A method detects unintended acceleration of a motor vehicle during a closed-loop speed control mode by determining external forces on the vehicle via a controller, and then calculating a desired acceleration using a measured vehicle speed and the external forces. The method includes determining an actual acceleration of the vehicle, including filtering a speed signal as a first actual acceleration value and/or measuring a second actual acceleration value using an inertial measurement unit (IMU). During the speed control mode, the method includes calculating an acceleration delta value as a difference between the desired acceleration and the actual acceleration, and then using the acceleration delta value to detect the unintended acceleration during the speed control mode. A powertrain system for the motor vehicle, e.g., an electric vehicle, includes the controller and one or more torque generating devices coupled to road wheels of the vehicle.

The present disclosure provides a system and a method for parking an autonomous ego-vehicle in a dynamic environment of a parking area. The method includes collecting measurements of a state of the dynamic environment, a state of one or multiple stationary vehicles and one or multiple obstacle vehicles moving in the parking area. The method further includes executing a path planner configured to produce a trajectory based on the state of the dynamic environment and executing an environment predictor configured to predict a path and a mode of motion for each of the obstacle vehicles. The method further includes determining a safety constraint for each of the obstacle vehicles based on the path and the mode of motion for each of the obstacle vehicles and parking the autonomous ego-vehicle based on the trajectory for parking and the safety constraint for each of the obstacle vehicles.

To reduce the man-hours of software development when vehicle types are deployed, an in-vehicle equipment control device has a control unit that outputs, to actuators, control signals based on the outputs from sensor devices. The control unit includes a middleware layer and a device driver layer as software components. The middleware layer includes a routing module that selects whether the communication data output from the sensor devices is output as is or the communication data is subjected to predetermined processing and then output according to the type of the communication data, and a treatment module that performs the predetermined processing on the communication data. The routing module has a function of outputting the communication data to the device driver.

A tracking system for tracking an expanded state of an object is provided. The tracking system executes, for a predetermined time period, a probabilistic filter that iteratively tracks a belief on the expanded state of the object, wherein the belief is predicted using a motion model of the object and is further updated using a compound measurement model of the object. After the predetermined time period, the updated beliefs are smoothed to generate a state-decoupled online batch of training data. The compound measurement model includes multiple probabilistic distributions constrained to lie around a contour of the object with a predetermined relative geometrical mapping to the center of the object. The compound measurement model is updated using the online batch of training data. Further, the tracking system tracks the expanded state of the object based on the updated compound measurement model.

A vehicle includes a powertrain, an inertial measurement unit configured to measure inertial forces exerted onto the vehicle, and a controller. The controller is programmed to control the torque at the powertrain based on a mapped relationship between the inertial forces and a vehicle velocity, wherein the mapped relationship utilizes at least one mapping parameter. The controller is further programmed to estimate a mass of the vehicle based on the mapping parameter.

The present invention relates to determination of a state of a vehicle on a road portion. The vehicle includes an Automated Driving System (ADS) feature. At first, map data associated with the road portion, positioning data indicating a pose of the vehicle on the road, and sensor data of the vehicle are obtained. Then, a plurality of filters for the road portion are initialized. Further, one or more sensor data point(s) in the obtained sensor data is associated to a corresponding map-element of the obtained map data to determine one or more normalized similarity score(s). Now, based on the determined one or more normalized similarity score(s), one or more multivariate time-series data are also determined and provided as input to a trained machine-learning algorithm. Then, one of the initialized filters is selected by the machine learning algorithm to indicate a current state of the vehicle on the road portion.

A method calibrates and validates a driver assistance system (ADAS) and/or an automated driving system (ADS) for a driving task in at least one scenario. The scenario represents a traffic event in a time sequence and is defined by selected parameters and associated parameter values. The method includes: creating first test cases by selecting scenarios, scenario parameters and calibration parameters using a test strategy for the driving task. The method proceeds by performing a simulation to determine simulation results; evaluating of the simulation results; adapting the test strategy to the evaluation results; creating second test cases using the adapted test strategy; starting a new simulation cycle; repeating the adaptation of the test strategy if an evaluation criterion is not met; or passing on the test cases of the last simulation cycle to an output module; outputting results of the test cases from the output module for calibration and validation.

A vehicle, a vehicle fuel reactant leak detection system, a computer program product, and a computer implemented method of detecting leakage of a fuel reactant from a vehicle. The vehicle includes one or more fuel cell modules, a fuel supply source to supply a fuel reactant to the one or more fuel cell modules via a high-pressure fuel supply line, a fuel supply valve configured to open and close fuel reactant flow through the high-pressure fuel supply line, and a computing device, operatively connected to the fuel supply source. The computing device includes one or more processors caused to conduct, in response to a detection as sensor data of pressure in the high-pressure fuel supply line when the vehicle engine is in a non-operating state, fuel pressure analysis of the sensor data, and detect, based on the fuel pressure analysis, leakage of the fuel reactant at the fuel supply valve.

The invention concerns a method and a system for recognizing the presence of any irregularities of any road pavement.

Herein, a “perception statistical performance model” (PSPM) for modelling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety/performance testing. A first PSPM is configured to: receive a computed perception ground truth; determine from the perception ground truth, based on a set of learned parameters, a probabilistic perception uncertainty distribution, the parameters learned from a set of actual perception outputs generated using the perception slice to be modelled, in order to compute a first time series of perception outputs. A second time series of perception outputs is computed using a second PSPM for modelling a second perception slice of the runtime stack, the first PSPM learned from data of a first sensor modality of the perception slice and the time series, and the second PSPM learned independently thereof from data of a second sensor modality of the second perception slice and the second time series.