The present invention discloses an intelligent vehicle platoon lane change performance evaluation method. First, an intelligent vehicle platoon lane change performance test scenario is established; secondly, a three-degree of freedom nonlinear dynamics model is established according to motion characteristics of intelligent vehicles in a platoon lane change process; further, an improved adaptive unscented Kalman filter algorithm is utilized to perform filter estimation on state variables of positions and velocities of platoon vehicles; and finally, based on accurately recursive vehicle motion state parameters, evaluation indexes for platoon lane change performance are proposed and quantified, and an evaluation system for platoon lane change performance is constructed. According to the method proposed in the present invention, the problem of lacking platoon lane change performance quantitative evaluation at present is solved, vehicle motion state parameters can be measured in a high-precision and comprehensive manner, multi-dimensional platoon lane change performance evaluation indexes are quantified and output, and comprehensive, accurate, and reliable scientific quantitative evaluation for platoon lane change performance is achieved.

The present invention provides an iterative joint estimation method of vehicle mass and road gradient based on MMRLS and SH-STF, which includes the following steps: establishment of a dynamic model considering steering, MMRLS/SH-STF iterative joint estimation algorithm architecture, improved slope estimation algorithm based on SH-STF. It is an iterative joint estimation method of vehicle mass and road slope based on MMRLS and SH-STF, which is designed reasonably, and the slow-variation characteristics of vehicle mass and the time-varying characteristics of road gradient are analyzed. According to the characteristics of gradual change and time change, based on the longitudinal dynamics model of the vehicle and the steering single-track model, the system identification algorithm of multi-model fusion recursive least squares is used to calculate the vehicle mass, and the noise adaptive strong tracking based on extended Kalman filter is used.

The disclosure relates to the process of ascertaining an input variable of a vehicle actuator using a model-based predictive control. According to one exemplary arrangement, a processor unit is designed to access trajectory information and a state data set, which represents a state of surroundings of a vehicle and/or the state of the vehicle and/or a driving state of the vehicle, by an interface. The processor unit carries out a secondary condition algorithm in order to calculate a secondary condition and an MPC algorithm for a model-based predictive control. By carrying out the secondary condition algorithm, a secondary condition is ascertained for the MPC algorithm on the basis of the trajectory information and on the basis of the state data set. By carrying out the MPC algorithm, an input variable is ascertained for an actuator of the vehicle on the basis of the secondary condition. This is carried out in particular such that in a future predicted trajectory, the vehicle follows the specified trajectory with a specified degree of reliability.

A behavior prediction device comprising: a moving object behavior detection unit configured to detect moving object behavior, a behavior prediction model database that stores a behavior prediction model, a behavior prediction calculation unit configured to calculate a behavior prediction of the moving object using the behavior prediction model, a prediction deviation determination unit configured to determine whether a prediction deviation occurs based on the behavior prediction and a detection result of the moving object behavior corresponding to the behavior prediction, a deviation occurrence reason estimation unit configured to estimate a deviation occurrence reason when determination is made that the prediction deviation occurs, and an update necessity determination unit configured to determine a necessity of an update of the behavior prediction model database based on the deviation occurrence reason when the determination is made that the prediction deviation occurs.

Operating an autonomous machine using analysis of machine vibration while it is operational. Accelerometers are used to measure the machines vibrations while it is being operated. If the vibrations exceed a predetermined acceleration a controller adjust the velocity of the machine to prevent/reduce further vibrations.

This specification discloses an obstacle trajectory prediction method and apparatus. In embodiments of the present disclosure, a global interaction feature under joint action of a vehicle and obstacles is determined according to historical status information and current status information of the vehicle, historical status information and current status information of the obstacles, and a future motion trajectory planned by the vehicle; an individual interaction feature of a to-be-predicted obstacle is determined according to the global interaction feature and current status information of the to-be-predicted obstacle; and a future motion trajectory of the to-be-predicted obstacle is predicted through the individual interaction feature and information about an environment around the vehicle.

The present disclosure relates to techniques to implement a vehicle action using a distributed model distributed across the vehicle and a remote node. A local portion of the distributed model at the vehicle may generate a local output model based on vehicle event data collected at the vehicle. The local output model may be sent from the vehicle at a first location to the remote node at a second location. The remote node may generate a remote output model based on the local output model using the remote portion of the distributed model. The vehicle action may be determined based on inspecting a reconstructed version of the vehicle event data included in the remote output model. The determined vehicle action may be implemented at the vehicle. The distributed model may facilitate the transmission of vehicle event data across multiple locations while securing the transmission of personally-identifiable information.

A control device includes a model generation unit that generates a control model that formulates a task to be executed, and a task processing unit that causes the vehicle to execute the task by performing model prediction control using the control model. Assuming that a first state space is a state space of the control model used at an execution time of the first task, and a second state space is a state space of the control model used at an execution time of the second task, the task processing unit starts to cause the vehicle to execute the second task, after executing a transition process that is a process of making a value of a state variable that is commonly included in both the first state space and the second state space within a predetermined range that is allowed at the execution time of the second task.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that determine yield behavior for an autonomous vehicle. An agent that is in a vicinity of an autonomous vehicle can be identified. An obtained crossing intent prediction characterizes a predicted likelihood that the agent intends to cross a roadway during a future time period. First features of the agent and of the autonomous vehicle are obtained. An input that includes the first features and the crossing intent prediction is processed using a machine learning model to generate an intent yielding score that represents a likelihood that the autonomous vehicle should perform a yielding behavior due to the intent of the agent to cross the roadway. From at least the intent yielding score, an intent yield behavior signal is determined and indicates whether the autonomous vehicle should perform the yielding behavior prior to reaching the first crossing region.

In various examples, an end-to-end perception evaluation system for autonomous and semi-autonomous machine applications may be implemented to evaluate how the accuracy or precision of outputs of machine learning models—such as deep neural networks (DNNs)—impact downstream performance of the machine when relied upon. For example, decisions computed by the system using ground truth output types may be compared to decisions computed by the system using the perception outputs. As a result, discrepancies in downstream decision making of the system between the ground truth information and the perception information may be evaluated to either aid in updating or retraining of the machine learning model or aid in generating more accurate or precise ground truth information.