Methods and systems for jointly estimating a pose and a shape of an object perceived by an autonomous vehicle are described. The system includes data and program code collectively defining a neural network which has been trained to jointly estimate a pose and a shape of a plurality of objects from incomplete point cloud data. The neural network includes a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network. The method includes receiving an incomplete point cloud representation of an object, inputting the point cloud data into the trained shared encoder, outputting a code representative of the point cloud data. The method also includes generating an estimated pose and shape of the object based on the code. The pose includes at least a heading or a translation and the shape includes a denser point cloud representation of the object.

A method for adjusting a safety distance between a first vehicle and a second vehicle preceding the first vehicle at a bunched traffic site by at least one control device is disclosed which includes receiving measurement data of at least one sensor unit with the at least one control device. The method includes evaluating the measurement data of the at least one sensor unit with the at least one control device to identify a bunched traffic site in front of the second vehicle and identifying a safety distance of the first vehicle from the second vehicle, wherein the identified safety distance accounts for a possible reverse motion of the second vehicle. The method further includes establishing the identified safety distance with the at least one control device based upon the identified bunched traffic site. A control device, a computer program and a machine-readable storage medium are further disclosed.

An apparatus, method and computer program product are provided for determining a state of visibility for a road object. In one example, the apparatus receives temporal data, calculates an orientation of a light source with respect to a road object using the temporal data, and predicts a state of visibility for the road object based on the orientation of the light source. In another example, the apparatus determines an artificial light source associated with a road object, receives attribute data associated with the artificial light source, determines a state of the artificial light source using the attribute data, and predicts a state of visibility for the road object based on the state of the artificial light source.

Systems for collision avoidance for a vehicle. One or more inputs are used to determine an impending collision. Once determined, corrective actions are taken to reduce the severity of the collision. The corrective actions can avoid the collision and/or reduce the damage caused by the collision. The systems and methods can be performed at the vehicle based on data available to a control unit in the vehicle. The systems and methods can also be performed at a system level that controls one or more vehicles and/or objects.

A method for operating an at least partially automated vehicle. The method includes: supplying surroundings data detected with the aid of sensors to at least three AI computing units which are independent of one another; generating data regarding at least one object from the surroundings data; carrying out a plausibility check of the pieces of data with respect to one another with the aid of majority voting; and using the data for which the plausibility check has been carried out for controlling, at least in a semi-automated manner, a lateral and/or longitudinal guidance of the at least partially automated vehicle.

Aspects and implementations of the present disclosure relate to modeling of positional uncertainty of moving objects using precomputed polygons, for example, for the purposes of computing autonomous vehicle (AV) trajectories. An example method includes: receiving, by a data processing system of an AV, data descriptive of an agent state of an object; generating a polygon representative of the agent state; identifying extreme vertices of the polygon along a longitudinal axis parallel to a heading direction of the object or along a lateral axis orthogonal to the heading direction; and applying, based on the extreme vertices, at least one expansion transformation to the polygon along the longitudinal axis or the lateral axis to generate a precomputed polygon.

The present application discloses a method and an apparatus for autonomous driving control, an electronic device, and a storage medium; the application relates to the technical field of autonomous driving. A specific implementation solution is: obtaining movement data of a pedestrian, where the movement data includes a velocity component of the pedestrian along a width direction of a lane and a time of duration that the pedestrian cuts into a driving path of the autonomous vehicle from one side; determining a movement direction of the pedestrian according to the movement data and the movement information of the pedestrian; and generating a driving strategy for the autonomous vehicle according to the movement direction of the pedestrian. Therefore, the movement direction of the pedestrian can be accurately predicted, which facilitates the autonomous vehicle to avoid the pedestrian and insures driving safety.

An apparatus for controlling a vehicle includes an object selection device configured to select an object intersecting the vehicle at an intersection existing on a driving path of the vehicle, a risk determination device configured to determine a risk during driving of the vehicle based on a predicted path of the object, and a driving control device configured to determine a driving method of the vehicle based on a risk determination result.

A control apparatus for a vehicle includes a skill level acquiring unit and an inter-vehicle distance controller. The skill level acquiring unit is configured to acquire a driving skill level of a driver of a first vehicle other than a second vehicle. The second vehicle is an own vehicle. The inter-vehicle distance controller is configured to control an inter-vehicle distance from the first vehicle to the second vehicle on the basis of the driving skill level acquired by the skill level acquiring unit.

A control system of a vehicle that can travel in a first state in which travel control is performed based on a position of a white line on a travel lane and in a second state in which travel control is performed based on a travel position of another vehicle. Periphery information is obtained of the vehicle. It is determined, based on the periphery information obtained, whether an emergency vehicle is approaching. A control unit configured to perform control so that travel control in the first state is prioritized when it is determined that the emergency vehicle is not approaching. Travel control in the second state is prioritized when it is determined that the emergency vehicle is approaching.