2498 results about "Vehicle type" patented technology

The invention belongs to the technical field of charging station planning, and discloses a charging station planning method and system based on an automobile charging demand, and the method comprises the steps: building a space-time charging demand distribution map through multi-source data collection and fusion processing, carrying out the seasonal change analysis and future demand prediction, and obtaining a dynamic charging demand prediction model; site layout optimization under a multi-constraint condition is performed by combining an urban road network structure and traffic flow data to form a preliminary charging station layout scheme, and power grid load capacity evaluation and renewable energy access analysis are implemented to establish an energy collaborative supply guarantee system. And designing a peak-valley period charging price dynamic adjustment and appointment queuing mechanism to form an intelligent scheduling control strategy, and finally obtaining a diversified charging facility configuration scheme through different charging power requirements and vehicle type suitability evaluation. According to the method, the problems of inaccurate demand prediction, unreasonable layout, uneven resource allocation and the like in traditional planning are solved, and accurate matching of charging resources and user demands is realized.

The invention discloses a portable unmanned aerial vehicle sound wave countering system and method, and relates to the field of radio countermeasure and sound wave interference, and the system comprises a multi-source sensing module which is used for obtaining a multi-modal feature data set of a target unmanned aerial vehicle; the signal processing module is used for carrying out space-time correlation analysis on the multi-modal characteristic data set and generating a threat evaluation parameter set containing an unmanned aerial vehicle type identification result, communication protocol analysis characteristics and navigation mode discrimination indexes; the countering strategy generation module is used for generating a sound wave modulation parameter set, a radio frequency interference waveform sequence and a positioning deception signal template; the cooperative interference module is used for generating a multiband joint countering instruction set according to the improved phase cooperative control strategy; and the execution monitoring module is used for executing the multiband joint countering instruction set in the preset airspace range. According to the portable unmanned aerial vehicle sound wave countering system and method provided by the invention, the multi-level countering capability for different threat levels is improved.

The invention relates to the field of new energy charging, in particular to a new energy charging management method for a smart city. The method comprises the steps that a municipal vehicle operation feature data set is acquired, on the basis, municipal vehicles are subjected to type division through a vehicle type classification strategy, and a municipal vehicle driving type set is determined; acquiring a municipal vehicle task set, and on the basis, tracking a dynamic task variable set of a task execution vehicle in a municipal vehicle driving type set in a task execution process; according to the dynamic task variable set, analyzing an association relationship between the dynamic task variable set and the real-time power consumption data through a multi-modal fusion strategy, and determining dynamic power change information; and acquiring a charging station information set, dynamically optimizing a charging decision in combination with the municipal vehicle task set and the dynamic electric quantity change information, and determining and outputting an optimal charging strategy. In the municipal vehicle charging decision-making process, the utilization rate of charging resources is improved, and the continuity of municipal task execution is ensured.

The invention discloses a highway traffic operation carbon emission monitoring and calculating method, which comprises the following steps of: classifying vehicles running on a highway according to vehicle types, and establishing a carbon emission correction model of a carbon emission coefficient and a longitudinal slope rate of the vehicle type through a real vehicle test; arranging a monitor in the section of a target highway section, and acquiring basic information and driving information of vehicles passing through the section through the monitor; a carbon emission factor method is adopted to calculate the carbon emission Cm before correction of the vehicle m in the highway section interval; and S3, according to the carbon emission correction model established in the step S1, in combination with the average longitudinal slope ratio of the road section, correcting the carbon emission Cm before correction in the step S3, and obtaining the carbon emission C after correction of the vehicle m in the specified highway road section interval. According to the calculation method, available information is fully utilized from the perspective of an expressway operation management party, and the expressway traffic operation carbon emission monitoring calculation precision is improved.

The invention provides a vehicle path planning method based on an adaptive optimization algorithm, and relates to the technical field of adaptive algorithm application, and the method comprises the steps: 1, based on a current order demand, a vehicle resource and a preset constraint condition, obtaining a vehicle path planning result; an initial path scheme set meeting the requirements of a time window, the number of splitting times, vehicle type matching and a temperature zone is generated through a step-by-step progressive decision-making process; 2, starting a plurality of independent optimization links according to the initial path scheme set, and dynamically adjusting local path nodes by each link through a greedy strategy to generate an optimization candidate scheme set; and step 3, performing real-time evaluation on the optimized candidate scheme set, determining a final candidate scheme based on the cost saving rate, the demand satisfaction rate and the soft service quality index, and updating the final candidate scheme as the current reference scheme. According to the method, dynamic events are sensed in real time through an adaptive optimization algorithm, influences are evaluated, the path is intelligently adjusted, and the timeliness and accuracy of path planning are improved.

The invention relates to a data-driven intersection vehicle complex interaction behavior strategy simulation method and system. The method comprises the following steps: acquiring real intersection vehicle traffic trajectory data, constructing a plurality of interaction scenes, extracting observation information of each time step, and obtaining real vehicle trajectory data; taking each vehicle as an intelligent agent, based on a Gaussian strategy network pre-constructed according to the vehicle type, calculating a reward by using a reward based on a target guiding gain and a reward estimator from a discriminator, and generating simulated vehicle trajectory data; based on the real vehicle trajectory data and the simulated vehicle trajectory data, respectively extracting observed value-action pair information, updating a discriminator of an intelligent agent by taking distinguishing different attribute information to the greatest extent as a target, and updating a Gaussian strategy network and a value estimator network in a generator by taking confusing the discriminator to the greatest extent as a target, so as to obtain the real vehicle trajectory data and the simulated vehicle trajectory data. And vehicle complex interaction behavior strategy simulation based on multi-agent countermeasure reverse reinforcement learning is realized.

The invention provides a road inspection method and device in severe weather, electronic equipment and a storage medium, and the method comprises the steps: carrying out the recognition processing of a scene image through a road inspection model, and obtaining a road inspection result of a target road; the training process of the road inspection model comprises the following steps: acquiring a data set, wherein the data set comprises a severe weather image; performing feature extraction on the data set by adopting a pyramid network to obtain a plurality of levels of first feature extraction results; when information fusion is carried out on the first feature extraction results of the multiple levels, feature adaptation processing is carried out according to the first feature extraction results, and a second feature extraction result is obtained; performing feature enhancement modulation on the second feature extraction result by adopting double branches, and merging feature enhancement modulation results to obtain a modulation feature map; and predicting the modulation feature map to obtain a prediction result. The method has the beneficial effect that the vehicle type identification accuracy during road inspection in severe weather is improved.

The invention discloses an elastic resource allocation and barrier gate linkage control system and method for mixed traffic of parking lots, a medium and equipment, and the system comprises a vehicle type recognition module, a dynamic resource allocation module, a cooperative control decision module, an emergency channel management module and a multi-mode communication module. According to the parking lot mixed traffic management system integrating vehicle-road cooperative communication, double-layer game optimization and elastic resource pool division, an independent resource pool and a dynamic quota adjustment mechanism of automatic driving vehicles and manual driving vehicles are constructed, and a charging pile-barrier gate cooperative optimization unit and a multi-stage emergency response protocol are combined; the global optimization of parking lot resource allocation and traffic control is realized, and the problems of prominent resource competition contradiction, separation of barrier gate control and resource allocation, insufficient intelligence of an emergency response mechanism and short delay of multi-mode communication and decision cooperation in a mixed traffic scene are solved.

The invention relates to an AI identification road traffic safety illegal behavior image evidence obtaining method and device based on an unmanned aircraft, equipment and a medium, and the method comprises the steps: calling a no-parking region identification model, so as to determine a no-parking region mask in a to-be-inspected region image, calling a potential violation vehicle detection model to identify potential violation vehicles in the no-parking area mask so as to determine bounding boxes of the potential violation vehicles and vehicle types corresponding to the bounding boxes; and calling a target tracking algorithm to perform target tracking on the potential violation vehicle, and determining the potential violation vehicle as a target violation vehicle when detecting that the intersection-to-union ratio between the bounding box and the no-parking area mask in the plurality of continuous image frames exceeds a preset threshold value and the potential violation vehicle is in a static state. And sending the violation evidence packet corresponding to the target violation vehicle to a traffic management system. According to the invention, the efficiency of the monitoring system is improved, and accurate and verifiable violation evidences are provided for the traffic management system.

The invention discloses a new energy automobile charging pile parking space occupation detection and billing linkage control method, and relates to the technical field of urban public resource optimization distribution and new energy automobile charging pile parking space management. Comprising the steps of multi-source sensing and state fusion: sensing real-time occupation states, vehicle types and charging behavior states of all parking spaces in real time by using a parking space detector, an ALPR visual sensor, a Hall sensor and a charging pile state communication protocol, collecting original data, deploying edge nodes at the parking spaces, and fusing the original data in real time, outputting a unified parking space state object after fusion; original data are collected in real time through the parking space detector, the ALPR visual sensor, the Hall sensor and the charging pile state communication protocol, and through multi-dimensional behavior analysis of the parking space state, the abnormal behaviors that a fuel truck occupies a space for a long time, an electric vehicle occupies the space and is not charged, and the electric vehicle is detained after being charged can be accurately recognized; and an accurate basis is provided for subsequent charging processing and precise management.

The invention belongs to the technical field of virtual automobile exhibition, and particularly relates to a virtual automobile exhibition system based on digital twinning. The system comprises a digital twinning construction module which generates a lightweight model and supports first-person visual angle dynamic updating; the user behavior analysis module is used for mapping data such as a sight track into a weighted directed graph, and predicting vehicle types and configuration requirements by using a multi-head graph attention network; the personalized configuration selection module is used for generating a configuration scheme in combination with digital twinborn parameters; the virtual test driving module is used for providing immersive experience based on the configuration scheme and the scene; and the background data analysis module integrates the data to generate a user portrait so as to optimize the exhibition content. According to the system, dynamic virtual-real mapping, accurate demand prediction and data-driven intelligent exhibition are realized, and the user interaction depth and the car purchase efficiency are improved.

The invention discloses a road shoulder vehicle exclusion method based on an inspection system, and relates to the technical field of intelligent transportation, and the method comprises the steps: obtaining a parking area image shot by an inspection vehicle, recognizing a vehicle target in the image, and extracting the license plate position information; based on vehicle three-dimensional feature reconstruction and space projection analysis, generating a space distribution thermodynamic diagram of the vehicle in the image coordinate system; constructing a dynamic segmentation line model, and adaptively generating a road shoulder region judgment boundary according to the parking space type and the road geometric parameters; and through a multi-target trajectory association algorithm, the berth vehicle and the road shoulder vehicle are distinguished, and the berth occupation state is output. According to the scheme provided by the invention, the spatial positioning precision and behavior judgment reliability in a complex scene can be remarkably improved, the limitations on environmental adaptability, vehicle type compatibility and time sequence continuity are broken through, and a road shoulder violation management and control solution with high robustness and full-time coverage is provided for urban traffic management.

The invention relates to a traffic state perception and control method based on laser vision fusion, and relates to the field of intelligent traffic, and the method comprises the steps: obtaining vehicle images and point cloud data through a traffic monitoring system of a highway entrance ramp, carrying out the vehicle static feature recognition and track reconstruction, and carrying out the analysis to obtain multi-dimensional parameters; according to the method, the traffic flow monitoring means such as license plate attribution distribution, vehicle type distribution and running speed mean value are selected, the congestion condition is predicted, the predicted congestion coefficient is output, and finally a traffic control scheme is formulated and executed according to the predicted congestion coefficient, so that the problems of insufficient data accuracy and reliability caused by single traffic flow monitoring means are solved; the method solves the technical problems of low prediction comprehensiveness and low prediction precision caused by traffic congestion prediction, effectively improves the accuracy and reliability of congestion prediction, provides a scientific basis for traffic management, and is helpful for optimizing traffic flow and reducing congestion.

The invention relates to a millimeter-wave radar global vehicle track splicing method based on track similarity, which comprises the following steps of: sensing a moving vehicle on a traffic road by utilizing millimeter-wave radars arranged on the traffic road at certain intervals, and acquiring vehicle track data detected by the millimeter-wave radars; and according to the vehicle type similarity, the distance similarity, the speed similarity and the behavior similarity, vehicle tracks of the two radar monitoring blind areas and the overlapping area are matched, the matching degree of the two target vehicle tracks is judged, and finally, tracks between frame windows are spliced according to the matching degree to obtain a final target track. The method solves the problem of discontinuous cross-device multi-target trajectory tracking, solves the problem of poor splicing effect of a trajectory splicing algorithm based on a linear prediction model, and has the advantages of being independent of moving target feature information, high in precision, good in adaptability and the like.

The invention provides an unmanned aerial vehicle radio frequency signal identification method and a related device, and relates to the technical field of deep learning. Signal data are loaded from an unmanned aerial vehicle radio frequency signal data set, and the data set comprises different unmanned aerial vehicle types, detection distances and signal fragment labels; dividing the signal data into a plurality of sections of continuous sampling point sequences which are not overlapped with one another; adding Gaussian additive white noise with different powers into the signal data, and constructing a training data set comprising a plurality of signal-to-noise ratios; performing short-time Fourier transform on the signal data to generate a time-frequency graph; performing principal component analysis and dimension reduction on the time-frequency graph, and extracting key time-frequency features; performing hyper-parameter optimization on the deep neural network model by adopting a k-fold cross validation strategy; and the optimized deep neural network model is used to classify and identify the time-frequency graph. The technical effect of accurately identifying the radio frequency signal of the unmanned aerial vehicle under different signal-to-noise ratio conditions is realized.

The invention relates to a multi-type emergency vehicle signal dynamic priority control method based on vehicle-road cloud cooperation, and belongs to the technical field of intelligent traffic control. The method comprises the following steps: acquiring an emergency vehicle state and traffic environment data in real time through cooperation of a vehicle-mounted terminal, roadside equipment and a cloud control platform; the cloud control platform dynamically calculates priority scores based on vehicle types, task emergency degrees, predicted arrival time, real-time traffic influences and path complexity multi-dimensional factors; when multiple vehicles have conflicts, collaborative decision making is carried out based on scores; and finally, an optimized signal control strategy is generated and executed. The system effectively solves the problems that a traditional priority control mode is extensive, and traffic jam and multi-vehicle conflicts are easily caused, achieves the purpose that interference to social traffic is minimized while efficient passing of emergency vehicles is guaranteed, and improves the overall efficiency and safety of an urban traffic system.

A vehicle information communication system including a center device and an in-vehicle device is provided. When pieces of configuration information regarding configurations of respective devices are received from electronic control units, the in-vehicle device transmits a configuration information list including the pieces of configuration information to the center device. The center device includes a configuration information storage unit storing the configuration information list approved with respect a vehicle type, compares the configuration information list received from the in-vehicle device with the configuration information list stored in the configuration information storage unit. When the center device determines that the configuration information list received from the in-vehicle device is disapproved, the center device transmits a presence of abnormality to the in-vehicle device.

The invention relates to the technical field of computer vision, and discloses a vehicle paint make-up color matching method based on computer vision, and the method comprises the steps: collecting a vehicle paint image in real time through an industrial camera, constructing a multi-dimensional color feature extraction system after preprocessing, and building a color feature database through a color space conversion technology; and positioning a target color card by combining a feature matching algorithm. Meanwhile, a historical paint make-up case information base is constructed, a multi-factor weight distribution model is established, an optimal matching scheme is solved by adopting an improved particle swarm optimization algorithm, and finally a result is output through an interactive verification platform and tracked and fed back. According to the method, multiple factors are comprehensively considered, the precision and efficiency of color matching are improved, and the method is suitable for paint make-up color matching of vehicles of different vehicle types, years and paint surface types and has high practicability and popularization value.

The invention discloses a bottom-up urban traffic carbon emission real-time estimation method and system based on a traffic monitoring video, and belongs to the technical field of intelligent traffic and urban low-carbon governance crossing. The multi-source data comprises meteorological monitoring data, traffic monitoring video data, vehicle parameter data and road geographic information system data; inputting the traffic monitoring video data into a vehicle type recognition model, and outputting a vehicle type recognition result; obtaining vehicle type aggregation data based on the vehicle type identification result; dynamic carbon emission factors are generated in combination with localized configuration of a motor vehicle emission simulator model; and estimating the urban traffic carbon emission based on the dynamic carbon emission factor and the power grid carbon emission coefficient. According to the method, the problems of poor real-time performance, data dimension splitting, insufficient model adaptability and the like of a traditional monitoring method are solved, and based on the real-time analysis capability of the traffic monitoring video, technical support is provided for urban traffic carbon emission accurate metering and intelligent emission reduction decision making.

A system for detecting the motion state of a vehicle behind a mobile maintenance vehicle based on thunder-vision fusion belongs to the technical field of traffic intelligent control and comprises a multi-source sensing module, a target detection module, a trajectory tracking module and a vehicle motion state modeling module. A structured state output module performs standardized integration on vehicle target trajectory information provided by a detection and tracking module based on a unified data expression model to form a multi-dimensional structured data format including fields such as a relative distance, a relative speed, an acceleration, a vehicle type, a lane number, a frame timestamp and the like; consistency analysis and downstream calling can be conveniently carried out among different systems, the problem that a traditional detection system is difficult to integrate due to lack of a structured interface is avoided, the system supports flexible butt joint with a road sensing platform, a vehicle-mounted control system and an upper-layer data analysis module, good expansibility and cross-platform adaptation capacity are achieved, and the system is suitable for being popularized and applied. And the engineering availability and deployment efficiency of the traffic flow state information are obviously improved.

The invention relates to a charging parking space intelligent occupation management linkage control method and system based on the Internet of Things, and belongs to the field of charging parking space management.The method comprises the steps that the state of a charging pile, the state of a parking space lock and occupation information are collected through Internet of Things equipment, and a dynamic parking space resource pool is constructed; based on knowledge graph semantic reasoning, matching parking spaces meeting vehicle type adaptation and available time periods, and combining historical late data of a user to set elastic reservation retention duration; the identity legality is verified by comparing the information of the reserved vehicle with the information of the actual parking vehicle; and when the charging pile is controlled to start charging and the parking space lock is unlocked, the block chain is synchronously associated to generate the transaction voucher, and the transaction record uploading is completed based on the voucher after the charging is finished, so that the technical effects of efficient matching and dynamic management of the charging parking space resources are realized.

The invention provides a motorcycle fuel tank electric arc welding fixing tool, and relates to the technical field of welding tools, the motorcycle fuel tank electric arc welding fixing tool comprises a main body, and guide plates are fixedly mounted on the front side and the rear side in the main body; a sliding seat is mounted on the guide plate in a sleeving and sliding manner; a fixed sliding plate is fixedly mounted on the inner side of the sliding seat; a supporting column is fixedly mounted on the inner side of the fixed sliding plate; a guide column is fixedly mounted on the supporting column; a transmission sliding seat is mounted on the guide column in a sleeving and sliding manner; a stress block is fixedly mounted on the outer side of the inner end of the transmission sliding seat; and a pushing piece is mounted in the transmission sliding seat in a sliding manner. After the modularized installation socket is separated from the flexible chuck, chucks of different specifications can be independently replaced to adapt to oil tanks of multiple vehicle types, the replacement time of a single chuck is extremely short, the production line remodeling efficiency and the welding efficiency are improved, and the problems that the time consumed by single replacement of a rubber sleeve is long, the production line rhythm is affected, and the production cost is reduced are solved. And the contact surface of the clamp can be damaged due to repeated disassembly and assembly.

An analysis support system supports analysis for addressing vulnerability of software provided to vehicles. The analysis support system includes: a data receiver that obtains vehicle type information on each of a plurality of vehicle types of the vehicles provided with the software having vulnerability; and a determiner that performs a first determination to classify the plurality of vehicle types into two or more groups for the analysis, based on the vehicle type information obtained for each of the plurality of vehicle types.

The invention discloses a multi-steering-wheel calibration method and device and a storage medium, and the method comprises the steps: constructing a conversion relation between the motion state of each steering wheel and the motion state of a vehicle body, and obtaining a motion model parameter; indicating the vehicle body to move according to the motion instruction, and calculating and calculating pose parameters based on the motion model parameters and the motion speed and the motion angle of each steering wheel in the motion instruction; obtaining an actual pose parameter after the vehicle body moves, and carrying out difference calculation on the calculated pose parameter and the actual pose parameter to obtain a pose residual error; and updating the steering wheel zero offset of each steering wheel by using the pose residual error to obtain a finally calibrated target steering wheel zero offset of each steering wheel. The scheme is suitable for multi-steering-wheel vehicle type speedometer calibration, the vehicle body pose can be calculated according to the speedometer data by deducing the conversion process of the multi-steering-wheel encoder and the vehicle body motion state, then the difference value between the vehicle body pose and the actual pose parameter is calculated to adjust the zero offset, and the accuracy of zero offset adjustment is improved.

A traffic management system and method for operating the same includes a plurality of roadside devices associated a plurality of intersections. A control module is programmed to receive priority requests from priority connected vehicles that comprise a priority level associated with a type of vehicle. The control module generates average estimated arrival time for an intersection for the vehicles based on historical data, receives roadside device data from a plurality of roadside devices, estimates congestion and flow dynamics for the priority connected vehicles based on the average estimated arrival times and real-time data from the vehicles and data from the roadside devices, determines adapted arrival times based on the congestion and flow dynamics and adjusts a signal phase and timing for a traffic signal of the intersection for the priority connected vehicles based on the priority and adapted arrival times. A traffic signal operates with the signal phase and timing.

The invention discloses a park access control system and method based on artificial intelligence, and the method comprises the steps: carrying out the license plate recognition and color classification of a visited vehicle through a license plate recognition model based on an artificial intelligence algorithm, obtaining a license plate number and a vehicle type, and judging the passing authority type of the visited vehicle according to the license plate number; constructing a carbon emission measuring and calculating model according to the vehicle type, and analyzing the carbon emission of the vehicle entering the park by using the carbon emission measuring and calculating model to obtain carbon emission data; according to the carbon emission data, counting the total carbon emission amount of each vehicle entering the park in a preset period, marking the vehicle of which the difference value between the total carbon emission amount of each vehicle and a preset carbon emission limit is smaller than a preset threshold value as a limited vehicle, and sending an early warning signal; and making a passing decision for an external vehicle according to the vehicle type and the carbon emission capacity of the current day. The invention relates to the technical field of deep learning, and solves the technical problem that a traditional access control system cannot accurately identify vehicle types and cannot make intelligent traffic decisions according to vehicle carbon emission.

The invention relates to the technical field of road traffic, in particular to a safety control system and method for an emergency lane, and the system comprises a data collection module, an intelligent regulation and control module, an information feedback module and a remote control terminal. The data acquisition module is used for acquiring an image of a vehicle, a driving speed and a track of the vehicle on a road and a driving speed and a driving angle of the vehicle on an emergency lane. The intelligent regulation and control module is used for receiving the image of the vehicle, the driving speed and the track of the vehicle on the road and the driving speed and the driving angle of the vehicle on the emergency lane, and analyzing and generating a vehicle type according to the image of the vehicle; according to the driving speed and track of the vehicle on the road and in combination with the vehicle type, predicting whether the vehicle is out of control and needs to use an emergency lane; the prediction result is transmitted to the information feedback module; according to the invention, danger-avoiding guidance can be effectively provided for drivers, so that the emergency success rate is improved.

The invention discloses a vehicle blind area intelligent monitoring early warning and active risk prevention and control system, which comprises a sensing module, a central AI processing unit, an early warning module and an execution control module, and is characterized in that the sensing module is used for collecting vehicle blind area information in a full-dimension manner; the central AI processing unit is used for processing the information acquired by the sensing module, performing risk assessment according to the processed blind area information and the working state of the vehicle, sending an early warning signal to the early warning module, and sending a control signal to the execution control module; the early warning module is used for sending a risk type and grade signal which can be perceived by a driver according to an early warning signal output by the central AI processing unit; and the execution control module is used for outputting control and driving signals according to the control signal sent by the central AI processing unit, so that a corresponding vehicle power device acts, and active risk prevention is realized. The system effectively prevents and controls blind area collision accidents, adapts to various vehicle types such as cars, SUVs, light trucks and heavy trucks, and does not need to redesign a hardware architecture.

The invention discloses a whole vehicle power hardware-in-the-loop simulation test system and method under a P2.5 + P4 hybrid architecture. The whole vehicle power hardware-in-the-loop simulation test system comprises a controller, a test cabinet and an upper computer system, the hybrid power gearbox control system is electrically connected with the high-voltage control system; the test cabinet is electrically connected with the hybrid power gearbox control system, the high-voltage control system and the upper computer system; the upper computer system comprises a management module, a signal interface module, a controller interface module and a virtual simulation vehicle module, the management module is connected with the test cabinet and the signal interface module, and the signal interface module is used for realizing signal interaction between the controller interface module and the management module; the controller interface module is connected with the signal interface module, the Soft ECU module and the power simulation module. Standardized development of the power simulation test system is realized, the modular design supports rapid adaptation of different vehicle type platforms and power architectures, and repeated development is reduced.