preset driving profiles, including profiles by age group, gender, region, weather, occupant type, and purpose of ride.

[0019] According to another aspect, the controlling step may include a step of controlling the vehicle driving acceleration by limiting the output of the vehicle motor according to the preference profile.

[0020] According to another aspect, the controlling step may include a step of adjusting the vehicle driving parameters in real time by monitoring compliance with the preference profile based on real-time driving data collected from a sensor of the vehicle in motion at a 10ms interval.

[0021] According to another aspect, the controlling step may include the step of applying safety limits, including the maximum allowable speed set in the preference profile, to the vehicle ECU.

[0022] According to another aspect, the controlling step may include a step of adjusting the vehicle's suspension and steering sensitivity in real time according to the preference profile.

[0023] According to another aspect, the occupant preference-based real-time vehicle control method may further include the step of performing vehicle matching for the occupant by considering the occupant's preference profile and vehicle characteristics.

[0024] According to another aspect, the occupant preference-based real-time vehicle control method may further include the step of performing driver matching with the occupant's preferred driving style based on the occupant's preference profile.

[0025] According to another aspect, the occupant preference-based real-time vehicle control method described above may further include a step of monitoring the profile application status of the driving vehicle and the vehicle driving status.

[0026] According to another aspect, the occupant preference-based real-time vehicle control method may further include a step of suspending the application of the preference profile when a dangerous situation defined as a detection item is detected for a driving vehicle.

[0027] According to another aspect, the passenger preference-based real-time vehicle control method may further include a step of calculating differential fares based on the passenger preference profile.

[0028] The present invention provides a computer device comprising at least one processor implemented to execute a readable command on a computer device, wherein the at least one processor processes: a process of generating and managing a preference profile that quantifies a passenger's preference for acceleration intensity, deceleration intensity, and cornering speed of vehicle driving; and a process of controlling vehicle driving parameters linked to a vehicle ECU (electronic control unit) by monitoring compliance with the preference profile based on real-time driving data collected from a sensor of the passenger's vehicle according to the passenger.

[0029] According to embodiments of the present invention, customized ride comfort can be provided through AI-based personalized profile generation, real-time driving environment adaptive control, and multi-sensor-based precision control, thereby improving passenger satisfaction and reducing motion sickness incidence and ride comfort quality variations.

[0030] According to embodiments of the present invention, safety can be improved by reducing sudden braking / sudden acceleration through 10ms periodic real-time control, a triple nested safety system, and prediction-based risk situation prevention, and can support hardware-software integrated safety design, a self-diagnosis and recovery system, a real-time remote monitoring system, etc.

[0031] According to embodiments of the present invention, ride comfort satisfaction can be improved through fully automated ride comfort optimization, real-time situation-adaptive service, and individual preference learning functions, thereby increasing the service reuse rate.

[0032] FIG. 1 is a block diagram illustrating an example of the internal configuration of a computer device in an embodiment of the present invention.

[0033] FIG. 2 illustrates a data collection architecture for real-time monitoring in an embodiment of the present invention.

[0034] FIGS. 3 and 4 illustrate the overall configuration of a real-time vehicle control system based on passenger preference in one embodiment of the present invention.

[0035] FIG. 5 illustrates a detailed configuration diagram of a profile service module in an embodiment of the present invention.

[0036] FIG. 6 illustrates a detailed configuration diagram of a vehicle control system in one embodiment of the present invention.

[0037] FIG. 7 illustrates a detailed configuration diagram of a profile management system in one embodiment of the present invention.

[0038] FIG. 8 illustrates a detailed configuration diagram of a real-time vehicle control system in one embodiment of the present invention.

[0039] FIG. 9 illustrates a detailed configuration diagram of a user interface system in one embodiment of the present invention.

[0040] FIG. 10 illustrates a passenger boarding process sequence in one embodiment of the present invention.

[0041] FIG. 11 illustrates a profile-based vehicle control sequence in one embodiment of the present invention.

[0042] FIG. 12 illustrates a safety monitoring process sequence in one embodiment of the present invention.

[0043] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings.

[0044]

[0045] Embodiments of the present invention relate to vehicle control technology based on passenger preference.

[0046] Embodiments including those specifically disclosed in this specification can provide a differentiated competitive vehicle sharing service through vehicle control based on passenger preferences, and future service applications such as fully autonomous robot taxis are also possible.

[0047] A real-time vehicle control system based on passenger preference according to embodiments of the present invention may be implemented by at least one computer device, and a real-time vehicle control method based on passenger preference according to embodiments of the present invention may be performed through at least one computer device included in the real-time vehicle control system based on passenger preference. At this time, a computer program according to an embodiment of the present invention may be installed and run on the computer device, and the computer device may perform a real-time vehicle control method based on passenger preference according to embodiments of the present invention under the control of the run computer program. The above-described computer program may be stored on a computer-readable recording medium to be combined with the computer device to execute the real-time vehicle control method based on passenger preference on the computer.

[0048] FIG. 1 is a block diagram illustrating an example of a computer device according to an embodiment of the present invention. For example, a passenger preference-based real-time vehicle control system according to embodiments of the present invention can be implemented by a computer device (100) illustrated in FIG. 1.

[0049] As illustrated in FIG. 1, the computer device (100) may include a memory (110), a processor (120), a communication interface (130), and an input / output interface (140) as components for executing a real-time vehicle control method based on passenger preference according to embodiments of the present invention.

[0050] Memory (110) is a computer-readable recording medium and may include a non-perishable mass storage device such as RAM (random access memory), ROM (read only memory), and a disk drive. Here, a non-perishable mass storage device such as a ROM and a disk drive may be included in the computer device (100) as a separate permanent storage device distinct from memory (110). Additionally, an operating system and at least one program code may be stored in memory (110). These software components may be loaded into memory (110) from a computer-readable recording medium separate from memory (110). This separate computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, disk, tape, DVD / CD-ROM drive, or memory card. In another embodiment, software components may be loaded into memory (110) through a communication interface (130) rather than a computer-readable recording medium. For example, software components can be loaded into the memory (110) of the computer device (100) based on a computer program installed by files received through the network (160).

[0051] The processor (120) may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Instructions may be provided to the processor (120) via memory (110) or a communication interface (130). For example, the processor (120) may be configured to execute instructions received according to program code stored in a recording device such as memory (110).

[0052] The communication interface (130) may provide a function for the computer device (100) to communicate with other devices through a network (160). For example, requests, commands, data, files, etc. generated by the processor (120) of the computer device (100) according to program code stored in a recording device such as memory (110) may be transmitted to other devices through the network (160) under the control of the communication interface (130). Conversely, signals, commands, data, files, etc. from other devices may be received by the computer device (100) through the communication interface (130) of the computer device (100) via the network (160). Signals, commands, data, etc. received through the communication interface (130) may be transmitted to the processor (120) or memory (110), and files, etc. may be stored in a storage medium (the permanent storage device described above) that the computer device (100) may further include.

[0053] The communication method is not limited and may include not only communication methods utilizing communication networks (e.g., mobile communication networks, wired internet, wireless internet, broadcasting networks) that the network (160) may include, but also short-range wired / wireless communication between devices. For example, the network (160) may include any one or more networks such as a PAN (personal area network), LAN (local area network), CAN (campus area network), MAN (metropolitan area network), WAN (wide area network), BBN (broadband network), and the Internet. Additionally, the network (160) may include any one or more network topologies such as a bus network, star network, ring network, mesh network, star-bus network, tree or hierarchical network, but is not limited thereto.

[0054] The input / output interface (140) may be a means for interfacing with an input / output device (150). For example, the input device may include a device such as a microphone, keyboard, camera, or mouse, and the output device may include a device such as a display or speaker. As another example, the input / output interface (140) may be a means for interfacing with a device in which the functions for input and output are integrated into one, such as a touchscreen. The input / output device (150) may be composed of a computer device (100) and a single device.

[0055] Additionally, in other embodiments, the computer device (100) may include fewer or more components than the components of FIG. 1. However, it is not necessary to clearly illustrate most of the prior art components. For example, the computer device (100) may be implemented to include at least some of the input / output devices (150) described above, or may include other components such as a transceiver, a camera, various sensors, a database, etc.

[0056] Below, specific embodiments of occupant preference-based real-time vehicle control technology will be described.

[0057] When using taxis, car-sharing services, ride-sharing services, etc., a vehicle control method based on the preferred riding type of the user who called is required.

[0058] When using taxis or car-sharing services, passengers may experience discomfort depending on the driver's characteristics.

[0059] In particular, electric vehicles may cause motion sickness symptoms such as head tilting due to initial acceleration, and dangerous incidents such as sudden acceleration and speeding may occur.

[0060] Therefore, if a vehicle caller has pre-set characteristics regarding their driving style, the settings related to the vehicle's performance should be configured together when the vehicle is called, thereby providing a role to control the driver so that they cannot accelerate rapidly or overspeed.

[0061] If a passenger's driving preference pattern is registered, the driver can selectively apply it, and if applied, can request a separate fee system, additional fees, or a tip.

[0062] In addition, settings for ride comfort can be applied in an automated manner by connecting to the vehicle's chassis and power control sections, and in the case of supporting autonomous driving, it can operate in conjunction with the driving characteristics of the autonomous driving software.

[0063] This can serve as a differentiated competitive advantage for ride-sharing and taxi-hailing services, and will be applicable to other future services, such as fully autonomous robot taxis.

[0064] The computer device (100) according to the present embodiment can provide a real-time vehicle control service based on passenger preference to a client by accessing a dedicated application installed on the client or a web / mobile site related to the computer device (100). The computer device (100) may be configured with a real-time vehicle control system based on passenger preference implemented by a computer. For example, the real-time vehicle control system based on passenger preference may be implemented in the form of a program that operates independently, or configured as an in-app of a specific application so that it can operate on said specific application.

[0065] The processor (120) of the computer device (100) may be implemented as a component for performing the following passenger preference-based real-time vehicle control method. Depending on the embodiment, the components of the processor (120) may be optionally included in or excluded from the processor (120). Additionally, depending on the embodiment, the components of the processor (120) may be separated or merged to represent the function of the processor (120).

[0066] These processors (120) and components of the processor (120) can control a computer device (100) to perform steps included in the following passenger preference-based real-time vehicle control method. For example, the processor (120) and components of the processor (120) may be implemented to execute instructions according to the code of an operating system included in memory (110) and the code of at least one program.

[0067] Here, the components of the processor (120) may be representations of different functions performed by the processor (120) according to instructions provided by program code stored in the computer device (100).

[0068] The processor (120) can read necessary instructions from memory (110) in which instructions related to the control of the computer device (100) are loaded. In this case, the read instructions may include instructions for controlling the processor (120) to execute the steps to be described later.

[0069] The steps included in the occupant preference-based real-time vehicle control method described later may be performed in a different order than the illustrated order, and some of the steps may be omitted or additional processes may be included.

[0070]

[0071] The embodiments may provide a occupant preference-based vehicle control system comprising the following technical features.

[0072] 1) Management of Passenger Driving Preference Profiles

[0073] - Quantification of detailed driving preferences such as acceleration / deceleration intensity and cornering speed

[0074] - Supports differentiated control profiles by vehicle type (EV / Internal Combustion Engine)

[0075] - Automatic preference learning and recommendation features based on ride history

[0076] - Supports multiple preferred profile settings for different situations (commute, returning home, long distance, etc.)

[0077] 2) Real-time vehicle control mechanism

[0078] - Real-time driving parameter control linked to the vehicle ECU

[0079] - Acceleration control through electric vehicle motor output limiting

[0080] - Real-time adjustment of vehicle suspension / steering sensitivity

[0081] - In-vehicle sensor-based ride comfort monitoring and feedback

[0082] 3) Profile Service Platform

[0083] - Preset driving profiles provided by the vehicle supplier

[0084] : Provides basic profiles such as Comfort / Eco / Sport.

[0085] : Provides age group / gender-based target profiles

[0086] : Support for special purpose profiles (pregnant women / elderly / motion sickness prevention, etc.)

[0087] : Provides region / weather-specific profiles

[0088] - Subscription-based premium profile

[0089] : Professional Racer Tuning Profile

[0090] : High-end vehicle simulation profile

[0091] Season-specific profiles

[0092] Real-time profile update service

[0093] - Profile Marketplace

[0094] : Third-party developer profile provision platform

[0095] : User-generated profile sharing / trading

[0096] : Profile Evaluation / Review System

[0097] Popular Profile Recommendation Service

[0098] - Profile Optimization System

[0099] : AI-based personalized profile automatic generation

[0100] Automatic profile correction based on vehicle characteristics

[0101] : Driving Data-Based Profile Evolution

[0102] Dynamic optimization linked to real-time road conditions

[0103] 4) Profile Management System

[0104] - Profile Version Management

[0105] : Vehicle Model Compatibility Management

[0106] Safety Verification and Certification

[0107] : Update history tracking

[0108] : Rollback support

[0109] - Profile Subscription Management

[0110] : Differentiated services based on subscription tiers

[0111] : Automatic renewal / payment system

[0112] : Usage statistics and analysis

[0113] : Customized Profile Recommendation

[0114] - Profile Security Management

[0115] : Profile encryption / authentication

[0116] : Anti-piracy

[0117] : Access permission management

[0118] : Security Update

[0119] 5) Revenue Model

[0120] - Vehicle supplier

[0121] Premium Profile Package Sales

[0122] : Subscription-based profile service

[0123] Season-specific specialized profile products

[0124] : Bundle products linked to vehicle sales

[0125] - System provider

[0126] : Profile platform operation fee

[0127] API provision license

[0128] : Technical support service

[0129] : Data analysis service

[0130] - Third party

[0131] : Profile development / sales revenue

[0132] Custom Profile Creation Service

[0133] : Profile Consulting

[0134] : Market development for specialized profiles

[0135] These embodiments can provide an intelligent matching platform including the following technical features.

[0136] 1) Driver-Passenger Smart Matching

[0137] - Optimal matching algorithm based on driving style similarity

[0138] - Priority dispatch of vehicles capable of supporting passenger preferences

[0139] - Dynamic matching reflecting real-time traffic conditions

[0140] - Integration with driver rating / review system

[0141] 2) Differential pricing system

[0142] - Differential pricing based on preference levels

[0143] - Linked with driver incentive / penalty system

[0144] - Commercialization of premium service packages

[0145] - Provides automatic payment / settlement system

[0146] These embodiments can provide an autonomous driving linkage system including the following technical features.

[0147] 1) Autonomous driving software integration

[0148] - Optimization of driving patterns based on passenger preferences

[0149] - AI-based ride comfort quality prediction / control

[0150] - Real-time route optimization integration

[0151] - Automatic reflection of vehicle hardware constraints

[0152] 2) Expansion of robot taxi services

[0153] - Custom control for fully autonomous vehicles

[0154] - Dynamic control based on occupant status monitoring

[0155] - Automatic setting of in-vehicle convenience features

[0156] - Establishment of a remote monitoring / control system

[0157] These embodiments may provide an integrated platform including the following technical features.

[0158] 1) Cloud-based service architecture

[0159] - Real-time data processing / analysis system

[0160] - Secure communication system between vehicle and server

[0161] - Multi-model / Manufacturer Integrated Interface

[0162] - Ensuring service scalability and flexibility

[0163] 2) Mobile App Service

[0164] - Provides an intuitive user interface

[0165] - Real-time vehicle status / location monitoring

[0166] - Integration with easy payment / review system

[0167] - Personalized service recommendation feature

[0168]

[0169] As a occupant preference-based vehicle control profile technology according to the present invention, the profile data structure may include a basic information field, a vehicle information field (target_vehicle), control parameters (control_parameters), safety limit values ​​(safety_limits), data processing characteristics, scalability considerations, etc.

[0170] 1. Basic Information Fields profile_id Description: Unique identifier for the profile Format: String in UUID v4 format Example: "550e8400-e29b-41d4-a716-446655440000" Purpose: Used for profile tracking and version management version Description: Version information for the profile Format: Semantic Versioning (Major.Minor.Patch) Example: "1.0.0" Purpose: Profile updates and compatibility management category Description: Classification of the profile's driving characteristics Possible values: "comfort", "eco", "sports", "custom" Purpose: Used for profile search and classification

[0171] 2. Vehicle Information Field (target_vehicle) type Description: Supported vehicle drive systems Possible values: ["EV", "ICE", "HYBRID"] Purpose: Check compatibility by vehicle type models Description: List of supported vehicle models Format: Array of strings Example: ["Model3", "ModelY", "ID4"] Purpose: Check compatibility by vehicle model year Description: Range of supported vehicle model years Format: "Start Year - End Year" Example: "2020-2024" Purpose: Check compatibility by model year

[0172] 3. Control Parameters (control_parameters)acceleration{"max_rate": 0.8, / Maximum acceleration rate (value between 0 and 1)"response_curve": "linear", / Accelerator pedal response curve type"throttle_map": [ / Output mapping relative to pedal input[0.0, 0.0], / [Input value, Output value][0.5, 0.3], / Implements smooth acceleration by reducing actual output[1.0, 0.8] / Prevents sudden acceleration by limiting maximum output]}brake{"sensitivity": 0.7, / Brake pedal sensitivity (0 to 1)"initial_bite": 0.3, / Initial braking force (0 to 1)"force_distribution": [ / Front / rear braking force distribution ratio[0.0, 0.6, 0.4], / [Pedal input value, Front ratio, Rear ratio][0.5, 0.7, 0.3],[1.0, 0.8, 0.2]]}steering{"ratio": 0.75, / Steering gear ratio adjustment value (0~1)"force_feedback": 0.6, / Steering reaction force intensity (0~1)"assistance_map": [ / Power steering assistance by speed[0, 1.0], / [Vehicle speed(km / h), assistance ratio][60, 0.7],[120, 0.5]]}suspension{"damping_rate": 0.65, / Damping force adjustment value (0~1)"rebound_rate": 0.6, / Rebound damping ratio (0~1)"height_control": [ / Vehicle height control[0, 0.0], / [Speed(km / h), height adjustment value(mm)][80, -10],[120, -20]]}

[0173] 4. Safety Limits (safety_limits) max_speed Description: Maximum driving speed limit Unit: km / h Range: 0 ~ 250 Purpose: Limit value to prevent speeding max_g_force Description: Maximum allowable acceleration Unit: G (relative to gravitational acceleration) Range: 0.1 ~ 1.0 Purpose: Ensuring ride comfort and safety cornering_limits[[30, 0.3], / [Radius of curvature (m), Maximum allowable lateral acceleration (G)][50, 0.4], / Lower lateral acceleration allowed as radius of curvature decreases [100, 0.5]]

[0174] 5. Data Processing Characteristics Update Cycle: 10ms (100Hz) Storage Capacity: Approx. 10KB / Profile Encryption: AES-256 applied Compression Method: GZIP (Approx. 70% compression ratio) 6. Scalability Considerations Ability to add new control parameters Ability to define vehicle-specific parameters Support for user-defined parameters Integration with real-time monitoring data

[0175] An example of the profile data structure of the above configuration is shown in Table 6.

[0176] {"profile_id": "unique_identifier","version": "1.0.0","category": "comfort / eco / sports","target_vehicle": {"type": ["EV", "ICE"],"models": ["supported_models"],"year": "2020-2024"},"control_parameters": {"acceleration": {"max_rate": 0.8, / 0.0 ~ 1.0"response_curve": "linear / exponential","throttle_map": [...],},"brake": {"sensitivity": 0.7,"initial_bite": 0.3,"force_distribution": [...]},"steering": {"ratio": 0.75,"force_feedback": 0.6,"assistance_map": [...]},"suspension": {"damping_rate": 0.65,"rebound_rate": 0.6,"height_control": [...]}},"safety_limits": {"max_speed": 120,"max_g_force": 0.4,"cornering_limits": [...]}}

[0177] The passenger preference-based vehicle control profile technology according to the present invention may include a profile optimization algorithm. The profile optimization algorithm may include a driving data collection process (S10), a data preprocessing process (S20), a profile parameter optimization process (S30), etc. The driving data collection process (S10) may include a vehicle sensor data real-time collection (100Hz) process (S11), an acceleration, gyroscope, and GPS data integration process (S12), a passenger feedback information linkage process (S13), and a road / weather condition data combination process (S14).

[0178] The data preprocessing process (S20) may include a noise filtering (Kalman filter application) process (S21), an outlier removal (MAD method) process (S22), a time series data normalization process (S23), and a feature extraction (FFT-based) process (S24).

[0179] The profile parameter optimization process (S30) can perform profile parameter optimization by comprehensively reflecting the passenger's ride comfort quality evaluation, user preferences, etc.

[0180] def optimize_profile(sensor_data, user_preference): # Set initial parameters current_params = initialize_parameters() # Iterative optimization for epoch in range(MAX_EPOCHS): # Process data batches for batch in get_batches(sensor_data): # Evaluate comfort quality comfort_score = evaluate_comfort(batch, current_params) # Incorporate user preferences preference_score = calculate_preference_match(current_params, user_preference) # Calculate total score total_score = comfort_score * 0.7 + preference_score * 0.3 # Update parameters current_params = update_parameters(current_params, total_score, learning_rate) # Check convergence if check_convergence(current_params): break return current_params

[0181] The occupant preference-based real-time vehicle control technology according to the present invention is as follows. Control signal processing for occupant preference-based real-time vehicle control may include a signal conversion process and a real-time control loop.

[0182] 1. Signal Conversion Process class ControlSignalProcessor: def __init__(self, vehicle_spec): self.vehicle_spec = vehicle_spec self.signal_queue = Queue() def process_control_signal(self, profile_params, sensor_data): # Interpret profile parameters target_values ​​= decode_profile_params(profile_params) # Calibrate current sensor values adjusted_sensor = calibrate_sensor_data(sensor_data) # Generate control signal control_signal = generate_control_signal(target_values, adjusted_sensor) # Calibrate signal based on vehicle spec final_signal = adjust_for_vehicle(control_signal, self.vehicle_spec) return final_signal 2. Real-time Control Loop Control cycle: 10ms Priority-based signal processing Real-time verification of safety limit values Emergency response logic

[0183] The passenger preference-based profile service platform technology according to the present invention is as follows. The passenger preference-based profile service platform may include a profile distribution system, and the version control scheme for profile distribution may include the application of semantic versioning, a vehicle model-specific compatibility matrix, an automatic update mechanism, and a rollback procedure. In addition, the integrity verification for profile distribution is as shown in Table 9.

[0184] class ProfileValidator:def validate_profile(self, profile_data):# Structure validationif not self._validate_structure(profile_data):raise ValidationError("Invalid profile structure")# Parameter range validationif not self._validate_parameters(profile_data):raise ValidationError("Parameter out of range")# Safety validationif not self._verify_safety_constraints(profile_data):raise ValidationError("Safety check failed")# Compatibility Verificationif not self._check_compatibility(profile_data):raise ValidationError("Compatibility issues found")return True

[0185] A passenger preference-based profile service platform may include a real-time monitoring system. The data collection architecture for real-time monitoring is as shown in FIG. 2, and performance analysis metrics may utilize ride quality indicators (RMS acceleration), energy efficiency indicators, safety indicators (number of sudden braking / accelerations), profile compliance rates, etc. Vehicle sensors can collect real-time data from various vehicle sensors. The main sensor type, the IMU (Inertial Measurement Unit), can collect acceleration and gyroscope sensor data (100Hz), the GPS can collect position and speed data (1Hz), the ECU can collect vehicle state data (50Hz) such as engine, brake, and steering, and the suspension sensor can collect body vibration and attitude data (100Hz).

[0186] The Local Buffer can perform temporary storage and preprocessing of sensor data. It performs real-time data buffering (in 10ms increments) and supports data synchronization, timestamp assignment, initial noise filtering, and data format standardization.

[0187] The Edge Processor can perform in-vehicle data preprocessing and primary analysis. The Edge Processor can apply sensor fusion algorithms and perform outlier detection and correction, data compression and encryption, and real-time status monitoring.

[0188] Cloud Storage can support the centralized storage of all vehicle data. Cloud Storage may include large-capacity distributed storage systems and data backup and recovery systems, and can support access control management, security, and data lifecycle management.

[0189] The Analytics Engine can perform in-depth analysis of collected data. By analyzing machine learning-based patterns, the Analytics Engine can execute profile optimization algorithms, create and update predictive models, and calculate performance metrics.

[0190] The Dashboard and Alert System can visualize analysis results and perform monitoring. It provides real-time monitoring dashboards and supports automatic detection and notification of anomalies, automatic generation of performance reports, and the provision of user-customized interfaces.

[0191] FIGS. 3 and 4 illustrate the overall configuration of a real-time vehicle control system based on passenger preference in one embodiment of the present invention.

[0192] Referring to FIGS. 3 and 4, the passenger preference-based real-time vehicle control system according to the present invention may include a cloud platform (310), a vehicle system (320), and a mobile application (330).

[0193] The cloud platform (310) may include a profile service module, an analysis server, and a matching server.

[0194] The profile service module of the cloud platform (310) can handle profile CRUD (Create, Read, Update, Delete) operations and perform version control and distribution management. In addition, the profile service module can provide user-customized recommendation services by performing real-time profile optimization.

[0195] The profile service is a core service for occupant preference-based vehicle control and can manage the entire lifecycle of profiles, from creation to optimization, verification, and storage.

[0196] The profile creation and management functions of Profile Management may include UUID-based profile identifier generation, semantic versioning (Major.Minor.Patch)-based version management, profile category classification (comfort / eco / sports / custom), and vehicle model-specific compatibility information management. The profile component management functions may include control parameter settings (acceleration, braking, steering, suspension), safety limit management (maximum speed, G-force, cornering limit), and vehicle-specific parameter mapping. The profile distribution management functions may include real-time profile updates, rollback mechanisms, and phased distribution strategies.

[0197] The profile creation process for profile CRUD management allows for the collection and validation of basic user and vehicle information. Starting from a basic profile template, users can customize it to meet user requirements and verify that all configured parameters remain within a safe and valid range. A unique profile ID can be generated, metadata added, and initial information for profile version management can be set.

[0198] The profile lookup / search function allows users to search for profiles based on various criteria (vehicle type, user type, preferences, etc.). Filtering is possible based on vehicle model compatibility, and a caching system can be utilized to verify and manage user-specific access permissions for faster retrieval.

[0199] The profile update processing function can track and manage the history of all changes. It monitors the status of profiles applied in real time, manages rollback points to restore to previous versions if necessary, and detects and resolves conflicts that may occur during simultaneous modifications.

[0200] The profile deletion process allows you to clean up all data associated with the profile, follows a safe termination procedure for profiles currently in use, manage backups of deleted data, and record the history of all deletion operations.

[0201] The data-driven optimization function of the Optimization Engine may include the collection and analysis of driving data (100Hz sampling), the integration of passenger feedback information, and the combination of road and weather condition data. The profile parameter optimization function may include ride comfort quality evaluation algorithms, weight adjustments reflecting user preferences, and the execution of iterative optimization processes. The real-time adaptive optimization function may include dynamic adjustments based on the driving environment, AI-based automatic parameter adjustment, and the incorporation of real-time performance feedback.

[0202] A real-time optimization engine may include data collection and preprocessing, optimization algorithms, and real-time monitoring and adjustment processes.

[0203] In the data collection and preprocessing process, data can be collected from various sensors of the vehicle at a 100Hz frequency to filter out noise from the collected data. At this time, abnormal data can be detected and removed, and key features necessary for analysis can be extracted.

[0204] Optimization algorithms can analyze driving patterns using deep learning models and calculate and evaluate current performance indicators. Based on user feedback and current performance, optimization goals can be set, and parameters can be optimized while complying with safety constraints.

[0205] During the real-time monitoring and tuning process, key performance indicators can be calculated and tracked in real time. The effectiveness of optimization tasks can be continuously verified, and parameters can be dynamically adjusted according to the situation. The system can be improved through a continuous feedback loop.

[0206] The structural verification function of the validation system may include validating profile data structures, verifying the existence of essential parameters, and verifying data types and ranges. The safety verification function may include simulations based on vehicle dynamics models, extreme condition tests, and verification of compliance with safety limits. The compatibility verification function may include compatibility tests by vehicle model, verification of hardware constraints, and verification of version compatibility.

[0207] The profile verification system may include structural verification, safety verification, and compatibility verification.

[0208] The structural verification process confirms whether all profile parameters adhere to a defined format and structure. It checks for the existence of all required fields, verifies the correct data types of each parameter, and verifies the version compatibility of the profile structure.

[0209] During the safety verification process, it is possible to confirm whether all parameters are within a safe operating range and to verify that the interactions between related parameters are safe. It is also possible to check whether a sufficient safety margin has been secured by comparing it with vehicle specifications and to verify whether it is controllable in real-time on an actual vehicle.

[0210] In the compatibility verification process, it is possible to check whether the profile is compatible with the specified vehicle model and to examine compatibility with the vehicle's ECU version. It is also possible to verify whether all control parameters are within the vehicle's physical limits and to examine compatibility with the vehicle's special functions or constraints.

[0211] The data storage management functions of the storage may include profile metadata management, version-specific profile data storage, and backup and recovery systems. Access control functions may include role-based access control (RBAC), encryption and security management, and audit log recording. Data archiving functions may include long-term retention policies, data compression and optimization, and history management.

[0212] The analysis server of the cloud platform (310) can collect and analyze driving data and perform profile performance evaluation based on the results of the data analysis. In addition, the analysis server can improve the AI-based profile by analyzing user feedback.

[0213] The analysis service of the cloud platform (310) can derive insights based on collected data and perform analysis for system improvement.

[0214] Data analytics functions can support real-time analysis, performance analysis, and user behavior analysis. Real-time analysis functions may include real-time processing of driving data, pattern recognition and analysis, and anomaly detection; performance analysis functions may include analysis of ride comfort quality indicators, energy efficiency analysis, and safety indicator analysis; and user behavior analysis functions may include preference pattern analysis, user segmentation, and satisfaction analysis.

[0215] The Machine Learning Engine (ML) can support model training, predictive analytics, and model management. Model training functions may include deep learning-based profile optimization, reinforcement learning-based control algorithms, and pattern recognition model training; predictive analytics functions may include driving pattern prediction, user preference prediction, and system performance prediction; and model management functions may include model version management, performance monitoring, and retraining scheduling.

[0216] A data warehouse can perform data integration, data structuring, and analysis support. Data integration functions may include multi-source data integration, ETL process management, and data quality management; data structuring functions may include schema design and management, metadata management, and data mart configuration; and analysis support functions may include OLAP cube configuration, reporting systems, and data mining support.

[0217] The analysis server of the cloud platform (310) may include a data collection and storage module, an analysis engine, and a reporting system.

[0218] In the data collection process of the data collection and storage module, real-time data can be collected from all sensors of the vehicle and driver input data can be recorded. Environmental data such as weather and road conditions can be collected, and all collected data is refined through a preprocessing stage, after which the refined data can be stored in a time-series database.

[0219] The data collection and storage module can store all data along with timestamps. It can distinguish data based on vehicle ID and profile ID, and record physical measurements such as acceleration, braking pressure, and steering angle. In this process, it can include GPS location information and driving environment data, and apply data compression and backup policies.

[0220] The analysis engine may include a performance analysis system, a pattern recognition system, and a predictive modeling module.

[0221] The performance analysis system can analyze the vehicle's energy efficiency by calculating ride comfort quality indicators in real time. It can continuously monitor safety-related indicators and quantitatively analyze the effects of applied profiles.

[0222] The pattern recognition system can extract characteristic patterns from driving data and classify the extracted patterns by type. It can evaluate the safety and efficiency of each pattern and utilize the pattern analysis results for profile optimization.

[0223] The predictive modeling module can predict changes in ride comfort based on driving conditions and calculate estimated energy consumption. Additionally, it can predict the lifespan and maintenance timing of vehicle components and detect potential accident risks in advance.

[0224] The reporting system supports real-time dashboards, enabling the real-time visualization of key performance indicators and the immediate generation of alerts in the event of anomalies. It can display trends of major indicators in graphs and provide a user-friendly interface.

[0225] The matching server of the cloud platform (310) can process driver-passenger matching and verify profile compatibility. In addition, the matching server can handle real-time availability management as well as dynamic fare calculation.

[0226] The matching service can provide optimal matching between passengers and vehicles / drivers and manage related policies.

[0227] Matching Management can support matching algorithms, matching optimization, and matching monitoring. Matching algorithms can support preference-based matching, real-time availability verification, and priority processing; matching optimization features can include minimizing latency, optimizing travel distance, and maximizing user satisfaction; and matching monitoring features can include real-time matching status tracking, performance metric monitoring, and problem detection.

[0228] The matching algorithm can analyze passengers' preferred driving styles through preference-based matching and evaluate drivers' driving patterns and styles. It can provide optimal matching by calculating the suitability between vehicle characteristics and passenger preferences and considering service quality history.

[0229] The real-time availability management function supports vehicle status monitoring, enabling real-time tracking of the location information of all vehicles and continuous updates to their operating status. Additionally, it can monitor battery and fuel status and provide real-time availability information to the matching system.

[0230] Pricing policies can support base rate management, dynamic rates, and profile-based rates. Base rate management may include distance / time-based rates, rates by vehicle class, and promotion management; dynamic rate features may include supply-demand-based rate adjustments, time-of-day rate fluctuations, and special circumstances rate policies; and profile-based rates may include customized profile add-ons, special purpose profile rates, and subscription model rates.

[0231] The dynamic fare system supports a fare calculation engine that can calculate base fares based on distance and time. It adjusts fares by reflecting real-time supply and demand conditions and calculates additional charges based on profile types. At the same time, various discounts and promotions can be applied.

[0232] The Scheduler can support dispatch optimization, route optimization, and resource management. Dispatching optimization features may include real-time demand forecasting, vehicle placement optimization, and waiting time management; route optimization features may include real-time route calculation, traffic condition reflection, and multi-waypoint optimization; and resource management features may include vehicle availability management, driver schedule management, and maintenance schedule management.

[0233] The monitoring service module of the cloud platform (310) can monitor the stability and performance of the entire system and respond to problematic situations.

[0234] The System Monitor can support performance monitoring, status monitoring, and traffic monitoring. Performance monitoring features may include real-time system performance tracking, resource usage monitoring, and response time monitoring; status monitoring features may include service availability monitoring, error log monitoring, and security status monitoring; and traffic monitoring features may include API call monitoring, network traffic analysis, and usage pattern analysis.

[0235] Alert Management can support alert settings, alert processing, and alert optimization. The alert setting function may include threshold settings, alert priority settings, and alert channel management; the alert processing function may include real-time alert sending, escalation management, and alert history management; and the alert optimization function may include alert deduplication, noise filtering, and alert effectiveness analysis.

[0236] Emergency Response can support failure response, emergency planning, and post-failure management. Failure response functions may include failure detection and classification, automatic recovery processes, and manual intervention procedures; emergency planning functions may include DR (Disaster Recovery) planning, BCP (Business Continuity Plan) management, and emergency contact management; and post-failure management functions may include failure analysis and reporting, measures to prevent recurrence, and system improvement plans.

[0237] The vehicle system (320) may include a vehicle control system, a sensor system, and a communication system. A real-time profile of the vehicle system (320) may be applied, and the ECU interface may be managed and emergency situations may be responded to by monitoring safety limit values.

[0238] The vehicle control module of the vehicle system (320) performs real-time vehicle control based on the occupant profile and is a core system that ensures safety, and may include a real-time controller, a safety controller, a vehicle monitor, etc.

[0239] The real-time controller can support control cycle management, vehicle dynamics control, and profile-based control. The control cycle management function may include a 10ms default control cycle, priority-based task scheduling, and operation based on a real-time operating system (RTOS). The vehicle dynamics control function may support longitudinal control (acceleration / braking), such as real-time adjustment of accelerator pedal mapping, optimization of braking force distribution, and regenerative braking control (EV); lateral control (steering), such as variable steering gear ratio control, power steering assist, and cornering stability control; and suspension control, such as real-time adjustment of damping force, body posture control, and ride comfort optimization. The profile-based control function may include real-time application of profile parameters, switching of control strategies based on driving conditions, and correction of dynamic parameters.

[0240] In other words, the real-time controller can accurately determine the current vehicle status through sensor data and set control goals based on the applied profile. It can verify the safety of all control commands and transmit the verified commands to the vehicle system.

[0241] The safety controller can support safety monitoring, emergency response, and safety recording. Safety monitoring functions may include vehicle limit state monitoring (maximum acceleration / deceleration limit, maximum lateral acceleration limit, vehicle speed limit), safety zone departure detection (lane departure detection, collision risk detection, slip / spin detection), and system error monitoring (sensor failure detection, actuator anomaly detection, communication error detection). Emergency response functions may include step-by-step safety measures (warning notification generation, control authority transfer, emergency stop) and fail-safe operations (backup system switching, safe mode switching, emergency recovery procedures). Safety recording functions may include safety event logging, black box data storage, and accident analysis data collection.

[0242] The safety controller supports the processing of safety constraints, allowing for the real-time application of acceleration limits and the restriction of maximum braking force depending on the situation. Additionally, it can dynamically adjust steering angle limits based on vehicle speed and ensure that all control commands remain within a safe range.

[0243] The vehicle monitor may include status monitoring, diagnostic functions, and reporting functions. The status monitoring function may include real-time vehicle status monitoring (engine / motor status, battery status, brake system status) and performance indicator measurement (fuel / energy efficiency, control responsiveness, ride comfort indicators). The diagnostic function may include self-diagnosis (system health check, sensor calibration, control performance evaluation) and predictive diagnosis (parts life prediction, maintenance timing prediction, performance degradation prediction). The reporting function may include real-time status reporting, performance analysis reports, and abnormal condition notifications.

[0244] The sensor system of the vehicle system (320) detects the vehicle, environment, and passenger status in real time and collects data, and may include vehicle status sensors, environmental sensors, passenger status sensors, etc.

[0245] Vehicle condition sensors may include dynamic sensors, powertrain sensors, chassis sensors, etc. Dynamic sensors may include an IMU (Inertial Measurement Unit) (acceleration sensor (100Hz), gyroscope sensor (100Hz), geomagnetic sensor), wheel sensors (wheel speed sensor, steering angle sensor, brake pressure sensor), etc. Powertrain sensors may include engine / motor sensors (RPM sensor, torque sensor, temperature sensor), transmission sensors (gear position sensor, oil temperature sensor, clutch position sensor), etc. Chassis sensors may include suspension sensors (ride height sensor, damper displacement sensor), tire sensors (tire pressure sensor, tire temperature sensor), etc.

[0246] Environmental sensors may include driving environment sensors and position / navigation sensors. Driving environment sensors may include weather sensors (rain detection sensors, temperature and humidity sensors, illuminance sensors), road condition sensors (road surface friction sensors, inclination sensors, unevenness detection sensors), etc., and position / navigation sensors may include GPS / GNSS (position information (1Hz), speed information, azimuth information), map matching (road information, speed limit information, curvature information), etc.

[0247] The passenger status sensor may include an occupancy status sensor and an interior environment sensor. The occupancy status sensor may include passenger detection sensors (seat pressure sensor, seatbelt sensor, posture detection sensor), biosignal sensors (heart rate sensor, respiration sensor, body temperature sensor), etc., and the interior environment sensor may include air conditioning sensors (indoor temperature sensor, humidity sensor, air quality sensor), noise / vibration sensors (microphone, vibration sensor), etc.

[0248] The data processing process for processing sensor data of a sensor system may include the process of collecting raw data from various sensors, the process of removing noise through a Kalman filter, the process of detecting and correcting abnormal data, and the process of estimating an accurate state by integrating data from multiple sensors.

[0249] Status monitoring of the sensor system supports vehicle status detection and may include the process of monitoring the vehicle's dynamic state in real time, verifying the operating status of key systems, rapidly detecting abnormal conditions, and generating detailed status reports.

[0250] The communication system of the vehicle system (320) is responsible for stable data communication between the vehicle and an external system and may include a communication manager, a gateway, data synchronization, etc.

[0251] The communication manager can support communication protocol management, QoS management, security management, etc. Communication protocol management functions may include in-vehicle communication (CAN / CAN-FD, FlexRay, Automotive Ethernet) and external communication (4G / 5G cellular, Wi-Fi, Bluetooth). QoS management functions may include bandwidth management (priority-based traffic control, bandwidth allocation, congestion control) and latency management (real-time data priority processing, latency monitoring, latency compensation algorithms). Security management functions may include data encryption, authentication and authorization management, and security threat detection.

[0252] The security system may include data encryption and access control functions.

[0253] An encryption management system can apply strong encryption to sensitive data and securely manage encryption keys. It can select appropriate encryption algorithms based on data types and guarantee the integrity of encrypted data.

[0254] The access control function allows for the application of granular access control policies within an access control management environment. It can log and monitor all access attempts, and detect and block abnormal access attempts. Regular security policy reviews can also be performed.

[0255] The gateway can support data transformation, data filtering, etc. Data transformation functions may include protocol transformation (internal protocol transformation, external protocol transformation), data format transformation (message formatting, encoding / decoding), and address transformation (internal / external address mapping, routing table management). Data filtering functions may include message filtering (removal of duplicate data, filtering of unnecessary data) and data validation (integrity checks, format validation).

[0256] An API gateway supports a routing management system through request routing, enabling it to route client requests to the appropriate service. It distributes system load through load balancing, provides alternative paths in the event of service failure, and continuously monitors routing performance. An API gateway may include an authentication system for authentication and authorization management. In this case, the authentication system can perform authentication for all API requests. It can implement token-based access control, manage user-specific permissions granularly, and generate and manage security audit logs.

[0257] Data synchronization can support real-time synchronization, offline data management, and data recovery. Real-time synchronization features may include status synchronization (vehicle status synchronization, control command synchronization, profile synchronization) and time synchronization (GPS time synchronization, NTP server synchronization, distributed system time synchronization). A synchronization management system for real-time data synchronization can maintain data consistency across multiple systems and manage the priority of synchronization tasks. It can detect and resolve data conflicts and continuously monitor the synchronization status. Offline data management features may include data caching (local cache management, cache policy management) and retransmission management (retransmission of failed data, guaranteed order transmission, prevention of duplicate transmission). Offline data management features may include processes for securely storing data locally in the event of network disconnection, automatically performing synchronization upon network recovery, determining the synchronization order based on data priority, and applying automatic retry policies in the event of synchronization failure. Data recovery features may include communication failure recovery (automatic reconnection, data recovery, session recovery) and data backup (backup of critical data, recovery point management).

[0258] The vehicle status monitor of the vehicle system (320) can collect sensor data from the entire vehicle to perform real-time status analysis, and at this time, can detect abnormal situations, collect performance metrics, etc.

[0259] Mobile applications (330) can be divided into passenger apps (ride-hailing apps) and driver apps.

[0260] The passenger app of the mobile application (330) may include features such as a profile selection interface, real-time driving information display, feedback input function, and fare information display.

[0261] The passenger app provides an interface that allows passengers to set their preferences, call a vehicle, and monitor the ride quality, and may include profile management, vehicle calling, real-time monitoring, and a feedback system.

[0262] The profile management function of the passenger app is a preference setting environment that can support driving style settings (selection of Comfort / Eco / Sport modes, adjustment of acceleration / deceleration intensity, setting of cornering preference), ride comfort settings (adjustment of suspension intensity, saving of seat position, interior environment settings), and situational profiles (commuting profile, long-distance driving profile, special purpose profile (pregnant women, motion sickness prevention, etc.)).

[0263] The profile management function of the passenger app is a ride-comfort-based profile learning environment and may include real-time satisfaction input (acceleration / deceleration satisfaction, cornering satisfaction, overall ride-comfort satisfaction), automatic profile adjustment (satisfaction-based parameter optimization, AI-based preference learning, step-by-step profile evolution), feedback history analysis (satisfaction tracking by driving segment, preference analysis by road conditions, preference pattern analysis by time of day), and customized profile suggestions (recommendations based on learned preferences, reference to similar user profiles, suggestion of optimal profiles for specific situations).

[0264] The aforementioned profile selection interface allows users to intuitively adjust their preferred ride comfort settings through profile setting management. Detailed settings for acceleration, braking, and cornering are possible, the validity of the configured profile can be verified immediately, and changed settings can be synchronized with the server in real time.

[0265] The passenger app's ride-hailing function serves as a vehicle search environment and can support real-time location-based search (GPS-based current location verification, display of nearby available vehicles, calculation of estimated arrival times), and vehicle filtering (filters by vehicle type, check for profile support, filters based on driver ratings).

[0266] The passenger app's ride-hailing function serves as a reservation and payment environment that supports reservation management (real-time reservations, advance reservations, regular reservations), fare verification (base fare calculation, profile-based surcharges, application of discounts / promotions), and payment processing (support for various payment methods, automatic payment, receipt issuance).

[0267] The real-time monitoring function of the passenger app can support ride comfort monitoring, route and arrival information, etc. The ride comfort monitoring function can support driving status display (current speed / acceleration, vehicle movement information, ride comfort quality score) and profile application status (currently applied profile, control parameter status, real-time adjustment status). Route and arrival information may include real-time location tracking (current location on the map, display of travel path, remaining distance / time) and traffic information (real-time traffic conditions, route recalculation, detour route suggestion).

[0268] The passenger app can support real-time ride comfort analysis through ride experience monitoring. Ride comfort data can be collected using sensors on mobile devices, measuring acceleration, vibration, and movement at a frequency of 100Hz. The collected data can be analyzed in real time to evaluate ride comfort quality and generate immediate notifications in the event of discomfort.

[0269] The feedback system of the passenger app can support real-time feedback and history management. Real-time feedback features may include ride comfort evaluations (5-point scale evaluations, evaluations by detailed categories, comment input), reporting inconveniences (safety-related reports, service complaints, emergency notifications), and profile feedback (requests for real-time profile adjustments, input of improvements during driving, recording of preferences by situation, automatic profile update settings). History management features may include driving history (past usage history, profile usage history, payment history) and statistical analysis (usage pattern analysis, preference trends, cost analysis).

[0270] The passenger app's real-time feedback system collects immediate feedback on ride comfort during driving and analyzes the collected feedback in real time. The analysis results can be reflected in profile optimization, and feedback data can be periodically transmitted to the server.

[0271] The driver app may include functions such as checking the profile application status, displaying real-time driving information, managing revenue information, and monitoring vehicle status.

[0272] The driver app can provide features to check vehicle operation information and passenger profiles, and support efficient operation.

[0273] The driving management function of the driver app can support call management and route guidance. The call management function may include call reception (real-time call notification, passenger information verification, profile requirement verification) and acceptance / rejection (checking profile compatibility, considering distance / time, automatic matching settings), and the route guidance function may include navigation (optimal route guidance, reflection of real-time traffic information, voice guidance) and pick-up / drop-off points (precise location guidance, display of restricted pick-up / drop-off zones, guidance on safe stopping zones).

[0274] The profile application function of the driver app can support profile verification and real-time feedback. The profile verification function may include checking passenger preferences (driving style requirements, special requirements, precautions) and vehicle settings (automatic profile application, range of manual adjustments, checking limitations), while the real-time feedback function may include driving quality monitoring (real-time display of ride comfort indicators, verification of profile compliance, reception of improvement suggestions) and driver guidance (optimal driving guides, safe driving alerts, profile optimization suggestions).

[0275] The driver app's profile management system allows users to view detailed information on currently applied passenger profiles. It can automatically verify compatibility between the profile and the vehicle, monitor the profile application status in real-time, and provide quick response guidelines in the event of issues.

[0276] The revenue management function of the driver app can support run revenue and performance analysis. The run revenue function may include real-time revenue verification (base fare, profile bonus revenue, incentives / tips) and settlement management (daily / weekly / monthly settlements, commission verification, tax calculation), while the performance analysis function may include run statistics (driving distance / time, acceptance / completion rate, average rating) and revenue analysis (revenue by time of day, revenue by profile, revenue trends).

[0277] The vehicle management features of the driver app can support vehicle status and driving optimization. Vehicle status features may include real-time monitoring (vehicle system status, fuel / battery status, status of major components) and maintenance management (maintenance schedule notifications, parts replacement timing, repair shop reservations), while driving optimization features may include energy efficiency (fuel / electric efficiency monitoring, efficient driving guides, charging / refueling planning) and work optimization (recommendations for optimal driving time, break time management, demand forecasting information).

[0278] The driving monitoring system can support real-time performance monitoring, displaying key vehicle performance indicators in real time and continuously verifying compliance with the profile. Additionally, it can generate performance reports on fuel efficiency, ride comfort, and more, and provide feedback on areas requiring improvement.

[0279] The passenger preference-based real-time vehicle control system according to the present invention can support system linkage between a cloud platform (310), a vehicle system (320), and a mobile application (330).

[0280] In a system integration environment, data flow for profile distribution, status monitoring, and user interaction can be supported.

[0281] In the case of profile distribution, real-time updates from the cloud platform (310) to the vehicle system (320) can be supported, and profile changes can be applied immediately.

[0282] For status monitoring, real-time data transmission from the vehicle system (320) to the cloud platform (310) and performance analysis feedback can be supported.

[0283] For user interaction, real-time state synchronization can be supported through bidirectional communication between the cloud platform (310) and the mobile application (330).

[0284] The aforementioned system integration environment can support security requirements including endpoint authentication, data encryption, access control management, and audit log recording.

[0285]

[0286] FIG. 5 illustrates a detailed configuration diagram of a profile service module in an embodiment of the present invention, and FIG. 6 illustrates a detailed configuration diagram of a vehicle control system in an embodiment of the present invention.

[0287] Referring to FIG. 5, the profile service module may include a profile management module (510), a data processing module (520), and a service interface module (530). In this case, the profile management module (510) may include a profile manager (511), a profile validator (512), and a profile optimizer (513); the data processing module (520) may include a data collector (521), a data processor (522), and a data analyzer (523); and the service interface module (530) may include an API gateway (531), an authentication service (532), and a billing service (533).

[0288] The profile manager (511) can manage the entire profile lifecycle, and can provide profile creation, modification, and deletion functions and perform profile version management.

[0289] The profile validator (512) performs validation of a newly created or modified profile, and can verify the consistency of the profile components and check whether safety-related rules are complied with.

[0290] The profile optimizer (513) can perform profile optimization based on the results of data analysis. It can adjust parameters for performance improvement and improve the profile by reflecting real-time feedback.

[0291] The data collector (521) is responsible for collecting profile-related data from various sources. It can integrate sensor data, user feedback, performance logs, etc., and perform data quality verification and preprocessing.

[0292] The data processor (522) can perform cleaning and structuring of the collected data. It can perform data format conversion and standardization, and data conversion into an analyzable form.

[0293] The data analyzer (523) can perform in-depth analysis of the processed data. It can support pattern recognition, trend analysis, and the derivation of insights for optimization.

[0294] The API gateway (531) provides a communication interface with external systems. It can support API request / response processing and routing, security and access control management, etc.

[0295] The authentication service (532) can provide user and system authentication processing, authorization management and access control, security token management, etc.

[0296] The billing service (533) may include measuring and recording service usage, applying billing policies and calculating costs, processing payments and generating invoices, etc.

[0297] Referring to FIG. 6, the vehicle control module may include a real-time control module (610), an actuator control module (620), and a sensor system (630). In this case, the real-time control module (610) may include a profile converter (611), a real-time controller (612), and a safety controller (613), the actuator control module (620) may include an actuator controller (621), an actuator monitor (622), and an actuator feedback (623), and the sensor system (630) may include various sensors (631), a sensor filter (632), and a sensor analyzer (633).

[0298] The profile converter (611) can convert the vehicle control profile into a real-time executable form to generate profile-based control parameters.

[0299] The real-time controller (612) performs real-time control based on sensor data and profile information, and can ensure safety by linking with the safety controller and adjust control commands through real-time feedback.

[0300] The safety controller (613) can monitor safety-related constraints of the vehicle and can support detection and response to dangerous situations, and verification of the safety of control commands.

[0301] The actuator controller (621) can convert upper control commands into actuator control signals and execute control logic for each actuator.

[0302] The actuator monitor (622) monitors the operating status of the actuator in real time and can detect and report abnormal operation.

[0303] Actuator feedback (623) collects the actual operation results of the actuator and can process the feedback data and transmit it to the upper system.

[0304] The sensor system (630) can collect data from various vehicle sensors (631) to perform sensor status monitoring and management.

[0305] The sensor filter (632) can perform filtering and noise removal of collected sensor data and verify the reliability of the data.

[0306] The sensor analyzer (633) analyzes filtered sensor data, performs integrated analysis with actuator feedback data, and can provide data for real-time control.

[0307] The profile manager (511) can manage the profile lifecycle (creation / modification / distribution / discarding), and can support profile integrity verification and version control, compatibility management by vehicle model, and the establishment and execution of profile distribution strategies.

[0308] The profile manager (511) can perform parameter normalization based on the vehicle dynamics model and convert the data into a real-time controllable form for structuring the profile data. For example, in the case of the electric vehicle Model 3, it can handle motor torque curve optimization (0-100 km / h 4.5 seconds → 6.0 seconds), regenerative braking intensity adjustment (maximum 0.2G → 0.15G), and suspension damping adjustment (comfort mode +20% softing).

[0309] The profile manager (511) can apply differentiated control logic for each powertrain type to reflect vehicle-specific characteristics and can perform chassis system response characteristic correction. For example, to differentiate between EV and ICE vehicles, for EVs, it can apply prevention of sudden acceleration by limiting initial torque, and for ICEs, it can apply improvement of ride comfort by optimizing shift timing.

[0310] The profile optimizer (513) can learn deep learning-based driving patterns through an optimization algorithm and can perform parameter optimization using a genetic algorithm. In addition, real-time adaptive control based on reinforcement learning can be applied.

[0311] Ride comfort-related items targeted for optimization may include longitudinal acceleration profiles (minimizing jerk), lateral acceleration control (reducing roll during cornering), and powertrain control for vibration / noise reduction. Actual application examples may include profiles for motion sickness-sensitive passengers in taxi services, acceleration limits of 0.15G or less, and maintaining lateral acceleration of 0.2G or less during cornering.

[0312] Items related to energy efficiency that are subject to optimization may include deriving the optimal energy consumption point for each driving situation and optimizing regenerative braking strategies. Actual application examples may include an improved fuel efficiency profile for long-distance taxis, an efficiency improvement of more than 10% through gradual acceleration and deceleration, and the maximization of braking energy recovery rates through situation-aware regenerative braking.

[0313] The real-time controller (612) can apply control algorithms such as optimal control based on Model Predictive Control, real-time correction through an Adaptive PID controller, and feedback control based on a state observer. Key control items may include longitudinal control and lateral control. Longitudinal control may include real-time adjustment of accelerator pedal mapping and optimization of braking force distribution, and actual application examples may include initial torque limiting to suppress sudden starts and smooth stopping through creeping control just before stopping. Lateral control may include variable steering gear ratio control and vehicle speed-linked power steering assist, and actual application examples may include steering reduction during low-speed parking and improvement of straight-line stability during high-speed driving.

[0314] The safety controller (613) may apply safety logic such as a triple redundant safety system, a fail-safe mechanism, and a watchdog timer-based system monitoring. Key safety functions may include limit situation detection and emergency situation response. The limit situation detection function may include real-time tire grip estimation and body posture stability monitoring, and actual application examples may include automatic driving force limiting in rainy conditions and automatic deceleration during overspeed cornering. The emergency situation response function may include the implementation of a graceful degradation strategy and automatic switching to a backup control mode, and actual application examples may include control utilizing a replacement sensor in case of sensor failure and switching to a safety mode in case of communication loss.

[0315] The data collector (521) can collect vehicle dynamics data (50Hz sampling), ride comfort data (100Hz sampling), environment / road information (1Hz update), etc.

[0316] The data processor (522) can apply Kalman filter-based noise removal, sensor fusion algorithms, etc. to the collected data, and, for example, can ensure improved precision by filtering acceleration sensor data and ensure the reliability of the measurement data by integrating multiple sensors.

[0317] The data analyzer (523) can apply analysis algorithms such as pattern recognition and anomaly detection, and can perform performance indicator analysis such as ride comfort evaluation and energy efficiency analysis.

[0318] The data analyzer (523) can perform wavelet transform-based driving pattern analysis and SVM-based driving style classification through pattern recognition. For example, it can derive 10 driving style profiles by learning driver driving patterns or match optimal driving patterns by time of day / weather.

[0319] The data analyzer (523) can perform LSTM-based abnormal state prediction and statistical process control (SPC)-based quality control through abnormality detection. For example, it can suggest a preemptive maintenance time by predicting part wear and support automatic detection and response to driving quality deterioration sections.

[0320] The data analyzer (523) can perform ride comfort evaluations such as ISO 2631-based vibration evaluation and NVH (Noise, Vibration, Harshness) analysis, and can support, for example, ride comfort scoring (1-100 points) by road condition, generation of ride comfort heatmaps by section, and automatic profile adjustment.

[0321] The data analyzer (523) can perform energy efficiency analysis, such as calculating a Driving Efficiency Index and analyzing energy consumption patterns, and can support, for example, calculating energy efficiency rankings by driver and deriving optimal energy consumption patterns by route.

[0322] The occupant preference-based real-time vehicle control system according to the present invention may include a Service Platform Module and an Integrated Control Module.

[0323] The service platform module may include a profile marketplace and a real-time matching system.

[0324] The commercialization strategy of the profile marketplace may include profile packaging and quality control. Profile packaging can support purpose-specific profile bundling and the implementation of subscription service models, such as business-specific profiles (VIP transport, dedicated to pregnant women, etc.) and season-specific profiles (response to rain, snow, heatwaves, etc.). Quality control can support A / B test-based profile verification and user feedback-based optimization, such as operating a profile rating system (out of 5 points) and a driving quality assurance system.

[0325] The real-time matching system can apply matching algorithms such as preference-based matching and supply-demand optimization. Preference-based matching can support recommendations based on collaborative filtering and dynamic matching based on context awareness; for example, this may include matching drivers with passenger preferred driving styles and automatically applying preference patterns by time of day. Supply-demand optimization can support real-time demand forecasting and dynamic pricing; for example, this may include providing incentives when demand for a specific profile increases and operating differential pricing schemes based on profiles.

[0326] The integrated relationship module may include a real-time monitoring system and an emergency response system.

[0327] The monitoring items of the real-time monitoring system may include system health and service quality. System health may include real-time monitoring of profile application status and vehicle system responsiveness; actual application examples may include automatic recovery in case of profile operation anomalies and optimization of resource allocation based on system load. Service quality may include management of Service Level Agreement (SLA) compliance rates and user experience monitoring; actual application examples may include real-time ride comfort satisfaction surveys and the generation of reports analyzing the effectiveness of profile application.

[0328] The response protocols of the emergency response system can support anomaly response and recovery management. Anomaly response can support a multi-stage emergency response system and automatic / manual response processes; practical applications may include automatic switching to safe mode in the event of a profile conflict and immediate activation of backup profiles in the event of a system failure. Recovery management can support failure recovery priority management and service continuity assurance; practical applications may include a recovery goal within 5 minutes of a failure and non-disruptive profile switching system operation.

[0329]

[0330] FIG. 7 illustrates a detailed configuration diagram of a profile management system in one embodiment of the present invention, FIG. 8 illustrates a detailed configuration diagram of a real-time vehicle control system in one embodiment of the present invention, and FIG. 9 illustrates a detailed configuration diagram of a user interface system in one embodiment of the present invention.

[0331] Referring to FIG. 7, the profile management system may include a monitoring and analysis module (710), a profile optimization module (720), a profile creation and verification module (730), and a profile distribution module (740). In this case, the monitoring and analysis module (710) may include a monitoring agent (711) and a notification system (712), the profile optimization module (720) may include a data analyzer (721), a machine learning engine (722), and a profile optimizer (723), the profile creation and verification module (730) may include a profile generator (731), a profile verifier (732), and a profile repository (733), and the profile distribution module (740) may include a distribution manager (741), a version control system (742), and a quality control system (743).

[0332] In the monitoring and analysis phase, system data is collected through a monitoring agent (711) and the monitoring results are transmitted through a notification system (712).

[0333] The profile optimization module (720) can analyze big data-based profile performance and can derive optimal parameters through an AI model, monitor real-time profile performance, and perform automatic optimization and improvement processes.

[0334] In the profile optimization step, data collected through the data analyzer (721) can be analyzed, data optimization can be performed using the machine learning engine (722), and an optimized profile can be generated through the profile optimizer (712).

[0335] The profile generation and verification module (730) can provide a basic profile template for each vehicle model and can perform real-time profile validation, profile version management and history tracking, safety verification and certification processes.

[0336] In the profile generation and verification step, a new profile is generated through the profile generator (731), the validity of the generated profile is verified through the profile verifier (732), and the verified profile is stored in the profile storage (733).

[0337] The profile distribution module (740) distributes customized profiles for each vehicle and can support a phased rollout strategy, emergency updates and rollbacks, and distribution status monitoring.

[0338] In the profile distribution phase, the profile distribution process is managed through the distribution manager (741), the actual distribution of the profile is performed through the version control system (742), and the quality of the distributed profile can be checked through the quality control system (743).

[0339] Referring to FIG. 8, the real-time vehicle control system may include a sensor integration module (810), a profile execution module (820), a control processing module (830), and a safety management module (840). In this case, the sensor integration module (810) may include a vehicle sensor (811), an environment sensor (812), a sensor processor (813), and a sensor integrator (814), and the profile execution module (820) may include a profile executer (821), a performance monitor (822), and a data logger (823). Additionally, the control processing module (830) may include a state analyzer (831), a real-time controller (832), a safety controller (833), and an actuator controller (834), and the safety management module (840) may include a safety monitor (841) and an emergency manager (842).

[0340] The sensor integration module (810) synchronizes data from multiple sensors, such as a vehicle sensor (811) and an environment sensor (812), and can support real-time data filtering and correction, application of sensor fusion algorithms, and detection and correction of outliers. The sensor integration module (810) collects vehicle-related data from the vehicle sensor (811) and the environment sensor (812), and can integrate and process the data from the two sensors in the sensor processor (813). The data processed in the sensor processor (813) can be integrated into a single data stream through the sensor integrator (814).

[0341] The profile execution module (820) can execute a vehicle control profile in the profile executer (821), and the performance monitor (822) can monitor the performance of the profile being executed in real time. At this time, performance data can be recorded and stored through the data logger (823).

[0342] The control processing module (830) can provide a real-time control response within 10ms, utilize multilayer safety control logic, situation-adaptive control algorithms, etc., and support hardware acceleration processing, etc. The state analyzer (831) can analyze sensor fusion data to determine the vehicle state, and the real-time controller (832) can generate control commands by reflecting profile execution data and safety monitoring information. The safety controller (833) can adjust the control commands by applying safety-related constraints and transmit the final control commands to the vehicle system through the actuator controller (834).

[0343] The safety management module (840) is composed of a triple redundant safety system and can support real-time danger detection, an automatic emergency response system, and safety data logging. The safety monitor (841) continuously monitors the safety status of the system, and the emergency manager (842) can take immediate response measures when a danger occurs. At this time, safety-related information can be transmitted to the real-time controller (832) and the safety controller (833) to ensure safe driving.

[0344] Referring to FIG. 9, the user interface system supports user interfaces for the administrator console (910), driver app (920), and passenger app (930). The administrator console (910) can provide user interfaces for a monitoring system, control system, reporting system, and configuration management. The driver app (920) can provide user interfaces for driving status, profile application, performance statistics, and notification center. The passenger app (930) can provide user interfaces for profile selection, preference setting, feedback system, and real-time monitoring.

[0345] The administrator console (910) may include a system monitoring dashboard and a profile management system.

[0346] The system monitoring dashboard supports a real-time monitoring system, allowing users to grasp the health status of the entire system at a glance and monitor user activity and vehicle status in real time. Additionally, it can send notifications through various channels by comprehensively analyzing profile performance indicators.

[0347] The profile management system supports profile distribution management, enabling thorough verification of new profiles prior to deployment. It minimizes risk through a phased deployment strategy and provides a system capable of detecting issues during the deployment process in real-time and performing rapid rollbacks when necessary.

[0348] In other words, the administrator console (910) can provide an integrated monitoring dashboard, real-time control and intervention, generation of detailed analysis reports, system configuration management, etc.

[0349] The driver app (920) can provide real-time vehicle status monitoring, check profile application status, performance indicators and statistical analysis, and notification and response to abnormal situations.

[0350] The passenger app (930) can provide real-time ride comfort feedback, personalized profile recommendations, driving history and statistics, etc.

[0351] FIG. 10 illustrates a passenger boarding process sequence in one embodiment of the present invention.

[0352] Referring to FIG. 10, the profile selection and matching step may include the process of vehicle calling → profile selection → vehicle matching. In the profile selection and matching step, matching of passenger preferences and vehicle characteristics, verification of driver profile support, and real-time location-based optimal matching may be performed.

[0353] The profile application step may include the process of profile transmission, verification, and application. During the profile application step, vehicle model compatibility verification, safety constraint checks, and real-time application status monitoring may be performed.

[0354] FIG. 11 illustrates a profile-based vehicle control sequence in one embodiment of the present invention.

[0355] Referring to FIG. 11, in the sensor data processing step, vehicle dynamic data, driver input data, environmental sensor data, etc., can be collected at a fixed collection period (e.g., 10 ms). The sensor data processing step can perform data processing such as multi-sensor fusion, noise filtering, and outlier removal.

[0356] In the control logic execution phase, profile-based parameters can be applied at a fixed execution cycle (e.g., 10ms) to perform real-time correction value calculation, control command generation, etc. Control items may include acceleration / braking control, steering responsiveness adjustment, suspension damping adjustment, etc.

[0357] FIG. 12 illustrates a safety monitoring process sequence in one embodiment of the present invention.

[0358] Referring to Fig. 12, safety monitoring can be performed at a fixed monitoring interval (e.g., 50ms) during the real-time monitoring phase. Monitoring items may include vehicle driving status, profile application status, system health, etc., and key indicators such as vehicle dynamic stability, control responsiveness, and system reliability may be utilized.

[0359] In the hazardous situation response phase, support can be provided for responding to hazardous situations such as sudden acceleration / sudden braking, excessive lateral acceleration, and system errors. Response procedures may include the immediate application of safety controls, temporary suspension of the profile, notification to the control center, and execution of emergency measures.

[0360]

[0361] The passenger preference-based real-time vehicle control technology according to the present invention can be applied to shared vehicle services.

[0362] In the case of car-sharing services, automatic application of user preference profiles upon vehicle rental, provision of optimized driving characteristics for each vehicle type, and real-time vehicle status monitoring and management can be supported, and the specific implementation plan is as follows.

[0363] 1) User Profile Registration - Setting Preferred Driving Style - Analyzing Safe Driving Tendencies - Learning Past Usage Patterns 2) Automatic Vehicle Optimization - ECU Control Map Adjustment - Setting Pedal Responsiveness - Adjusting Steering Sensitivity 3) Real-time Monitoring - Checking Vehicle Status - Detecting Abnormal Driving - Predicting Maintenance

[0364] For corporate vehicle management, support can be provided for optimizing business vehicle driving patterns, customized settings for multiple drivers, and maximizing vehicle operational efficiency. The specific operational plans are as follows.

[0365] 1) Driver Profile Management - Authentication-based Automated Application - Driving Pattern Analysis - Promoting Safe Driving 2) Vehicle Operation Optimization - Improved Fuel Efficiency - Extended Component Lifespan - Reduced Accident Risk 3) Integrated Control System - Real-time Location Tracking - Driving Record Management - Automated Cost Settlement

[0366] In addition, the passenger preference-based real-time vehicle control technology according to the present invention can be applied to the autonomous vehicle market. In the case of a robot taxi service, it can support the adjustment of autonomous driving patterns based on passenger preferences, real-time ride comfort monitoring and optimization, and AI-based driving style personalization, and specific implementation methods are as follows.

[0367] 1) Passenger-Customized Driving - Preferred Acceleration Profile - Cornering Style Settings - Stopping Pattern Optimization 2) Context-Adaptive Control - Reflection of Weather / Road Conditions - Traffic Flow Prediction - Passenger Status Detection 3) Intelligent Services - Real-time Route Optimization - Passenger Condition Monitoring - Emergency Response

[0368] For autonomous logistics services, it is possible to support the application of optimal driving patterns based on cargo characteristics, driving control based on cargo safety, and operation optimized for energy efficiency, and the specific operational plan is as follows.

[0369] 1) Customized Cargo Control - Weight / Size-Based Optimization - Special Handling for Cautionary Cargo - Monitoring of Temperature-Sensitive Cargo 2) Efficient Transport - Optimal Route Calculation - Maximizing Fuel Efficiency - Just-in-Time Delivery Management 3) Ensuring Safety - Real-Time Cargo Status Monitoring - Predictive Response to Hazards - Automated Documentation / Reporting

[0370] In addition, the passenger preference-based real-time vehicle control technology according to the present invention can be applied to the field of app-based taxi hailing services. In the case of premium taxi services, it can support vehicle / driver matching based on customer preference, real-time ride quality management, and operation of differentiated fare systems, and the specific service implementation is as follows.

[0371] 1) Customized Matching - Passenger Profile Analysis - Driver Style Matching - Consideration of Vehicle Characteristics 2) Quality Management - Real-time Driving Quality Assessment - Driver Feedback Provision - Service Class Management 3) Pricing Differentiation - Pricing by Profile - Additional Service Options - Membership Discount Benefits

[0372] For general taxi services, support can be provided for optimizing basic ride comfort profiles, applying safe driving assistance systems, and operating efficient dispatch systems, and specific operational plans are as follows.

[0373] 1) Standardized Service - Application of basic profile - Safe driving guidelines - Incorporation of passenger feedback 2) Operational Streamlining - Real-time demand forecasting - Optimal dispatch algorithm - Operational efficiency analysis 3) Quality Management - Driver evaluation system - Service monitoring - Feedback on improvements

[0374] Accordingly, according to embodiments of the present invention, customized ride comfort can be provided through AI-based personalized profile generation, real-time driving environment adaptive control, and multi-sensor-based precision control, thereby improving passenger satisfaction and reducing motion sickness incidence and ride comfort quality deviation. According to embodiments of the present invention, safety can be improved by reducing sudden braking / sudden acceleration through 10ms cycle real-time control, a triple-layered safety system, and prediction-based risk situation prevention, and can support hardware-software integrated safety design, a self-diagnosis and recovery system, a real-time remote monitoring system, etc.

[0375] According to embodiments of the present invention, ride comfort satisfaction can be improved through fully automated ride comfort optimization, real-time situation-adaptive service, and individual preference learning functions, thereby increasing the service reuse rate.

[0376] The device described above may be implemented as a hardware component, a software component, and / or a combination of a hardware component and a software component. For example, the device and components described in the embodiments may be implemented using one or more general-purpose or special-purpose computers, such as a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing unit may execute an operating system (OS) and one or more software applications executed on said operating system. Additionally, the processing unit may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing unit may be described as being used as a single unit, but those skilled in the art will understand that the processing unit may include multiple processing elements and / or multiple types of processing elements. For example, the processing unit may include multiple processors or one processor and one controller. In addition, other processing configurations, such as parallel processors, are also possible.

[0377] Software may include computer programs, code, instructions, or a combination of one or more of these, and may configure a processing unit to operate as desired or instruct the processing unit independently or collectively. Software and / or data may be embodied in any type of machine, component, physical device, computer storage medium, or device so as to be interpreted by the processing unit or to provide instructions or data to the processing unit. Software may be distributed over networked computer systems and may be stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

[0378] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. In this case, the medium may continuously store a program executable by a computer, or temporarily store it for execution or download. Additionally, the medium may be various recording or storage means in the form of a single or several hardware combined, and may not be limited to a medium directly connected to a computer system but may exist distributed over a network. Examples of media may include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and media configured to store program instructions, including ROM, RAM, and flash memory. Additionally, other examples of media may include recording or storage media managed by app stores that distribute applications or sites and servers that supply or distribute various other software.

[0379] Although the embodiments have been described above with reference to limited examples and drawings, those skilled in the art can make various modifications and variations from the description above. For example, suitable results can be achieved even if the described techniques are performed in a different order than described, and / or the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.

[0380] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.

Claims

1. A real-time vehicle control method based on passenger preference performed by a computer device comprising at least one processor, wherein A step of generating and managing a preference profile that quantifies the occupant's preference for acceleration intensity, deceleration intensity, and cornering speed during vehicle driving; and A step of controlling vehicle driving parameters linked to a vehicle ECU (electronic control unit) by monitoring compliance with the preference profile based on real-time driving data collected from sensors of the vehicle of the said passenger according to the said passenger. A real-time vehicle control method based on passenger preference including 2. In Paragraph 1, The above-mentioned management step is, A step of learning the preference profile of the said passenger based on the passenger's boarding history. A real-time vehicle control method based on passenger preference including 3. In Paragraph 1, The above-mentioned management step is, A step of recommending a preference profile for the passenger based on the passenger's boarding history. A real-time vehicle control method based on passenger preference including 4. In Paragraph 1, The above-mentioned management step is, Step of setting a preference profile for the passenger according to the passenger's boarding situation A real-time vehicle control method based on passenger preference including 5. In Paragraph 1, The above-mentioned management step is, A step of providing preset driving profiles including profiles by age group, gender, region, weather, occupant type, and purpose of ride. A real-time vehicle control method based on passenger preference including 6. In Paragraph 1, The above-mentioned controlling step is, A step of controlling vehicle driving acceleration by limiting the output of the vehicle motor according to the above preference profile A real-time vehicle control method based on passenger preference including 7. In Paragraph 1, The above-mentioned controlling step is, A step of adjusting the vehicle driving parameters in real time by monitoring compliance with the preference profile based on real-time driving data collected from a sensor of a moving vehicle at a 10ms interval. A real-time vehicle control method based on passenger preference including 8. In Paragraph 1, The above-mentioned controlling step is, A step of applying safety limits, including the maximum allowable speed set in the above preference profile, to the vehicle ECU A real-time vehicle control method based on passenger preference including 9. In Paragraph 1, The above-mentioned controlling step is, A step of adjusting the vehicle's suspension and steering sensitivity in real time according to the above preference profile A real-time vehicle control method based on passenger preference including 10. In Paragraph 1, The above-described passenger preference-based real-time vehicle control method is, A step of performing vehicle matching for the occupant by considering the occupant's preference profile and vehicle characteristics. A real-time vehicle control method based on passenger preference that further includes 11. In Paragraph 1, The above-described passenger preference-based real-time vehicle control method is, A step of performing driver matching between the passenger's preferred driving style and the passenger based on the passenger's preference profile. A real-time vehicle control method based on passenger preference that further includes 12. In Paragraph 1, The above-described passenger preference-based real-time vehicle control method is, A step for monitoring the profile application status of the moving vehicle and the vehicle driving status. A real-time vehicle control method based on passenger preference that further includes 13. In Paragraph 1, The above-described passenger preference-based real-time vehicle control method is, A step of temporarily suspending the application of the above preference profile when a dangerous situation defined as a detection item is detected for a moving vehicle. A real-time vehicle control method based on passenger preference that further includes 14. In Paragraph 1, The above-described passenger preference-based real-time vehicle control method is, Step of calculating differential fares based on the above-mentioned passenger preference profile A real-time vehicle control method based on passenger preference that further includes 15. At least one processor implemented to execute readable instructions on a computer device Includes, The above-mentioned at least one processor is, A process of generating and managing a preference profile that quantifies the occupant's preference for acceleration intensity, deceleration intensity, and cornering speed during vehicle driving; and A process of controlling vehicle driving parameters linked to the vehicle ECU (electronic control unit) by monitoring compliance with the preference profile based on real-time driving data collected from the sensors of the vehicle driven by the said passenger, depending on the said passenger. A computer device that processes.

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