A method and an apparatus for generating sensor data for controlling an autonomous vehicle in an environment is provided, such as driverless transport vehicles in a factory for example. Sensor positions of static sensors and the sensors of autonomous vehicles are defined in a global coordinate system on the basis of an environment model, such as a BIM model for example. Sensor data is centrally generated in this global coordinate system for all sensors as a function of these sensor positions. The sensor data is then transformed into a local coordinate system of an autonomous vehicle and transferred for controlling the autonomous vehicle.

An advanced driver assistance system configured to implement one or more automotive V2V applications designed to assist a driver in driving a Host Motor-Vehicle. The advanced driver assistance system is configured to be connectable to an automotive on-board communication network to communicate with automotive on-board systems to implement one or different automotive functionalities aimed at assisting the driver in driving the Host Motor-Vehicle, controlling the Host Motor-Vehicle, and informing the driver of the Host Motor-Vehicle of the presence of Relevant Motor-Vehicles deemed to be relevant to the driving safety of the Host Motor-Vehicle.

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.

A determining method for determining a state of a road surface includes: sequentially acquiring a rotational speed of tires mounted on the vehicle, sequentially acquiring a driving force of the vehicle, calculating a slip ratio based on the sequentially acquired rotational speed of the tires, calculating a regression equation and a confidence interval width for a relationship between the slip ratio and the driving force, based on data sets of the slip ratio and the driving force in a predetermined zone, and determining a state of the road surface on which the vehicle travels, based on the confidence interval width calculated for the predetermined zone.

Simulated degraded sensor data may be generated for use in training a model. For instance, first sensor data collected by a sensor of a perception system of an autonomous vehicle may be received and converted into the simulated degraded sensor data for a particular degrading condition, such as a weather-related degrading condition. Then, the simulated degraded sensor data may be used to train a model for evaluating performance of the perception system to detect objects external to the autonomous vehicle under one or more conditions.

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.

Systems and methods for detecting a traffic event include receiving vehicle data from a plurality of vehicles travelling along a roadway. The method also includes where the roadway is discretized at a cell level into a plurality of cells. The method also includes identifying vehicles of interest that are within a communication range of a host vehicle. Each of the vehicles of interest are associated with a cell of interest from the plurality of cells. Further, the method includes for each cell of interest at each timestep according to a measurement update interval, calculating an average deceleration based on vehicle data transmitted from each of the vehicles of interest associated with the cell of interest. Additionally, the method includes detecting the traffic event based on the average deceleration for each cell of interest at each timestep.

A method of controlling autonomous driving is provided. The method includes collecting driving information of a driver and curvature information of a road and generating a driving pattern of the driver, defined by associating behaviors in longitudinal and lateral directions of the vehicle based on the driving information. The driving pattern is then set to a constraint condition for driving torque and brake pressure and the vehicle is operated based thereon.

A method for performing an on-board diagnostic function in a motor vehicle including at least one drive system, at least one operating element and at least one control unit with a processor, a control module and an on-board diagnostic function module. The method includes the steps of: activating an on-board diagnostic function in the on-board diagnostic function module; supplying a first signal profile (S1) of an operating value (CV) of the operating element to an analysis module; the analysis module analyzing the first signal profile (S1) of the operating value (CV); activating a filtering module in the event of a positive analysis result; and the filtering module filtering the first signal profile (S1) of the operating value (CV) with selected damping parameters (DP) upon activation of the filtering module in order to obtain a filtered second signal profile (S2) of the operating element.

A method for controlling operation of a motor vehicle includes an electronic controller receiving, e.g., from a vehicle-mounted sensor array, sensor data with dynamics information for a target vehicle and, using the received sensor data, predicting a lane assignment for the target vehicle on a road segment proximate the host vehicle. The electronic controller also receives map data with roadway information for the road segment; the controller fuses the sensor and map data to construct a polynomial overlay for a host lane of the road segment across which travels the host vehicle. A piecewise linearized road map of the host lane is constructed and combined with the predicted lane assignment and polynomial overlay to calculate a lane assignment for the target vehicle. The controller then transmits one or more command signals to a resident vehicle system to execute one or more control operations using the target vehicle's calculated lane assignment.