Abstract: The present invention provides a real-time path planning method taking into account dynamic properties of vehicles; the method includes a calculation without connecting to internet of a reachable set and an online path planning; in the calculation without connecting to internet of the reachable set, all vehicle safety states are traversed by means of the vehicle model and the wheel model, thereby predicting the position set capable of being reached by the vehicle at a next moment; in the online path planning, by means of calculating the position set capable of being reached by the vehicle at the next moment with connecting to internet, non-linear dynamic constraints are provided for the exploration of the artificial potential field method, achieving the purpose of real-time planning while taking into account dynamic properties of the vehicles.

Abstract: A coordinated control method for electric vehicles having independent four-wheel driving and steering, comprising the steps of: calculating to obtain a desired value of yaw velocity according to the steering angle and the current vehicle driving speed, and limiting the desired value of yaw velocity according to the current road adhesion condition; constructing an optimization problem according to the current vehicle motion state and the desired value of yaw velocity, and solving the optimization problem to obtain a desired active rear wheel steering angle control variable and a desired additional yaw moment control variable; calculating to obtain an additional torque of each wheel according to a desired additional yaw moment control variable, obtaining a desired active rear wheel steering angle, and sending the additional torque of each wheel and the desired active rear wheel steering angle to an executor of the vehicle for performing a coordinated control.

Abstract: Disclosed is a decision-making and planning integrated method for a nonconservative intelligent vehicle in a complex heterogeneous environment, including the following steps: offline establishing and training a social interaction knowledge learning model; obtaining state data of the traffic participants and state data of an intelligent vehicle online in real time, and splicing the state data to obtain an environmental state; using the environmental state as an input to the trained social interaction knowledge learning model to obtain predicted trajectories of all traffic participants including the nonconservative intelligent vehicle; updating the environmental state based on the predicted trajectories; and inputting the updated environmental state to the social interaction knowledge learning model to complete trajectory decision-making and planning for the nonconservative intelligent vehicle by iteration, where a planned trajectory of the nonconservative intelligent vehicle is a splicing result of a first poi

Abstract: Disclosed is a decision-making and planning integrated method for a nonconservative intelligent vehicle in a complex heterogeneous environment, including the following steps: offline establishing and training a social interaction knowledge learning model; obtaining state data of the traffic participants and state data of an intelligent vehicle online in real time, and splicing the state data to obtain an environmental state; using the environmental state as an input to the trained social interaction knowledge learning model to obtain predicted trajectories of all traffic participants including the nonconservative intelligent vehicle; updating the environmental state based on the predicted trajectories; and inputting the updated environmental state to the social interaction knowledge learning model to complete trajectory decision-making and planning for the nonconservative intelligent vehicle by iteration, where a planned trajectory of the nonconservative intelligent vehicle is a splicing result of a first poi

Abstract: Disclosed is a method for predicting a trajectory of a traffic participant in a complex heterogeneous environment, including the following steps: obtaining traffic participant information in a complex heterogeneous environment; arranging and numbering traffic participant classes based on the class information, to obtain serial numbers of the traffic participant classes; establishing a position graph, a velocity graph, an acceleration graph, and a class graph, into each of which expert experience is introduced; and capturing topological structure relationships and time dependence relationships to obtain a position hidden state, a velocity hidden state, an acceleration hidden state, and a class hidden state; classifying the position hidden state, the velocity hidden state, the acceleration hidden state, and the class hidden state to obtain a hidden state set of traffic participants; and decoding hidden states of the traffic participants separately using a corresponding decoder to obtain future trajectory predic

Abstract: The present invention provides a real-time path planning method taking into account dynamic properties of vehicles; the method includes calculation without connecting to internet of a reachable set and an online path planning; in the calculation without connecting to internet of the reachable set, all vehicle safety states are traversed by means of the vehicle model and the wheel model, thereby predicting the position set capable of being reached by the vehicle at a next moment; in the online path planning, by means of calculating the position set capable of being reached by the vehicle at the next moment with connecting to internet, non-linear dynamic constraints are provided for the exploration of the artificial potential field method, achieving the purpose of real-time planning while taking into account dynamic properties of the vehicles.

Abstract: Disclosed is a method for predicting a trajectory of a traffic participant in a complex heterogeneous environment, including the following steps: obtaining traffic participant information in a complex heterogeneous environment; arranging and numbering traffic participant classes based on the class information, to obtain serial numbers of the traffic participant classes; establishing a position graph, a velocity graph, an acceleration graph, and a class graph, into each of which expert experience is introduced; and capturing topological structure relationships and time dependence relationships to obtain a position hidden state, a velocity hidden state, an acceleration hidden state, and a class hidden state; classifying the position hidden state, the velocity hidden state, the acceleration hidden state, and the class hidden state to obtain a hidden state set of traffic participants; and decoding hidden states of the traffic participants separately using a corresponding decoder to obtain future trajectory predic

Abstract: A vehicle state estimation method based on adaptive total variation denoising (TVD) filtering includes the following steps: step 1: collection and preprocessing of an original signal of a vehicle; step 2: noise level evaluation; step 3: Teager-Kaiser energy evaluation; step 4: optimization problem construction; and step 5: application of a filtered signal in the step 4 in the estimation of a vehicle state. The vehicle state estimation method is mainly based on the global noise level characteristic and the local intensity change characteristic of the vehicle system state data, and adaptive filtering of parameters is achieved by means of a TVD filtering method. The signal is denoised to the maximum extent, peak information of the signal is retained while the data smoothness is maintained, and then the signal is used for vehicle state estimation, working condition identification and the like.

Abstract: A vehicle state estimation method based on adaptive total variation denoising (TVD) filtering includes the following steps: step 1: collection and preprocessing of an original signal of a vehicle; step 2: noise level evaluation, step 3: Teager-Kaiser energy evaluation; step 4: optimization problem construction; and step 5: application of a filtered signal in the step 4 in the estimation of a vehicle state. The vehicle state estimation method is mainly based on the global noise level characteristic and the local intensity change characteristic of the vehicle system state data, and adaptive filtering of parameters is achieved by means of a TVD filtering method. The signal is denoised to the maximum extent, peak information of the signal is retained while the data smoothness is maintained, and then the signal is used for vehicle state estimation, working condition identification and the like.

Abstract: Disclosed is a steering wheel torque feedback optimization control method for a differential braking system, including: estimating a tire lateral force of a vehicle; predicting a state of the vehicle at a next moment under a non-differential torque condition; predicting a state of the vehicle at the next moment under a differential torque condition with an additional yaw moment; calculating tire lateral forces at the next moment under the differential torque condition and the non-differential torque condition separately based on the predicted states of the vehicle at the next moment under the differential torque condition and the non-differential torque condition, and obtaining a predicted one-step change value of a front tire lateral force by performing subtraction; performing integral calculation based on time, and obtaining a continuous change amount of the tire lateral force; and correcting a desired moment of a steering power motor.

Abstract: A coordinated control method for electric vehicles having independent four-wheel driving and steering, comprising the steps of: calculating to obtain a desired value of yaw velocity according to the steering angle and the current vehicle driving speed, and limiting the desired value of yaw velocity according to the current road adhesion condition; constructing an optimization problem according to the current vehicle motion state and the desired value of yaw velocity, and solving the optimization problem to obtain a desired active rear wheel steering angle control variable and a desired additional yaw moment control variable; calculating to obtain an additional torque of each wheel according to a desired additional yaw moment control variable, obtaining a desired active rear wheel steering angle, and sending the additional torque of each wheel and the desired active rear wheel steering angle to an executor of the vehicle for performing a coordinated control.

Abstract: A longitudinal and lateral vehicle motion cooperative control method based on a fast solving algorithm comprises the following steps: calculating a desired yaw rate according to a steering wheel rotation angle and a current vehicle traveling speed; constructing a nonlinear optimization problem according to the desired yaw rate and a current actual motion state of a vehicle, wherein an objective function of the nonlinear optimization problem is used for tracking the desired yaw rate, and simultaneously for restraining a lateral speed and a tire slip ratio of the vehicle; solving the nonlinear optimization problem to calculate desired slip ratios of four tires; calculating an additional torque of each tire according to an actual slip ratio and the desired slip ratio of the tire; and sending the additional torque of each tire to an actuator of the vehicle for cooperative control.

Abstract: This application discloses a method and an apparatus for detecting a complexity of a traveling scenario of a vehicle, comprising: obtaining a travelling speed of the vehicle and a travelling speed of a target vehicle; determining, based on the traveling speed of the vehicle and the traveling speed of the target vehicle, a dynamic complexity of a traveling scenario in which the vehicle is located; determining static information of each static factor in the traveling scenario in which the vehicle is currently located; obtaining, based on the static information of each static factor, a static complexity of the traveling scenario in which the vehicle is located; and obtaining, based on the dynamic complexity and the static complexity, a comprehensive complexity of the traveling scenario in which the vehicle is located.