1233 results about "Safe driving" patented technology

The invention discloses a fatigue driving monitoring and early warning method based on multi-dimensional feature fusion, and relates to the field of intelligent traffic and safe driving, and the method comprises the steps: carrying out the feature extraction of a collected face image of a vehicle driver through a feature extraction model based on a convolutional neural network, and obtaining a face key point; then calculating the eye closing rate, the mouth opening rate, the head posture data and the facial muscle data to finally obtain multi-dimensional features, and fusing the multi-dimensional features by using a multi-layer perceptron to obtain fused facial features; and then, a fatigue state detection model obtained based on recurrent neural network training is utilized to detect the facial feature time sequence at a plurality of continuous sampling moments so as to detect whether a fatigue driving state occurs and trigger alarm feedback. According to the method, the fatigue state of the driver can be identified and judged more accurately through combination of multi-dimensional data fusion and a time sequence analysis algorithm, and the monitoring and early warning accuracy and reliability are improved. And the system can keep high robustness even in an illumination change or complex driving environment.

The invention provides a vehicle safety control method and device, electronic equipment, a vehicle and a storage medium. An electronic image of an intelligent driving perception picture is acquired in real time; wherein the electronic image is used for displaying the identification information of the vehicle and the identification information of the surrounding environment; receiving a current instruction input by a user for the identification information of the vehicle and the identification information of the surrounding environment in real time; according to at least one layer of security judgment mechanism, if it is determined that the current instruction is unreasonable, receiving a next instruction adjacent to the current instruction, and covering the current instruction with the next instruction; the safety judgment mechanism is used for enabling the vehicle to drive safely after judging that the current instruction is reasonable; and if it is determined that the current instruction is reasonable, the vehicle is allowed to be controlled to drive according to the current instruction.

The present invention relates to the field of dynamic monitoring technology, and more specifically, to a dynamic monitoring system for safe driving behavior and safe production of mobile machinery on-site based on an AI large model. It includes a multimodal data acquisition unit, which collects real-time video data, motion data, control instruction data, and obstacle data around the vehicle; a multimodal data fusion unit is used to achieve time domain alignment of video data and motion data through a dynamic time offset compensation model and an interpolation algorithm, and then establish a causal association model between control instruction data and motion data based on a structural equation model, and reversely correct the time domain alignment parameters; a safety monitoring unit detects abnormal driving behavior in real time, and triggers an early warning mechanism when an abnormality is detected. The system can accurately adjust the alignment parameters of the time domain alignment module, while improving the sensitivity and accuracy of abnormal driving behavior detection, and can also improve the adaptability of the safety monitoring system to dynamic environments.

The invention relates to the technical field of risk assessment, in particular to a safe driving assessment method and system based on Beidou navigation. The method comprises the following steps: acquiring corresponding real-time position, speed and driving track data in a vehicle driving process through a Beidou navigation module, and acquiring vehicle state data and environment sensing data to obtain a vehicle multi-dimensional driving state data set; performing vehicle space-time and driving operation feature analysis and driving road matching analysis on the vehicle multi-dimensional driving state data set to obtain vehicle driving road attribute matching data; and carrying out vehicle driving risk estimation and vehicle safety over-rate evaluation based on the vehicle driving road attribute matching data, carrying out safety comparison verification evaluation, generating vehicle personalized driving improvement suggestions, and pushing the vehicle personalized driving improvement suggestions to the vehicle-mounted terminal to execute corresponding vehicle safe driving behavior improvement operation. According to the invention, real-time monitoring, accurate evaluation and active safety intervention of vehicle driving behaviors can be realized.

The invention discloses a vehicle driving track prediction method based on a large language model, and belongs to the technical field of automatic driving. The invention aims to provide a vehicle driving track prediction method based on a large language model, which is used for inputting a vehicle track into the large language model in a text format and evaluating the capability of the large language model in understanding the time dynamic aspect of a traffic agent. According to the method, tracks of different vehicles in a traffic scene are encoded by using a track encoder, track encoding features are mapped into a text embedding space of a large language model by using a reprogramming adapter, and the encoding features are translated into lexical combination which can be understood by the large language model. According to the method, powerful support is provided for realizing reliable and safe driving decisions by the large language model.

The invention discloses a new energy automobile safe driving detection method and system, and relates to the technical field of safe driving detection, and the method comprises the steps: collecting vehicle state data, and carrying out the preprocessing of the vehicle state data; constructing a health state evaluation model, and predicting a vehicle health state score; dynamically adjusting scene detection parameters according to the vehicle health state score; environment data are collected in real time through the adjusted scene detection parameters, and a scene risk score is evaluated based on the environment data; and according to the vehicle health state score and the scene risk score, generating an optimal driving strategy, and executing an active safety linkage mechanism. According to the invention, the health state evaluation model is constructed, the multi-dimensional state data of the vehicle is comprehensively analyzed, and the health state of the vehicle is accurately quantified; the environment detection parameters are dynamically adjusted according to the health state score, the detection sensitivity and frequency are improved when the health state is poor, and the adaptability to complex road conditions and severe weather is enhanced.

The invention discloses an intelligent emergency control system of an arrangement ship steering oar, and belongs to the technical field of arrangement ship steering oar control. An intelligent emergency control system of an arrangement ship steering oar comprises a main control module, a steering oar driving module and a steering oar control module. The problem that in the prior art, when a main steering system breaks down, a ship is prone to being out of control, colliding or other safety accidents are caused is solved, the steering oar driving module is used for conducting driving operation on the arrangement ship steering oar in an emergency mode, the state of the arrangement ship steering oar is monitored in real time, and the safety of the arrangement ship steering oar is improved. When potential problems and fault trends exist in the arrangement ship steering oar, a control instruction of the arrangement ship steering oar is adjusted so as to ensure safe operation of the arrangement ship steering oar, quick response can be made and an emergency mode can be started in emergency, dangerous situations such as out-of-control of the ship can be effectively avoided, remote monitoring and management can be achieved, and the safety of the arrangement ship steering oar is improved. Safety and management efficiency of the ship are improved, and safe driving of the ship is effectively guaranteed.

The invention relates to the technical field of intelligent networked vehicles, in particular to an intelligent networked vehicle remote control and monitoring method. The method comprises the following steps: acquiring real-time running state information and roadside sensing data of a vehicle, and generating a remote takeover request; receiving a control instruction sent by the cloud control platform, and feeding back a control execution result; calculating instruction interaction delay and network transmission delay; the stability of the communication environment is evaluated by combining the two, and the vehicle driving mode is dynamically adjusted. Furthermore, time delay is calculated according to heartbeat signals and control instruction timestamps in the communication link, the stability index of the communication environment is evaluated, and the safe driving speed is adjusted according to the stability. And when the communication environment stability is lower than the threshold value or the instruction interaction delay exceeds the threshold value, an emergency stop mechanism is triggered. According to the method, the safety and the stability of the intelligent network connection vehicle under remote control can be improved.

The invention discloses a vehicle tire burst control method and system, a vehicle, control equipment and a storage medium, and relates to the technical field of vehicle safety control. The method mainly comprises the steps of determining the position of a tire burst wheel according to a vehicle tire burst signal, and identifying a driving scene according to vehicle state information; if the linear driving scene is recognized, acquiring tire braking force pressure according to the position of the flat tire wheel, calculating a yaw torque compensation value according to the tire braking force pressure, performing rear wheel steering adjustment, transferring a driving torque based on the position of the flat tire wheel, and performing vehicle body posture adjustment based on the position of the flat tire wheel; if the scene is recognized as the curve scene, controlling and changing the steering angle of the rear wheel based on the position of the flat tire wheel so as to adjust the yawing torque, transferring the driving torque based on the position of the flat tire wheel, and adjusting the posture of the vehicle body based on the position of the flat tire wheel. According to the vehicle tire burst control scheme, safer driving experience can be provided for the user, and the vehicle using experience and satisfaction degree of the user are improved.

A driving analysis server may be configured to receive vehicle operation data from vehicle sensors and telematics devices of a first vehicle, and may use the data to identify a potentially high-risk or unsafe driving behavior by the first vehicle. The driving analysis server also may retrieve corresponding vehicle operation data from one or more other vehicles, and may compare the potentially high-risk or unsafe driving behavior of the first vehicle to corresponding driving behaviors in the other vehicles. A driver score for the first vehicle may be calculated or adjusted based on the comparison of the driving behavior in the first vehicle to the corresponding driving behaviors in the other vehicles.

The invention relates to the technical field of intelligent traffic, in particular to an integrated fog measurement type highway safe driving guidance device and method, and provides the following scheme that local visibility data is acquired based on a plurality of highway safe driving guidance devices and uploaded to a region controller to generate guidance information. And the regional controller performs research and judgment calibration and gradient calibration on the data, corrects measurement errors and generates road visibility space distribution information by screening calibration anchor points and adopting an exponential attenuation function and iterative gradient calculation. Based on the information, a connectivity algorithm is used to identify a fog area, and in combination with road environment and historical accident data, an optimal induction strategy is matched, and induction information is dynamically adjusted. The fog area detection accuracy is improved, the induction information matching is optimized, and the driving safety and the passing efficiency under the low-visibility condition are improved.

The invention provides a control method, device and equipment, a vehicle, a chip and a medium, relates to the artificial intelligence technology and can be applied to the field of vehicles, and the method comprises the steps that scene information of a scene where a movable target is located and input data of a planning track are determined; motion risk prediction of the movable target is carried out according to the input data, and a risk prediction result is determined; the risk prediction result is used for indicating whether the movable target has a risk when moving according to the planned trajectory; and according to the risk prediction result, determining whether to update the planning trajectory of the movable target. Therefore, a motion risk prediction technology is introduced into the trajectory planning scene of the movable target, potential motion risks are predicted, dangerous points or collision risks possibly existing in the planning trajectory are recognized in advance, and early warning is provided for safe driving. Once the potential motion risk is detected, the planning trajectory can be adjusted, so that the efficient and safe trajectory can be planned, and the possibility of risks such as collision of the movable target in actual driving can be reduced.

The invention discloses an unmanned vehicle azimuth end-based object autonomous detection obstacle avoidance intelligent identification system, which comprises the following modules: a sensing data acquisition module, which is used for acquiring and preprocessing multi-source sensing data and generating an environment sensing data set; the feature extraction module is used for inputting the environment perception data set into the Motion-DETR dynamic perception model to generate a target object feature set; the perception graph construction module is used for constructing a dynamic environment perception graph; the threat evaluation module is used for calculating threat levels, generating an azimuth end threat level matrix and establishing a dynamic risk distribution diagram; the path extension optimization module is used for dynamically selecting an extension direction by adopting a path extension optimization method introducing dynamic risk cost; the path cost optimization module is used for dynamically optimizing the expansion structure according to the principle of minimum accumulated path cost value; and the path output module is used for generating an optimal safe driving path in the dynamic environment. The invention provides an intelligent obstacle avoidance path planning method fusing perception mapping and risk optimization.

The invention discloses an intelligent electric vehicle driving state safety estimation method under network attacks, and relates to automobile intelligent safety and automatic driving. Firstly, a three-degree-of-freedom dynamic model of the intelligent electric vehicle is established, the model comprehensively considers transverse, longitudinal and yaw dynamic characteristics of the vehicle, and accurate description is provided for subsequent state estimation. And designing an attack signal and state security estimation method based on an adaptive tolerance extended Kalman filtering algorithm. According to the method, the real-time and accurate estimation of the vehicle state is realized by fusing the information of various sensors such as GPS, LiDAR, IMU and the like, and malicious data introduced due to network attack can be effectively eliminated. Meanwhile, the method also has the capability of tolerating non-ideal network environments such as sensor data packet loss and the like, and the safety and reliability of the self-driving automobile are further enhanced. The intelligent electric vehicle can still maintain accurate driving state estimation when subjected to network attack, and safe driving of the vehicle is ensured.

The invention relates to the technical field of automobiles, in particular to a control system and method for self-adaptively adjusting the position of an outside rear-view mirror based on a DMS. The control system comprises a static judgment module, a dynamic judgment module, a DMS main module, a seat adjusting module, a memory module, an information acquisition module, a data analysis module, a rearview mirror adjusting module and a feedback optimization module. The information acquisition module and the data analysis module work cooperatively, so that the system can calculate the optimal view angle and position according to real-time road and vehicle driving information, and finally, the feedback optimization module continuously optimizes the system performance by collecting the adjustment effect and the driver satisfaction degree, so that the system performance is improved. According to the method, a closed-loop continuous improvement process is formed, and the method not only improves the efficiency and accuracy of adjustment, but also ensures that the system can continuously adapt and learn along with the passage of time so as to provide more personalized and safer driving experience.

The invention relates to the technical field of driving control, in particular to a new energy automobile safe driving control method and system. The method comprises the following steps that a historical urgent danger avoiding data set and a corresponding user misoperation braking state are obtained through a new energy automobile control center, sample undersampling processing is conducted firstly, and an urgent danger avoiding balance sample is obtained; thirdly, analyzing user misoperation braking abnormity by using a balance sample, and performing disordered vector intensity calculation under a time dimension based on braking abnormity data to generate disordered vector segmentation clustering data; then, braking time sequence intervention correction sensing is carried out according to the clustering data, optimized braking intervention correction data are generated, finally, the optimized data are transmitted to a new energy automobile control terminal, and safe driving control is executed. According to the invention, the driving control technology is optimized, so that the driving control technology is more perfect.

The invention relates to the technical field of parking lot digital management, and discloses a digital twin enabling parking lot digital global management system. According to the system, a global digital twin model of a parking lot is generated, and multi-source sensing data such as parking space states, vehicle identities and environmental parameters are fused; generating a parking space distribution matrix with priorities and time windows in combination with the parking request of the user and the real-time parking space state; according to the parking space two-dimensional code hardware parameters, the digital twin model and the parking space distribution matrix, outputting a vehicle guiding path set with a timestamp; a real-time obstacle avoidance and dynamic adjustment system is constructed to generate a safe driving path set so as to dispatch vehicles; outputting a power distribution control instruction set and updating the digital twinborn model by combining the electric quantity of the energy storage unit, the power distribution network data and the safe driving path set; and finally, system knowledge evolution and end-to-end closed-loop optimization are realized through scheduling execution logs. According to the system, global efficient management of the parking lot is realized, and the parking space utilization, vehicle scheduling and energy utilization levels are improved.

Systems and methods are provided for operation of a vehicle to prevent the vehicle from entering unsafe driving states. In examples, a current operational state of a vehicle is determined based on sensor data collected by sensors on the vehicle. Based on the current operational state, a safety-critical threshold value for a vehicle dynamic parameter at which the vehicle is predicted to breach a limit-of-handling is determined. A command to operate the vehicle in a requested operational state is received, which includes driving inputs for operating the vehicle in the requested operational state. The command is filtered by constraining the requested operational state according to a control barrier function based on the safety-critical threshold value and a requested value of the vehicle dynamic parameter determined from the requested operational state. A control signal is generated that controls the vehicle by modifying the driving inputs based on the filtered command.

The embodiment of the invention relates to the technical field of vehicles, and discloses a vehicle accident early warning method and device, electronic equipment and a storage medium, the method comprises the following steps: obtaining running state data in a vehicle based on a preset duration, the running state data being determined based on vehicle data obtained by at least one bus source in the vehicle; determining target time sequence characteristic data in the operation state data, wherein the target time sequence characteristic data indicates a change trend, a periodic rule and a sudden change condition of the operation state data in a time dimension and a dependency relationship among the data; and inputting the target time sequence characteristic data into a risk assessment model to obtain an assessment result of the accident risk of the vehicle after the preset duration. By applying the technical scheme of the invention, more accurate vehicle early warning can be realized, and the safety driving requirement of the vehicle can be better met.

The invention discloses an anti-loosening monitoring hub, which belongs to the field of vehicle hubs, is used for hubs, and comprises a radial plate with a radial plate mounting hole, a spoke and a rim, the radial plate is provided with a monitoring sleeve and a monitoring seat cover plate, the position, corresponding to the monitoring sleeve, of the radial plate is provided with an ejector rod which moves relatively, and the ejector rod drives a transmission rod to move. A spiral transmission groove is machined in the circumferential side face of the transmission rod and drives the rotating plate to rotate. The rotating plate is rotationally connected with the monitoring seat cover plate through the rotating plate connecting plate; the rotating plate drives a sensor sliding rod through a plurality of rotating plate driving holes distributed in the rotating plate, the sensor sliding rod is located in a loosening sensor, and the loosening sensor is installed on a monitoring sleeve. By improving the hub structure, the looseness condition of the bolts in the radial plate mounting holes can be monitored after the hub is mounted, hub falling caused by looseness of the bolts is avoided, and safe driving of a vehicle is guaranteed.

The invention provides a heterogeneous motorcade prediction control method based on a path information protection mechanism, and relates to the technical field of network connection unmanned vehicle cooperative control and intelligent traffic. Firstly, a path information protection mechanism is provided for preventing an alien invading vehicle from tracking a motorcade and ensuring the safety of motorcade path information. In addition, a predictor model is established to improve the estimation performance of the adaptive law, and a variable gain nonlinear expansion state observer is designed to approach and compensate the total disturbance. Then, by introducing a variable-gain hyperbolic tangent tracking differentiator, the problem of'complexity explosion 'caused by repeated derivation of a virtual controller in a backstepping method is avoided; and finally, a distributed prediction cooperative controller is constructed, and stable and safe driving of the whole heterogeneous network connection fleet is ensured.

The invention relates to the technical field of vehicles, and discloses a method and device for dealing with abnormal disconnection of a trailer, electronic equipment and a vehicle. The method comprises the steps that based on a preset judgment strategy, the connection state of the trailer and a tractor is monitored and judged through a multi-mode sensor; when it is judged that the connection state is abnormal unhooking, a trailer unhooking signal is generated and sent to a vehicle-mounted bus to trigger a corresponding safety mechanism, and the safety mechanism comprises an acousto-optic self-starting mechanism and a self-adaptive driving control mechanism; the self-adaptive driving control mechanism is used for executing a corresponding safe driving strategy through an intelligent driving function in combination with current environment information; and when an instruction that the driver takes over the control right of the tractor is received, the control right of the tractor is returned to the driver. According to the invention, the monitoring function of the multi-mode sensor is combined with the intelligent driving function, so that the potential safety hazard caused by abnormal disconnection of the trailer is effectively solved, and the driving safety and reliability in the transportation process are obviously improved.

The invention provides a vehicle automatic driving path planning method, a path planning system and a vehicle, and relates to the technical field of intelligent driving. The method comprises the following steps: acquiring environmental parameters of a vehicle; the environment parameters comprise road environment parameters and road friction parameters; determining a safe driving area of the vehicle at least according to the road environment parameters, and determining an environment error coefficient of the vehicle at least according to the road friction parameters; and based on the environmental error coefficient, the steering angle variation of the vehicle in the path planning algorithm is constrained, the constrained path planning algorithm is utilized to generate an initial path of the safe driving area, and the vehicle is controlled to drive according to the initial path. According to the method, the environment parameters of the vehicle are obtained, the safe driving area and the vehicle steering angle constraint of the vehicle are determined according to the environment parameters, the initial path in the safe driving area is generated on the basis of the vehicle steering angle constraint, the vehicle is controlled to drive according to the initial path, and the safety of automatic driving of the vehicle is improved.

Techniques are disclosed to increase the safety of vehicles travelling in a vehicle platoon. These techniques include the utilization of a comprehensive safety framework such as a safety driving model (SDM) for the platoon control systems. In contrast to the conventional approaches, the use of the SDM model allows for platoon vehicle control systems to consider the acceleration / deceleration capabilities of the vehicles to calculate minimum safe longitudinal distances between the platoon vehicles. The disclosed techniques may utilize the periodicity of platoon messages as well as other parameters to improve upon platoon vehicle control and safety.

The invention discloses a bridge floor global driving wind environment early warning method, system and device based on limited measuring points and a storage medium, and relates to the technical field of wind speed early warning and artificial intelligence. Future short-time wind speed is predicted based on wind speed time history data, and a two-dimensional high-temporal-spatial-resolution prediction flow field of multiple planes is obtained according to the future short-time wind speed; main beam global wind-induced vibration response data are obtained based on the wind-induced vibration response data; obtaining three-dimensional high-temporal-spatial-resolution prediction flow field data according to the two-dimensional high-temporal-spatial-resolution prediction flow field and the girder global wind vibration response data; analyzing the three-dimensional high-temporal-spatial-resolution prediction flow field data to obtain a safe driving critical wind speed, and setting a wind speed-vehicle speed limiting criterion; and when the predicted wind environment exceeds the wind speed limit criterion, outputting the predicted data and the early warning level of the corresponding driving wind environment, and carrying out advanced early warning. According to the invention, the problem of low accuracy of existing driving safety early warning is solved, and wind speed multi-stage early warning for different vehicles in different driving environments is realized.

Provided in the present application are a train section occupancy detection method and a related apparatus, which are applied to a ground control server. The method comprises: receiving an identifier set from target wireless access points; on the basis of determination that first identification information and second identification information pass consistency voting, adopting a target logical section corresponding to the first identification information and the second identification information, thus determining the section, which is occupied by a train indicated by third identification information, as the target logical section. Therefore, the accuracy of train occupancy detection is improved, safe driving requirements during train operation is guaranteed, and the stability of train occupancy detection is also improved.

The invention discloses a safe driving map construction method based on binocular vision and IMU information fusion, and the method comprises the following steps: obtaining image data information through a binocular image collection unit, obtaining IMU data information through an inertial measurement unit, and carrying out the data preprocessing of the image data information and the IMU data information; performing joint initialization processing on a data preprocessing result based on binocular vision initialization and inertial measurement unit initialization; carrying out global pose nonlinear optimization processing on a joint initialization processing result based on a tight coupling optimization strategy of a sliding window; based on a BVAI-Fusion algorithm, generating a safe driving track map for a global pose nonlinear optimization processing result; initial pose estimation is improved through binocular vision and IMU multi-source heterogeneous information fusion, so that pose estimation precision and stereoscopic vision matching under multi-source heterogeneous fusion are improved, SLAM environment positioning accuracy is enhanced, and safety of driving map construction is guaranteed.

The invention discloses a hierarchical game-based energy and heat collaborative multi-objective optimization method for a pure electric vehicle. The method comprises the following steps: S1, establishing a coupling system and a collaborative optimization system # imgabs0 #; s2, acquiring the temperature and the current working condition; s3, establishing a hierarchical game system, and determining a game equilibrium solution; and S4, performing iterative adjustment. By constructing a collaborative optimization system and a coupling system, deeply fusing dynamic association between energy management and thermal management systems, and establishing a hierarchical game model, objective functions are established for the collaborative optimization system, the energy management system, the thermal management system and each subsystem of the thermal management system, mutual association is analyzed, the energy utilization efficiency is improved, and the energy utilization efficiency is improved. Energy distribution is more accurate and efficient; through layered game dynamic adjustment, multi-dimensional balance of overall energy consumption of the whole vehicle, battery safety and comfort of a passenger compartment is achieved, the limitation of single system optimization is broken through, and the comprehensive performance requirements for efficient operation, safe driving and comfortable driving of the vehicle under different environments and complex working conditions are met.

The invention provides a ramp energy-saving control method based on off-line speed planning and dual-mode model prediction tracking, and aims to solve the problems of insufficient precision of ramp driving energy consumption and safety cooperative control and frequent mode switching of an automatic driving vehicle. The method comprises the following steps: firstly, identifying a key road section based on high-precision road gradient data, and generating an optimal energy-saving reference speed matched with gradient characteristics in an off-line manner by adopting a dynamic planning and sequential quadratic planning hybrid optimization strategy; secondly, when the vehicle runs, relevant information is loaded after confirmation of a driver, the system takes over the longitudinal control right, two control modes are dynamically switched based on the front vehicle distance, and frequent mode switching is avoided through a hysteresis threshold value; and finally, taking the slope-fused vehicle longitudinal dynamics model as a prediction model, solving an optimal control instruction online by adopting a model prediction control algorithm, performing closed-loop execution, monitoring intervention operation of a driver, and immediately returning the driving right after the intervention operation is detected. According to the method, offline planning and online dual-mode tracking technologies are integrated, the method has the advantages of high energy consumption optimization precision, stable mode switching and good safety adaptability, and energy-saving and safe driving of the automatic driving vehicle in a ramp scene is realized.

A battery management system-based vehicle control unit wakeup method and apparatus, relating to the technical field of new energy vehicles. The method comprises: when an onboard boost pressure sensor wakes up a microcontroller unit within a fixed time on the basis of current abnormal pressure data of a battery pack, using the microcontroller unit to detect whether the current abnormal pressure data of the battery pack is valid; if not, on the basis of a preset control policy, controlling the microcontroller unit to enter a sleep state; and if yes, on the basis of the microcontroller unit, waking up a vehicle control unit. When onboard boost pressure sensors malfunction, the method and the apparatus can solve the problem that frequently waking up microcontroller units and consequently frequently waking up vehicle control units interfere with safe vehicle driving.