A dynamic velocity planning method for an autonomous vehicle is performed to plan a best velocity curve of the autonomous vehicle. An information storing step is performed to store an obstacle information, a road information and a vehicle information. An acceleration limit calculating step is performed to calculate the vehicle information according to a calculating procedure to generate an acceleration limit value range. An acceleration combination generating step is performed to generate a plurality of acceleration combinations according to the obstacle information, the road information, and the acceleration limit value range. An acceleration filtering step is performed to filter the acceleration combinations according to a jerk threshold and a jerk switching frequency threshold to obtain a selected acceleration combination. An acceleration smoothing step is performed to execute a driving behavior procedure to adjust the selected acceleration combination to generate the best velocity curve.

Systems, methods, and devices are disclosed for predicting behaviors of objects (vehicles, bicycles, pedestrians, etc.) at a location. A model descriptive of a possible object behavior can be received by an autonomous vehicle, where the model provides conditional predictions about a future behavior of an object based on a position of the object in a lane. The autonomous vehicle can detect the position of a specific object in the lane, and the model can then be applied to determine probabilities of a future behavior of the specific object.

Systems and methods for estimating values of dynamic attributes of autonomous vehicles are disclosed. A first vehicle includes an inertial measurement unit (IMU) configured to measure a dynamic attribute (e.g., rate of change of vehicle yaw angle) and correlate the measured attribute with one or more input variables (e.g., values of steering angle commands). The correlated data is used to generate a model that can be used in a second vehicle to predict a dynamic attribute based at least in part on variable values input from the second vehicle. As a result, it is not necessary for the second vehicle to have an IMU.

A method, system and computer-usable medium are disclosed for autonomous vehicle (AV) behavior synchronization. The AV driving pattern is adjusted to facilitate an occupant's satisfaction by receiving information as to person to form a driving history. The driving history is analyzed to identify preferences and patterns. Based on the driving history a driving preference model is formed for the person. AV driving algorithm(s) are adjusted based on the driving preference model for the person when the person is an occupant of the AV.

Systems and methods for determining an estimated weight of a vehicle are provided. The system includes at least one data storage and at least one processor. The at least one data storage is configured to store vehicle data associated with the vehicle. The at least one processor is configured to: identify a plurality of vehicle maneuvers based on the vehicle data, each vehicle maneuver being associated with a portion of the vehicle data, each portion of the vehicle data comprising a measured torque profile; generate a plurality of simulated torque profiles for each vehicle maneuver; generate a plurality of error profiles, an error profile being generated for each vehicle maneuver based on differences between the plurality of simulated torque profiles and the measured torque profile corresponding to that vehicle maneuver; and determine the estimated weight of the vehicle based on the plurality of error profiles.

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 a vehicle control device capable of improving fuel consumption while reducing deterioration of emission by appropriately controlling a powertrain system of a vehicle. A vehicle control device includes: a prediction unit configured to predict speeds or accelerations of a vehicle based on a plurality of prediction models; a fuel consumption information calculation unit configured to calculate fuel consumption for each of a plurality of prediction results obtained by the prediction unit; a selection unit configured to select any one of the plurality of prediction results; and a powertrain control unit configured to control at least one of an engine, a generator, an inverter, a drive motor, and a transmission of the vehicle based on the prediction result selected by the selection unit.

In a target identification device, an acquisition unit is configured to acquire trajectory information including information on a movement trajectory of a moving object in the surroundings of a vehicle. A calculation unit is configured to calculate a likelihood for each type of moving object from the trajectory information by using a plurality of models predefined for each type of moving object. A target identification unit is configured to identify the type of the moving object according to the likelihood calculated by the calculation unit.

A driving assistance device executes processing relating to a behavior model of a vehicle. Detected information from the vehicle is input to a detected information inputter. An acquirer derives at least one of a travel difficulty level of a vehicle, a wakefulness level of a driver, and a driving proficiency level of the driver on the basis of the detected information that is input to the detected information inputter. A determiner determines whether or not to execute processing on the basis of at least one information item derived by the acquirer. If the determiner has made a determination to execute the processing, a processor executes the processing relating to the behavior model. It is assumed that the processor does not execute the processing relating to the behavior model if the determiner has made a determination to not execute the processing.

A computer for managing the drive train of a hybrid vehicle including an internal combustion engine, an electric machine and a battery. The drive train being capable of operating in a plurality of charging or discharging modes of the battery, the computer determines a set of probabilities of activation of the mode, determines the value of the speed of the electric motor for each mode, determines a set of electrical powers of the electric machine, calculates an energy consumption reduction indicator, determines the torque requested by the driver, the value of the speed of the internal combustion engine and the speed of the vehicle, determines a torque to be applied to the electric machine, and sends a command to the electric machine on the basis of the torque to be applied to the electric machine determined.