1211results about "Vehicle condition input parameters" patented technology

A management server system may obtain data associated with a vehicle. The management server system may obtain the data from a plurality of data sources associated with the vehicle. The data may include a plurality of subsets of data that each correspond to correspond to a particular driver assignment category. Each subset of data may identify a particular persona as a driver of a vehicle. The management server system may determine a driver of the vehicle based on each subset of data and a hierarchical plurality of driver assignment categories. Based on the determination of the driver of the vehicle, the management server system can dynamically map the driver to a particular event. The management server system may generate a user interface identifying the driver and the event.

This application discloses a method, system, and related equipment for intelligent energy management of new energy vehicles. Based on the vehicle's starting and ending points, at least one candidate energy-saving path is determined. The predicted energy consumption of the vehicle along at least one candidate energy-saving path is lower than that of other paths. The total energy consumption is predicted based on road condition information and energy consumption impact information for each path. In response to the selection of at least one candidate energy-saving path, a preset travel route is determined. The preset travel route includes multiple road segments, and the total energy consumption includes the energy consumption of each road segment. With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial state of charge (SOC) of the power battery in each road segment, the energy consumption of the road segment, and the vehicle's actual overall demand, ensuring the engine operates within its high-efficiency range. Using this application can reduce fuel consumption for users and improve the driving experience.

A new energy vehicle energy intelligent management method and system, and a related device. The method comprises: determining at least one candidate energy-saving path on the basis of a starting point and an ending point of a vehicle, wherein path-based vehicle energy consumption predicted on the basis of the at least one candidate energy-saving path is lower than path-based vehicle energy consumption predicted on the basis of other paths, and the path-based vehicle energy consumption is predicted on the basis of road condition information of each path and energy consumption impact information; in response to a selection operation on the at least one candidate energy-saving path, determining a preset travel path, wherein the preset travel path comprises a plurality of road sections, and the path-based vehicle energy consumption comprises road section-based vehicle energy consumption of the plurality of road sections; and by taking the minimization of fuel consumption along the preset travel path as an objective, controlling an operation state of an engine on the basis of an initial SOC of a power battery and the road section-based vehicle energy consumption at each road section and an actual vehicle demand of the vehicle, so that the engine is in an efficient operation range during operation.

A method is for improving a vehicle-dynamics-related stability of a utility vehicle, wherein the utility vehicle has a lift axle. The method includes: determining a current ground speed of the utility vehicle; determining a stability-critical speed of the utility vehicle; comparing the current ground speed with the stability-critical speed; determining a lift status of the lift axle of the utility vehicle; and lowering the lift axle of the utility vehicle if the lift status represents a raised lift axle and the current ground speed is greater than or equal to the stability-critical speed. A device is for improving a vehicle-dynamics-related stability of a utility vehicle.

The invention belongs to the technical field of industrial vehicle active safety control, and particularly discloses an industrial vehicle safety anti-collision early warning method and device, and the method comprises the steps: building a unified space-time perception reference through fusing ultrasonic wave, inertial navigation, laser radar and camera data; extended Kalman filtering is fused with inertial navigation prediction and laser radar poses, and high-precision positioning in the tunnel is achieved. Constructing a high-dimensional time sequence feature vector, and predicting a future collision risk in combination with a Bi-LSTM model; the early warning threshold value is dynamically adjusted according to the load, the gradient and the sensor state; the method has the following advantages that the early warning accuracy and response speed under the complex working condition are remarkably improved, and the operation safety of the industrial vehicle is guaranteed.

An AI-connected-electrified (ACE) heavy truck system equipped with an intelligent multi-mode hybrid (iMMH) powertrain system and a vehicle supervision control strategy based on machine learning (ML) or reinforcement learning (RL) paradigm are presented. This system ensures industry-leading power and braking performance of the ACE heavy truck while automatically optimizes both energy saving and emission reduction based on the vehicle's dynamic driving data and 3D electronic map information of roads for any transport event. In this application, the conventional analog electronic control (AEC) method is replaced by a novel digital pulse control (DPC) method on the instantaneous power function of the engine. The DPC method converts the complex surface working conditions of the AEC engine of the hybrid vehicles into simpler pre-defined working condition lines of the DPC engine. Consequently, the multi-variable nonlinear technical problem of simultaneously optimizing real driving environment (RDE) fuel consumption and pollutant emissions of the ACE heavy truck is simplified into two decoupled quasi-liner optimization problems, and ensures the reduction of on-vehicle computing resources for real-time AI inference computation and the improvement of the optimal performance, the convergent rate, and the robustness of the corresponding fuel-saving algorithm for the trained ML model or the learned RL model. Ultimately, the ACE truck achieves in the engineering sense the global minimum RDE fuel consumption and pollutant emissions meeting the standard consistently at high performance to cost ratio for any transport event and the RDE fuel consumption is decoupled from the vehicle configuration parameters and the human driver.

A computer system has processing circuitry to selectively operate the powertrain system in at least a freewheeling mode, in which an output shaft of the engine is non-rotating, and the engine is disconnected from the one or more drive wheels. The processing circuitry is further configured to: determine an opportunity for the freewheeling mode by predicting a feasibility of restarting the engine along an intended route by using a controllable clutch. Predicting the feasibility of restarting the engine by using the controllable clutch includes determining fulfillment of any one of a first restarting condition and a second restarting condition. Determining fulfillment of the first restarting condition includes comparing current total weight of the vehicle with a threshold value indicative of a minimum total weight for avoiding excessive braking of the vehicle during restarting at a high friction road condition with a given total gear ratio. Determining fulfillment of the second restarting condition includes comparing a current drive axle load with a corresponding threshold value indicative of a minimum drive axle load on a drive axle for avoiding excessive wheel slip during restarting at a low friction road condition with a given total gear ratio. The powertrain system is controlled into the freewheeling mode upon the fulfillment of any one of the first restarting condition and the second restarting condition.

The invention provides a vehicle stability control method, device and system and electronic equipment, relates to the technical field of vehicle active safety control, and aims to construct a control framework based on a two-degree-of-freedom dynamical model and realize accurate control of the yaw velocity and the side slip angle of a vehicle. By introducing the road adhesion coefficient and combining the vertical load of each wheel for independent torque distribution, the tire adhesion utilization rate and the environmental adaptability and safety of the vehicle under different road conditions (such as ice and snow, wet and slippery conditions and dry conditions) are improved. According to the scheme, calculation is easy and convenient, response is rapid, good engineering practicability is achieved, and the robustness and adaptability of the system under the complex working condition are enhanced while the vehicle stability and the path tracking capacity are improved.

A system for controlling an overtake maneuver of a control vehicle includes a controller. The controller is structured to: receive at least one overtake parameter associated with a control vehicle for overtaking a front vehicle in a vehicle lane; receive an indication of a direction of traffic in an overtake lane that is adjacent to the vehicle lane; receive an indication that the at least one overtake parameter satisfies at least one condition according to the direction of the traffic in the overtake lane; and in response to the at least one overtake parameter satisfying the at least one condition according to the direction of traffic in the overtake lane, execute an overtake maneuver by adjusting at least one parameter of the control vehicle or providing a command to an operator of the control vehicle to execute the overtake maneuver.

In some embodiments, techniques are provided for autonomously backing a vehicle (such as a Class 8 truck) to a trailer. In some embodiments, environment sensors such as image sensors and range sensors mounted to the vehicle detect a trailer and determine distances between the trailer and the vehicle as well as relative angles of the trailer and the vehicle. In some embodiments, the vehicle determines a path to the trailer based on this information, and autonomously controls braking, torque, and / or steering of the vehicle to autonomously back the vehicle along the determined path.

A powered balancing mobility device that can provide the user the ability to safely navigate expected environments of daily living including the ability to maneuver in confined spaces and to climb curbs, stairs, and other obstacles, and to travel safely and comfortably in vehicles. The mobility device can provide elevated, balanced travel.

The invention provides a road adhesion coefficient estimation method based on an adaptive slip speed tire model. The method comprises the following steps: establishing a vehicle dynamics model; estimating a tire force based on vehicle dynamics; estimating the vehicle speed by using an unscented Kalman filtering algorithm based on singular value decomposition; a tire model based on the slip speed is constructed, and normalized tire force is calculated according to the tire model; estimating a road adhesion coefficient by using an unscented Kalman filtering algorithm based on singular value decomposition; according to the self-adaptive slip speed tire model provided by the invention, the problem of denominator singularity of a current slip rate-based tire model at a low speed and the limitation of a fixed stiffness parameter are overcome, so that the estimation precision and the response speed of the road adhesion coefficient are improved.

Systems and methods for navigating a host vehicle are disclosed. In one embodiment, at least one processing device may be programmed to receive an image representative of an environment of a host vehicle, determine a planned navigation action of the host vehicle, analyze the image to identify a target vehicle having a direction of travel toward the host vehicle, and transmit the target vehicle to the host vehicle. And determining a next state distance between the host vehicle and the target vehicle that would result from the planned navigation action being taken. The at least one processing device may further determine a stop distance of the host vehicle based on a braking rate, a maximum acceleration capability, and a current speed of the host vehicle, determine a stop distance of the target vehicle based on a braking rate, a maximum acceleration capability, and a current speed of the target vehicle, and performing a planned navigation action if the determined next state distance is greater than the sum of the stopping distances of the host vehicle and the target vehicle.

The invention discloses a vehicle torque control method and device and related equipment. The method comprises the steps that the utilization adhesion coefficient and the slip rate of target wheels of a vehicle in a preset first time period and the feedback torque of the target wheels are obtained, and the target wheels comprise at least one wheel driven by a target motor; the utilization adhesion coefficient and the slip rate of the target wheel are fitted, and the longitudinal rigidity of the vehicle is determined; performing torque operation on the longitudinal stiffness to obtain a feed-forward torque of the target wheel; and determining the sum of the feedforward torque and the feedback torque as a target motor torque, and controlling the target motor to output the target motor torque, the target motor being a motor corresponding to the target wheel.

An autonomous driving control apparatus includes a memory that stores computer-executable instructions. The apparatus also includes a processor that executes the instructions by accessing the memory. The processor controls the target vehicle in a first control mode based on a first driving status of a target vehicle, a second driving status of a forward vehicle driving around the target vehicle, or any combination thereof. The processor releases the first control mode applied to the target vehicle by satisfying the conversion condition that the control mode applicable to the target vehicle is capable of being converted from the second control mode to the first control mode. The processor controls the target vehicle in the second control mode, based on the first driving status, the second driving status, the state of the driver of the target vehicle, energy consumption predicted according to information about a front road, or any combination thereof.

A vehicle having a transceiver and a processor is disclosed. The transceiver may receive environment condition, road condition, and vehicle information. The processor may be communicatively coupled with the transceiver, and may obtain the environment condition, the road condition, and the vehicle information from the transceiver. The processor may determine that the vehicle may be in an unstable driving state based on the obtained condition. Responsive to determining that the vehicle may be in the unstable driving state, the processor may determine a vehicle instability type based on the environment condition, the road condition, and the vehicle information. The processor may be further configured to generate a first instruction for a first vehicle wheel and a second instruction for a second vehicle wheel based on the vehicle instability type, and cause a vehicle movement based on the first instruction and the second instruction.

Sensor data is received at a processor of a vehicle including at least one sensor, a plurality of axles, and a plurality of tires coupled to the plurality of axles. A determination is made by the processor if at least one tire from the plurality of tires is faulty based on the sensor data. A determination is made by the processor of at least one side of the vehicle and at least one axle from the set of axles associated with the at least one tire in response to the determining that the at least one tire is faulty. A determination is made by the processor of at least one remedial action to be performed by the vehicle based on the sensor data, the at least one side, and the at least one axle.

A method for approximating a coefficient of friction includes: determining a trajectory of the vehicle for a driving situation; determining a steering angle expected value; determining a steering angle actual value, which is set on the vehicle in the driving situation; determining a vehicle position of the vehicle in the driving situation; determining a manipulated variable deviation between the steering angle expected value and the steering angle actual value, and / or determining a setpoint-actual deviation between the vehicle position during the driving situation and the trajectory; and, approximating the coefficient of friction based on the determined manipulated variable deviation and the determined setpoint-actual deviation. A driver assistance system is configured to perform the method. A vehicle includes the driver assistance system. A computer program product is configured to cause the method to be performed.

The application discloses a wind power blade transportation anti-overturning control method, which comprises the following steps: S1, determining a vehicle dangerous overturning line and inputting vehicle structure parameters; S2, reading sensor information in real time; S3, judging a vehicle running state; S4, calculating related parameters of a limit overturning state of the vehicle in a horizontal state, a low-speed running or parking state of the vehicle; S5, calculating related parameters of the limit overturning state of the vehicle in a high-speed running state; and S6, judging according to the values of five transportation danger coefficients respectively: when all the transportation danger coefficients η are less than or equal to 90%, the wind power blade transportation vehicle is in normal work, and the steps S2 to S6 are circularly performed; otherwise, a display issues a warning. The application also provides a calculation method of the transportation danger coefficient in the wind power blade transportation anti-overturning control. The application can help a driver to comprehensively understand a vehicle working condition, further improve the accuracy of a vehicle safety state judgment, and reduce the overturning phenomenon in the existing wind power blade transportation.

A travel assistance device configured to detect a surrounding environment of a first vehicle by an onboard sensor of the first vehicle and assist the autonomously traveling first vehicle in traveling includes at least one computer configured to perform processing including: setting allowable travel speed, that is a speed at which the first vehicle can be caused to travel under autonomous driving control, to a speed lower than a legal speed or a set speed, that is a speed set by a user, according to a recognition state of the onboard sensor or a vehicle state of the first vehicle; and generating an alternative route including a road having a speed limit less than or equal to the allowable travel speed as a target travel route of the first vehicle, based on the current position of the first vehicle, the destination of the first vehicle, and the allowable travel speed.

A method for determining a desired lateral acceleration of a vehicle is provided. The method may include determining a first raw lateral acceleration request based at least in part on a steering wheel angle of the vehicle. The method may also include determining a maximum allowable lateral acceleration based at least in part on the tire slip model. The method may also include determining a desired lateral acceleration based at least in part on the first raw lateral acceleration request and the maximum allowable lateral acceleration. The method may also include adjusting operation of the vehicle based at least in part on the desired lateral acceleration.

A model predictive lane centering (MPLC) control system with reference switching and disturbance rejection includes: an MPLC application having control logic that utilizes model predictive control (MPC) for selectively tracking static and dynamic references, adapts MPC weights and constraints for trajectory tracking when the system switches between static and dynamic references. A Kalman filter estimates a lateral force disturbance, a yaw moment disturbance acting upon a vehicle, and a measurement bias corrupting a measured yaw rate. The system detects anomalies in MPC reference trajectory and adjusts actuator constraints in response to detected anomalies. The system actively and continuously adjusts actuator outputs, causing the vehicle to track and follow a current lane center, and on receiving a lane change command, smoothly changes lanes using vehicle actuators to alter position and alter a vehicle trajectory to enter an adjacent lane before returning to tracking and following a center of the adjacent lane.

A hydroplaning detection system includes a processor in communication with a communication system and a warning module in communication with the processor. The warning module receives data from vehicle-based sensors and generates an expected slip, a grip utilization, a drag force, and a calculated slip. The warning module receives data from a tire sensor unit and determines a confidence level by comparing the generated drag force to an expected drag. The warning module includes a hydroplaning detection logic that receives the expected slip, the grip utilization, the drag force, the calculated slip, and the confidence level and generates a hydroplaning detection warning and a warning confidence level. The warning module communicates the warning and the warning confidence level to at least one of a vehicle control system and an operator of the vehicle for action by the vehicle control system or the vehicle operator.

The invention relates to the field of computer vision, in particular to an intelligent parking system based on a computer vision algorithm, which is provided with a data acquisition module, a resource integration module, an anomaly analysis module, a parking matching module and a parking guide module, determines a vehicle running value and an actual parking effective value, calculates a parking permission characterization value, and displays the parking permission characterization value. Determining whether a parking position for a target vehicle exists in the parking area; determining a target parking area; dividing the target parking area into a plurality of sub-areas; analyzing data information corresponding to the target parking area; and parking guidance is carried out according to the judgment result of the preset condition, and intelligent parking is completed. According to the invention, parking guidance is carried out by determining the target parking area, analyzing the corresponding abnormal data and comparing with the parking related area of the target vehicle, so that the smoothness of the parking path and the parking efficiency are improved.

The proposed invention relates to methods for controlling energy consumption by a motor vehicle and can be used in transportation industry. The technical problem to be solved by the claimed invention is to provide a method, a device and a system that do not possess the drawbacks of the prior art and thus make it possible to generate an accurate energy-efficient track for a motor vehicle that allows to reduce energy consumption by the motor vehicle on the specific portion of the route. The objective of the claimed invention is to overcome the drawbacks of the prior art and thus to reduce energy consumption by the motor vehicle on the specific portion of the route.

A system for managing emissions of a vehicle includes a controller to perform: activating a heater responsive to a battery state of charge being at or above a predefined threshold, activating an air mover, causing the moving air to be heated to heat an aftertreatment system, receiving an aftertreatment system temperature responsive to activating the air mover, causing the engine to operate in a modified power mode where a power output of the engine is below a power output threshold, responsive to the temperature being at or above a first threshold and below a second threshold, causing the engine to operate the engine in a normal operating mode where the power output of the engine is at or above the power output threshold, responsive to the temperature being at or above the second threshold, and deactivating the heater when the temperature is at or above the second threshold.

An autonomous vehicle can include a tractor; a trailer coupled with the tractor; a thermal sensor coupled with the trailer, the thermal sensor configured to measure a temperature of the trailer; and one or more processors. The processors can be configured to receive a plurality of temperature measurements of the trailer from the thermal sensor while the autonomous vehicle is driving; compare the plurality of temperature measurements to a condition; and automatically execute a trailer anomaly response protocol responsive to determining at least one of the plurality of temperature measurements satisfies the condition.

Methods and systems are described herein for predicting collisions between uncrewed vehicles and preventing those collisions. A collision prevention system may generate collision spheroids for a multitude of uncrewed vehicles. Those collision spheroids may represent a plurality of three-dimensional points around each uncrewed vehicle. When the collision prevention system determines that collision spheroids corresponding to two uncrewed vehicles intersect, the collision prevention system may determine that a collision is imminent and may attempt to prevent the collision by causing one or both uncrewed vehicles to change velocity.

The present disclosure relates to a vehicle control method, a vehicle control device, and a vehicle control system including same. Specifically, a vehicle control device according to the present disclosure includes: a vehicle travel route estimation unit which estimates a first travel route of a vehicle on the basis of the turn radius and traveled distance of the vehicle, when the speed of the vehicle is equal to or less than a preset speed; a target travel route predicter which confirms a target from periphery detection information about the periphery of the vehicle, and predicts a second travel route of the target; a collision determiner which determines whether there is a possibility of a collision between the vehicle and the target; and a vehicle control unit which controls to perform at least one among an alert control, a brake control, and an avoidance control, when there is a possibility of a collision between the vehicle and the target.

A method for approximating a friction value includes: determining a load characteristic; determining a setpoint variable of the vehicle; determining a manipulated variable expected value specifying a predicted value of a manipulated variable to be provided to set the setpoint variable, wherein the determination of the manipulated variable expected value is performed using the load characteristic; determining an actual variable corresponding to the setpoint variable; determining a manipulated variable actual value, which is provided at the steering system, in order to modulate the actual variable; determining a manipulated variable deviation between the manipulated variable expected value and the manipulated variable actual value; and / or determining a setpoint-actual deviation between the setpoint variable and the corresponding actual variable; approximating the friction value based on the determined load characteristic and based on the determined manipulated variable deviation and / or the determined setpoint-actual deviation. A driver assistance system is configured to carry out the method.