A controller and a method for controlling motion of a vehicle is provided. The method comprises acquiring motion information including a current state of the vehicle and a desired state of the vehicle, determining a combination of a steering angle of the wheels and motor forces for moving the vehicle from the current state into the desired state by using a first model of the motion of the vehicle and a second model of the motion of the chassis of the vehicle, determining a cost function of the motion of the vehicle, optimizing the cost function of the motion of the vehicle to compute a command signal for controlling the steering wheel and the plurality of electric motors, and controlling the steering angle of the wheels and the motor forces based on the command signal.

A method may include obtaining input information that describes a driving operation of a vehicle and obtaining a rule that indicates an approved driving operation of the vehicle. The method may include parsing the rule using a domain-specific language to generate rule conditions in which the domain-specific language is a programming language that is specifically designed for analyzing driving operations of vehicles. The method may include representing the input information as observations relating to the vehicle in which each of the observations is comparable to one or more of the rules. The method may include comparing the observations to one or more respective comparable rule conditions and generating a grading summary that evaluates how well the observations satisfy the respective comparable rule conditions based on the comparison. A future driving operation of the vehicle may be adjusted based on the grading summary.

In one embodiment, an exemplary method includes receiving, at a simulation platform, a record file recorded by a manually-driving ADV on a road segment, the simulation platform including a first encoder, a second encoder, and a performance evaluator; simulating automatic driving operations of a dynamic model of the ADV on the road segment based on the record file, the dynamic model including an autonomous driving module to be evaluated. The method further includes: for each trajectory generated by the autonomous driving module during the simulation: extracting a corresponding trajectory associated with the manually-driving ADV from the record file, encoding the trajectory into a first semantic map and the corresponding trajectory into a second semantic map, and generating a similarity score based on the first semantic map and the second semantic map. The method also includes generating an overall performance score based on each similarity score.

A parallel computing method for man-machine coordinated steering control of a smart vehicle based on risk assessment is provided, comprising the following steps: building a lateral kinetic equation model of a vehicle; building a target function by targeting at minimizing an offset distance of a vehicle driving track from a lane center line and making a change in a front wheel steering angle and a longitudinal acceleration as small as possible in a driving process; building a parallel computing architecture of a prediction model and the target function, and employing a triggering parallel computing method; solving and computing a gradient with a manner of back propagation and using a gradient descent method to obtain an optimal control amount of the front wheel steering angle and an optimal control amount of the longitudinal acceleration; and computing a driving weight, obtaining a desired front wheel steering angle and completing real time control.

A computer-implemented method for training a machine learning system to generate driving profiles of a vehicle. The method includes first travel routes are selected from a first database having travel routes, a generator of the machine learning system receives the first travel routes and generates first driving profiles for each of the first travel routes, travel routes and associated driving profiles determined during vehicle operation are stored in a second database, second travel routes and respective associated second driving profiles determined during vehicle operation are selected from the second database, a discriminator of the machine learning system receives pairs made up of one of the first travel routes with the respective associated first generated driving profile and pairs made up of second travel routes with the respective associated second driving profile determined during vehicle operation, as input variables.

A method for operating a two-wheeler. The two-wheeler includes a drive unit and a sensor system, the sensor system including a rotation rate sensor, an acceleration sensor, and a wheel speed sensor. The wheel speed sensor detects at least one measuring pulse per revolution of a wheel of the two-wheeler. The method includes: detecting three-dimensional rotation rates of the two-wheeler, detecting acceleration values of the two-wheeler, and estimating a motion state of the two-wheeler based on the detected rotation rates, the motion state including estimated values for estimated acceleration values and an estimated speed and an estimated distance covered, first correction of the estimated motion state based on the detected acceleration values, ascertaining an instantaneous steering angle of the two-wheeler based on the corrected estimated motion state, and actuating the drive unit and/or an antilocking system of the two-wheeler as a function of the ascertained instantaneous steering angle.

A method for determining the payload mass resting on a wheel of a vehicle. In the method: using a level sensor system, in a time period in which the vehicle is moved, a time series of measured values is detected, which each indicate the vertical position of the vehicle body in relation to the wheel; a model is provided for the temporal development of the vertical position under the influence of the gravitational force of vehicle body and payload, an elastic suspension between the vehicle body and the wheel of the vehicle, and a damping of the vertical relative movement between the vehicle body and the wheel of the vehicle, the model being parameterized at least using the sought payload mass and the wheel of the vehicle and the connection of the wheel to the roadway being assumed to be rigid.

Techniques for controlling a vehicle based on a collision avoidance algorithm are discussed herein. The vehicle receives sensor data and can determine that the sensor data represents an object in an environment through which the vehicle is travelling. A computing device associated with the vehicle determines a collision probability between the vehicle and the object at predicted locations of the vehicle and object at a first time. Updated locations of the vehicle and object can be determined, and a second collision probability can be determined. The vehicle is controlled based at least in part on the collision probabilities.

The present disclosure provides example autonomous driving apparatuses and computer program products. One example apparatus includes receiving vehicle attribute information and traveling information of a target vehicle from the target vehicle. Layer information of a first road section on which the target vehicle travels is obtained from an autonomous-driving-policy-layer based on the traveling information. A first autonomous driving policy for the target vehicle is obtained based on the layer information of the first road section and the vehicle attribute information of the target vehicle. The first driving policy is sent to the target vehicle.

The disclosure relates to a method for operating a hybrid electric vehicle. According to the method, a route information is received in the form of a plurality of parameter sets, each parameter set relating to a segment of a route (S1). Subsequently, a power demand is estimated for each segment (S3) and a portion of an amount of energy being stored in the electric storage device is allocated to at least one of the segments. Alternatively or additionally, an amount of energy to be transferred into the electric storage device is allocated to at least one of the segments (S4). Additionally, at least one reference trajectory describing a state-of-energy of the electric storage device over the route resulting from the energy allocation is derived (S5). The operation of the hybrid electric vehicle is controlled as a function of a slope between a current state-of-energy and an upcoming control point on the reference trajectory (S7). Moreover, a data processing device comprising means for carrying out the method is presented.