1414 results about "Smart vehicle" patented technology

The invention discloses a vehicle multi-modal trajectory prediction method based on an improved attention network, and belongs to the technical field of intelligent vehicle trajectory prediction, and the method comprises the steps: collecting historical trajectory data of a target vehicle and surrounding vehicles as an input sequence; secondly, constructing a vehicle multi-modal trajectory prediction model which comprises a motion feature extraction module, a space-time interaction module, a space-time fusion module and a trajectory output module; the motion feature extraction module uses a multi-scale convolution attention network and a gating circulation unit for processing, the space-time interaction module uses a dynamic graph attention network for extracting vehicle interaction information, and the space-time fusion module splices and fuses target vehicle motion features and space-time interaction features to obtain space-time fusion features; the track output module inputs the fusion features into a gating circulation unit, decodes the fusion features and then inputs the fusion features into a mixed density network, and multi-mode output of vehicle tracks is achieved; and finally, a proper loss function is selected for training, so that the prediction precision and the convergence speed of the model are improved.

The invention discloses a control strategy model construction method based on big data, and relates to the technical field of intelligent control, a traffic situation deduction and emergency strategy generation module uses a multi-agent traffic simulation technology, simulates a traffic situation in combination with real-time traffic data, evaluates risks and early warns potential crisis by integrating multiple factors, and provides a control strategy model for the traffic situation deduction and emergency strategy generation module. And after an early warning is received, an emergency strategy is generated by using an intelligent algorithm, the strategy is evaluated and optimized by using virtual rehearsal, a result is fed back to a management department, and a synergistic effect with other modules is achieved. Traditional traffic detection equipment data is integrated with multi-source heterogeneous data such as mobile phone signaling, shared bicycle and online hailed bicycle GPS, intelligent vehicle-mounted equipment and public traffic operation, more comprehensive and accurate information such as traffic flow, flow direction, travel behavior track and the like is provided, and rich and accurate basis is provided for traffic management decisions.

The invention provides a scene adaptive projection vehicle lamp system based on deep reinforcement learning and a control method, and relates to the technical field of intelligent vehicle lamps and automatic driving perception systems. Comprising a multi-modal sensing module, a feature fusion module, a strategy generation module and an execution module. The multi-mode sensing module is used for collecting environment state data and performing primary processing to form an environment data information flow; the feature fusion module is used for generating a unified environment feature vector for the environment data information flow; the strategy generation module is used for receiving the environment feature vector and generating a vehicle lamp adjustment strategy through multi-layer neural network calculation; evaluating a result obtained by executing the vehicle lamp adjustment strategy based on the vehicle lamp, and optimizing strategy parameters of the strategy network based on a PPO algorithm; the execution module is used for controlling the vehicle lamp according to the vehicle lamp adjustment strategy output by the strategy network. The intelligent level of the vehicle lamp is remarkably improved, and the system is widely applied to night driving assistance, urban interaction prompt and low-visibility driving scenes.

The invention belongs to the technical field of traffic optimization, and particularly relates to a traffic optimization method based on a three-layer Stackelberg game, and the method comprises the steps: obtaining mixed traffic data; according to the mixed traffic data, constructing a three-layer Stackelberg game model; an equilibrium solution of the three-layer game model is solved through a deep reinforcement learning algorithm, an SAC algorithm is introduced into an upper layer to generate a global constraint strategy, and a DDPG algorithm is adopted in a lower layer to optimize cooperative driving and safe obstacle avoidance driving behaviors of the intelligent vehicle; according to the global constraint strategy and the intelligent vehicle driving behavior optimization result, the traffic efficiency, safety and energy consumption and carbon emission indexes of the mixed traffic flow are optimized, the traffic state is continuously updated through behavior feedback data of a middle layer and a lower layer, and strategy optimization is carried out in combination with SAC and DDPG models. Therefore, the problems of insufficient dynamic adaptability, low efficiency of main body interaction processing, limitation of a game model and the like in the prior art are solved.

The invention relates to a vehicle queue cooperative control method fusing deep reinforcement learning communication time delay compensation, and belongs to the technical field of intelligent vehicle control. For the problems of control error accumulation and queue instability caused by communication time delay in the vehicle-road cloud cooperative system, a solution integrating deep reinforcement learning prediction and preset performance control is provided. According to the technical scheme, the method comprises the following steps: constructing a vehicle state prediction model based on a DDPG algorithm, and generating a time delay compensation amount in real time; establishing a vehicle dynamics model containing communication time delay, and designing a sliding mode controller based on a barrier function; and dynamically adjusting the control constraint in combination with an adaptive error boundary mechanism. According to the method, through closed-loop fusion of prediction compensation and dynamic constraint, under the scene of burst communication delay (100-400 ms), the queue spacing error is reduced by 62%, the speed tracking error is still kept to be smaller than 0.3 m / s, and the queue stability and the anti-jamming capability are remarkably improved.

The invention relates to the technical field of electric vehicle control, in particular to an AI-based electric vehicle intelligent starting and acceleration control method and system. The method comprises the following steps: acquiring a driving log of a vehicle owner, performing intelligent vehicle optimization after starting, and constructing an intelligent starting mode selection strategy; physiological state data of a driver are obtained, state change evolution analysis is carried out, and a personalized driving portrait is constructed; holographic state sensing and vehicle virtual simulation are carried out, and a vehicle synchronous digital simulation model is constructed; radar feedback sensing information and a real-time monitoring image in front of a vehicle are collected, traffic situation prediction and vehicle scene prediction and perception are carried out, and a vehicle scene prediction and perception model is constructed. According to the method, the driving starting mode of the vehicle is intelligently selected, vehicle owner requirements and environment available power constraints are matched, and vehicle acceleration efficiency maximization and safety optimization are achieved.

The invention relates to the field of logistics vehicle management, in particular to a centralized management method and system for intelligent vehicles. The invention discloses a centralized management system for smart vehicles. The centralized management system comprises a task receiving module, a portrait construction module, a vehicle screening module, a sign-in verification module and a queue management module. According to the method, the vehicle multi-dimensional portrait model is constructed, and the transportation cost, the transportation efficiency and the like are subjected to multi-dimensional matching calculation based on the multi-objective optimization algorithm, so that intelligent vehicle dispatching decision-making from experience driving to data driving is realized; according to the method, the historical operation data, the real-time state and the driver behavior characteristics of the vehicle are comprehensively considered, a global optimal vehicle dispatching scheme can be output, the vehicle dispatching accuracy and the resource utilization efficiency are remarkably improved, and the limitation that a traditional method only depends on a single factor for dispatching is overcome.

The invention relates to the field of artificial intelligence, and provides a distributed reasoning method, device and equipment based on a hybrid expert architecture, and a medium, and the distributed reasoning method based on the hybrid expert architecture applied to an edge computing power gateway comprises the steps: determining a plurality of target intelligent vehicles participating in a reasoning task if the reasoning task is received; obtaining an expert reasoning model, and deploying the expert reasoning model to each target intelligent vehicle; wherein the expert reasoning model comprises a plurality of expert reasoning modules, and at least one expert reasoning module is deployed in each target intelligent vehicle; sending the reasoning task to each target intelligent vehicle, so that each target intelligent vehicle executes the reasoning task based on the expert reasoning module to obtain a reasoning result; and aggregating the reasoning results of the target intelligent vehicles to obtain a target reasoning result. Through the technical scheme provided by the invention, the computing power resource of the idle intelligent vehicle is fully utilized.

The invention discloses an intelligent vehicle maintenance management system and method based on big data analysis, and relates to the technical field of intelligent vehicle maintenance, and the method comprises the following steps: collecting various kinds of vehicle driving data in real time in the vehicle driving process through a data collection device, and providing a reliable basis for subsequent analysis and decision making. Through real-time analysis of vehicle driving data and combination of a machine learning model, the system can dynamically adjust the detection frequency and the maintenance strategy, and it is ensured that maintenance better fits the actual use environment. For a rugged road surface, the system accurately recognizes a high-risk state according to the impact load, the vehicle body rolling acceleration and other characteristics, early warning is conducted in time, the maintenance frequency is optimized, excessive maintenance is avoided, meanwhile, the safety is improved, and the maintenance cost is reduced.

The invention relates to the technical field of vehicle lamp systems, in particular to an intelligent vehicle lamp system based on DLP projection, a control method and a vehicle. The intelligent vehicle lamp system based on DLP projection comprises an optical system, an intelligent control system, an execution module and a power management system, the system collects environment data in real time through a sensor, a central processing unit fuses multi-source information and generates a control instruction through an AI algorithm, and the light source brightness, the irradiation angle and the road surface projection content are dynamically adjusted; meanwhile, voice interaction and remote control are supported, and the self-adaptive lighting and safety warning functions are achieved. Through the intelligent control and DLP projection technology, the vehicle lamp performance is improved, the lighting effect is optimized, and the night driving safety is enhanced; self-adaptive adjustment is realized, and the driving experience is improved; the energy consumption is reduced by more than 30%; the battery life is prolonged; v2X communication is supported, and intelligent early warning is realized; a voice control function is provided, and operation is more convenient and safer; a road surface projection function is added, navigation arrows and warning marks can be displayed, and visual driving assistance is provided.

The invention discloses an intelligent vehicle cluster safe formation control method based on an observer, an intelligent vehicle cluster comprises a leader vehicle and follower vehicles, and a third-order longitudinal dynamics mathematical model is constructed for each follower vehicle and each leader vehicle. A sampling-based distributed observer is constructed based on a communication relationship among vehicles and a third-order longitudinal dynamics mathematical model, and an extended state observer is constructed based on state information of a follower vehicle and introduction of unmodeled disturbance; an intelligent vehicle cluster safe formation control strategy is constructed based on a distributed observer and an extended state observer, and a DoS attack model is constructed. According to the technical scheme, precise estimation of the state information of the leader vehicle by the follower vehicles and real-time precise estimation of each state component and unmodeled disturbance of each follower vehicle are achieved, it is guaranteed that each vehicle in the vehicle formation runs at a safe interval and at a constant vehicle speed, and the robustness of a vehicle system in a complex environment is improved.

Systems and methods for on-board selection of data logs for training a machine learning model. The methods include, by an autonomous vehicle, receiving sensor data logs corresponding to surroundings of the autonomous vehicle from a plurality of sensors, identifying one or more events within each sensor data log. The methods also include, for each sensor data log: analyzing features of the identified one or more events within that sensor data log for determining whether that sensor data log satisfies one or more usefulness criteria for training a machine learning model, and transmitting that sensor data log to a remote computing device for training the machine learning model if that sensor data log satisfies one or more usefulness criteria for training the machine learning model. The features can include spatial features, temporal features, bounding box inconsistencies, or map-based features.

The invention belongs to the field of intelligent vehicle-mounted technology, and particularly relates to a multifunctional vehicle-mounted sensing detection system based on multi-source information fusion, which comprises multi-source information acquisition, high-precision space-time synchronization and heterogeneous data preprocessing. A heterogeneous sensing system with feature level deep fusion, vehicle state dynamic performance evaluation and adaptive decision and risk evaluation capabilities is constructed, and a driving decision is dynamically adjusted through fusion perception and vehicle performance. The vehicle state and dynamic performance evaluation module is constructed by deeply integrating vehicle internal state data acquired by a vehicle-mounted diagnosis system into a perception fusion and decision planning process, and the core contradiction of disjunction of a perception result and a vehicle dynamic performance strategy is effectively solved. The system can evaluate key performance parameters such as power, braking, steering and the like of the vehicle and potential faults of the key performance parameters in real time, and the driving strategy and the safety margin are dynamically adjusted in the self-adaptive decision and risk evaluation module according to the actual physical limitation of the vehicle.

The invention relates to the technical field of intelligent early warning, in particular to an intelligent vehicle-mounted blind area early warning device based on multi-modal sensor fusion, which comprises a sensing array module, a driver sensing module, an environment recognition module, an early warning decision module and a linkage execution module, the sensing array module is used for being connected with a vehicle-mounted system to obtain vehicle-mounted data related to a driver and a driving environment. The driver sensing module is used for analyzing driving habit information of the driver according to the vehicle-mounted data. The environment identification module is used for analyzing environment identification information of the vehicle according to the vehicle-mounted data. The early warning decision module is used for generating a grading threshold value of blind area early warning monitoring according to the driving habit information and the environment identification information; a personalized early warning and monitoring model is established by using a deep learning algorithm, and dynamic risk levels of a driver in a driving process are divided. According to the method, the personalized driving habit model is generated through deep learning, invalid interference is reduced, and the blind area early warning efficiency is improved.

A lane-changing trajectory planning method, an autonomous driving method, and a related apparatus. The autonomous driving method comprises: on the basis of first motion state information of an intelligent driving device and second motion state information of an opponent, controlling the intelligent driving device to change to a target lane within a first lane-changing time period and to be located in front of the opponent, wherein the opponent is located on the target lane for the intelligent driving device, and is located between a first position and a second position, wherein the first position is located behind the intelligent driving device and longitudinally distant from the intelligent driving device by a first distance, and the second position is located in front of the intelligent driving device and longitudinally distant from the intelligent driving device by a second distance. The technical solution of the present application can be applied to intelligent vehicles such as a new energy vehicle and an electric vehicle, and facilitates improvement of lane-changing success rates and traffic efficiency when the vehicles are in an autonomous driving mode in complex lane-changing scenarios such as merging into congested roads and merging into ramps.

The invention relates to an intelligent car insurance quotation system based on multi-modal data processing. The intelligent car insurance quotation system comprises a multi-source data acquisition module, a risk domain construction module and a dynamic quotation module, the multi-source data acquisition module analyzes the driving behavior characteristics, the vehicle condition characteristics and the driving environment characteristics of the target vehicle based on the acquired image data, the structured text data and the sensor time sequence data; the risk domain construction module constructs a driving behavior risk curve of the target vehicle based on the driving behavior characteristics, constructs a vehicle condition risk curve of the target vehicle based on the vehicle condition characteristics, and then constructs a driving environment risk curve of the target vehicle based on the driving environment characteristics. And finally, constructing a dynamic quotation domain of the target vehicle based on the driving behavior risk curve, the vehicle condition risk curve and the driving environment risk curve. And the dynamic quotation module performs quotation analysis on the dynamic quotation domain to obtain the vehicle insurance quotation of the target vehicle.

The invention belongs to a man-machine cooperative control technology in the field of intelligent driving, and particularly discloses a lane departure early warning and correction method based on driver behavior prediction. And in combination with vehicle state parameters (such as vehicle speed, yaw velocity, lane departure distance and the like), a Transformer-CNN hybrid model is adopted to predict the steering intention of the driver, so that accurate behavior judgment is realized. Multi-source data are fused through a graph neural network (GNN), the lane departure risk is dynamically evaluated, integration with an electric power steering system (EPS) is achieved, damping and torque of a steering wheel are adjusted in real time, and a vehicle is actively corrected to a safe lane. Meanwhile, the system provides multi-mode early warning such as sound, vibration and head-up display (HUD) according to the risk level, and it is ensured that a driver responds in time.

The invention relates to a dynamic path planning method in a PFC-DWA complex environment based on an improved DWA algorithm, and belongs to the technical field of intelligent driving. The method is a window reduction algorithm for dynamic prediction in an environment with a large scene change rate. According to the algorithm, the kinematics constraint of the Ackerman steering trolley is considered, a dynamic obstacle collision evaluation window reduction strategy according to the dynamic obstacle and intelligent vehicle motion state information is provided, the dynamic window range is optimized through the motion state information and a kinematics constraint equation by using an analytic geometry method, and the dynamic obstacle avoidance capability of the intelligent vehicle is improved. In order to solve the problem that the evaluation function of the DWA algorithm lacks evaluation of the obstacle state change trend, the proposed algorithm predicts the future position information of the obstacle based on the least square method according to the historical position information of the obstacle, the future position information is fused into the evaluation function of the algorithm, and the evaluation performance of the algorithm on the simulated trajectory in the dynamic environment is improved. The problem of low arrival rate of a path planned by a conventional algorithm in a frequent scene change environment is solved.

The invention discloses an intelligent vehicle transverse and longitudinal cooperative personalized control method based on reinforcement learning, and the method comprises the steps: 1, obtaining target path information and vehicle state information, and constructing an intelligent body state space; 2, vehicle control parameters are obtained, and an intelligent agent action space is constructed; 3, constructing a vehicle dynamics safety boundary according to the vehicle dynamics model; 4, according to different driving style types, designing differentiated reward functions in combination with path tracking targets and dynamic safety boundary constraints; and 5, constructing a strategy network and a value network, and training the strategy network and the value network based on the reward functions of the three driving styles to obtain an optimal control model. According to the method, the transverse and longitudinal cooperative control model considering the dynamic factors and the driving styles is trained by using the reinforcement learning method, personalized control requirements are met, and meanwhile, the control precision and stability of the intelligent vehicle are improved.

The invention relates to the technical field of video monitoring, and provides an ectopic dual-camera intelligent vehicle speed measurement method and system based on deep optimization. The method comprises the following steps: acquiring images acquired by two cameras which are arranged at different positions on the same side of a road; extracting features of each vehicle based on each vehicle detected by the first image and the second image, and matching the features of the vehicles in the first image and the second image; when a target vehicle reaches a speed measurement point area, determining a three-dimensional geometric projection constraint residual error according to depth estimation values of the two cameras and three-dimensional geometric projection constraints of the target vehicle and the two cameras, constructing an error-minimized target function in combination with a depth consistency error, and optimizing the depth estimation values of the two cameras to obtain a target vehicle speed measurement result; obtaining depth estimation optimization values of the first camera and the second camera; and converting the two-dimensional track pixel coordinates of the target vehicle into three-dimensional world coordinates based on the depth estimation optimization values of the first camera and the second camera, and calculating the final speed of the target vehicle.

The invention relates to an in-vehicle environment control method and device, an intelligent vehicle, a readable storage medium and a program product. The method comprises the following steps: acquiring internal and external multi-modal environment data of a vehicle; according to the multi-mode environment data inside and outside the vehicle, the passenger comfortable temperature of the vehicle is predicted; the passenger comfort temperature is used for representing the comfort requirement of passengers in the vehicle for the temperature in the vehicle; the current in-vehicle temperature of the vehicle is obtained, and an in-vehicle environment adjusting part of the vehicle is controlled to execute a corresponding in-vehicle environment adjusting action according to a comparison result between the current in-vehicle temperature and the passenger comfort temperature, so that the in-vehicle temperature of the vehicle is matched with the passenger comfort temperature; the in-vehicle environment adjusting part comprises a component capable of adjusting the in-vehicle temperature of the vehicle through the natural environment. By adopting the method, the intelligent degree of the intelligent vehicle in the aspect of in-vehicle environment control can be improved.

The invention provides a warning method and system of an intelligent tripod warning lamp, and relates to the technical field of intelligent vehicle-mounted technologies. When a vehicle is in danger, vehicle state information is collected to automatically generate a warning trigger instruction, and a rear target projection point is determined based on a road type and a vehicle position; projection parameters are calculated in combination with vehicle postures, a vehicle-mounted projection light source is driven to generate a triangular warning graph on the ground at the target distance, meanwhile, light beam output is adjusted in a self-adaptive mode according to environment illumination and visibility, and it is guaranteed that the graph is clear and visible and conforms to human eye safety; the projection position and parameters are dynamically updated to maintain stable warning during the duration of the dangerous state, and the system is automatically closed and reset when the danger is relieved or manual termination is performed, so that the risk of manual arrangement is avoided, and long-distance stable warning is realized.

The invention belongs to the technical field of automatic driving testing, and particularly relates to an intelligent driving method and device for a driver with disabled lower limbs. The system comprises a lower limb disabled driver operation module, an automatic driving module, a man-machine fusion module and a control execution module. According to the method, the position and speed change information of a steering wheel can be converted into digital signals through the steering wheel moving up and down and a steering wheel position sensor, the driving intention of acceleration and deceleration is obtained, an automatic driving longitudinal decision algorithm based on deep learning is developed, effective man-machine fusion is achieved through driving right distribution, and the driving efficiency is improved. And the control of the intelligent vehicle is completed.

The invention discloses an intelligent vehicle fusion perception and decision-making method and system based on low-orbit satellite communication and high-precision positioning, and the method comprises the steps: firstly, collecting the perception data of an intelligent vehicle through the low-orbit satellite communication and high-precision positioning technology, and achieving the time-space consistency processing through a cloud end, the method includes delay compensation, semantic information matching and multi-observation joint state estimation, improves complex scene perception precision, and reduces dangerous decision-making behaviors and high takeover rate caused by perception defects of an intelligent automobile. Secondly, a behavior guiding strategy is constructed based on track data of a large number of vehicle groups, and a macroscopic decision basis is provided for the intelligent vehicle groups; and through a multi-agent reinforcement learning algorithm, the intelligent vehicle group can carry out self-vehicle optimal decision making, so that efficient traffic flow management and group collaborative optimization are realized. A brand new technical scheme is provided for dynamic traffic flow management in a complex traffic scene, traffic safety and efficiency are improved, and important support is provided for application of an intelligent traffic system and automatic driving.

The invention provides a transmission device and an actuating system. The transmission device comprises two transmission components and a coupling component arranged between the two transmission components. One of the two transmission components is in transmission connection with the force generating mechanism and is used for receiving the torque output by the force generating mechanism; and the other one is in transmission connection with the force output torsion bar and is used for outputting torque to the force output torsion bar. The coupling component is arranged between the two transmission components and used for achieving force transmission between the two transmission components. The active suspension can be applied to intelligent vehicles or electric vehicles, and the frequency response performance of the active suspension can be improved.

The invention discloses a traffic control model training method and system based on a block chain and federated learning, and the method comprises the steps: enabling each target intelligent vehicle to train a local traffic control model according to the data of a corresponding to-be-trained vehicle-mounted sensor, and obtaining a local model training result, each local model training result is sent to the block chain network; evaluating each local model training result through an intelligent contract in the block chain network to determine a qualified model training result; all the qualified model training results are aggregated through the block chain network to obtain a global model, the global model is distributed to all the target intelligent vehicles, and all the target intelligent vehicles update local traffic control models according to the global model; and the local traffic control model is used for realizing intelligent management and control of traffic scenes. The method can avoid data privacy leakage and single-point fault risk, improves the model training quality and training efficiency, improves the safety and reliability of an Internet of Vehicles system, and can be widely applied to the technical field of computers.

The invention discloses an intelligent voice interaction system and method based on multi-mode perception, and belongs to the technical field of man-machine interaction. The system comprises a voice acquisition and preprocessing module, a multi-mode perception module, a voice recognition, voice understanding and decision module and a multi-mode feedback module. Wherein the multi-mode sensing module fuses voice, vision and sensor data through a space-time alignment algorithm to realize comprehensive sensing of user intentions. According to the invention, through integration of information of a plurality of perception channels, the recognition accuracy and the understanding ability of the voice interaction system in a complex environment are improved, and the problem that traditional single voice interaction is difficult to recognize in a noise environment, a multi-person scene and a complex context is solved; and more natural and accurate man-machine interaction experience can be provided for the fields of intelligent home, intelligent vehicle-mounted, intelligent customer service and the like.

The invention relates to an intelligent vehicle taillight system, a control method thereof and a vehicle, the intelligent vehicle taillight system comprises a data acquisition module used for acquiring vehicle dynamic operation parameters and external environment information, the vehicle dynamic operation parameters comprise at least one of brake pedal stroke, steering angle, vehicle speed and automatic driving state data, and the external environment information is used for acquiring the external environment information; the external environment information comprises at least one of a rear vehicle distance and a pedestrian position; the intelligent control module is connected with the data acquisition module and is used for generating current working condition information of the vehicle based on the vehicle dynamic operation parameters and the external environment information; and the tail lamp is connected with the intelligent control module and performs corresponding display and projection operation according to the current working condition information. According to the invention, the driving safety can be improved.

The invention relates to an optimal cooperative control method for preset performance of multiple intelligent vehicles under a non-convex input constraint, and belongs to the technical field of multi-agent cooperative control. The problems that non-convex input constraint processing is conservative, preset performance and optimal control are designed separately, and the timeliness of a traditional method is insufficient are solved. Designing a non-convex constraint operator to directly map, control and input to a feasible direction extreme value; constructing a distributed preset time observer to realize accurate estimation of the state of the leader; preset performance control is converted into an optimal control problem in combination with a time-varying performance function and error conversion; and solving an HJB equation based on a reinforcement learning iterative algorithm to approach an optimal control law. The conservative property of a traditional convex approximation method is broken through, and it is ensured that control input strictly meets the non-convex constraint; eliminating time uncertainty through preset time observation; fusing preset performance and optimal control to realize multi-target collaborative optimization; the reinforcement learning algorithm has global convergence and real-time performance, and is suitable for multi-vehicle cooperative control in a complex dynamic environment.

Presented are advanced control systems providing tire change modes for vehicles with adjustable ride-height suspension systems, methods for making / using such systems, and vehicles equipped with such systems. A method of operating a vehicle includes a controller receiving an input signal indicative of a tire change event for the vehicle, and determining a location of a tire associated with the tire change event. In response to receiving the input signal, the controller determines if a real-time slope of the vehicle is greater than a preset maximum secure slope; if not, the controller prompts a user to prepare the vehicle for the tire change event. After prompting the user to prep the vehicle, the controller commands the vehicle's ride height suspension system to lower the ride height of the tire's corner module to a predefined lowered height and raise the ride height of all other corner module to a predefined raised height.