An example operation includes one or more of determining a characteristic of an occupant in a transport and a current driving environment of the transport, wherein the characteristic includes a position of the occupant, determining that the current driving environment will lead to a collision of the transport, and based on a predicted result of the collision, sending a predicted state of the occupant based on the position, to an emergency service node.

A vehicle computing system may implement techniques to predict behavior of objects detected by a vehicle operating in the environment. The techniques may include determining a feature with respect to a detected objects (e.g., likelihood that the detected object will impact operation of the vehicle) and/or a location of the vehicle and determining based on the feature a model to use to predict behavior (e.g., estimated states) of proximate objects (e.g., the detected object). The model may be configured to use one or more algorithms, classifiers, and/or computational resources to predict the behavior. Different models may be used to predict behavior of different objects and/or regions in the environment. Each model may receive sensor data as an input, and output predicted behavior for the detected object. Based on the predicted behavior of the object, a vehicle computing system may control operation of the vehicle.

A vehicle may receive sensor data captured by a sensor of the vehicle, determine that the sensor data represents an object in the environment, and determine an impact location between the vehicle and the object. The impact location may be associated with a predicted collision between the vehicle and the object. The vehicle may also determine an object type corresponding to the object and/or a characteristic of the object. Based at least in part on the impact location, object type, and/or the characteristic, a ride height of the vehicle may be adjusted.

A method for driving a vehicle on an opposite lane in a controlled manner includes detecting, with a surroundings sensor system, surroundings of the vehicle and receiving, with a control device, measurement data of the surroundings sensor system. The method includes identifying at least one course of a road, and at least one course of at least one road user in the surroundings based on the received measurement data and planning a trajectory of the vehicle within the at least one course of a road. The method further includes identifying a section of the road wherein when driving on the section of road the opposite lane is cut across by the vehicle, and determining a first stop position for the vehicle prior to entering the identified section of road. The method then checks whether the opposite lane can be driven on in the identified section.

Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.

A method for steering a vehicle along a path in a driveway and around obstacles between a starting position into a target position, comprises the steps of determining the vehicle dimensions, steering and driving capabilities, carrying out a path optimization step to evaluate, based on a predetermined cost function, the least costly path between the starting position and the target position avoiding any collisions with obstacles. The method further comprises the further step of applying a path improver step, smoothening the trajectory obtained by the path optimization method by means of numerical optimization while fulfilling dynamical constraints on acceleration and steering rate of the vehicle through planning lateral and longitudinal movement of the vehicle in a joint optimization problem or by means of separate optimization problems.

The invention relates to a method for adjusting fully automatic vehicle guidance functions, which are realized by means of a vehicle system of a motor vehicle, during the operation of the motor vehicles in a predefined navigation environment. A stationary infrastructure device that communicates with the motor vehicles is associated with the navigation environment. Function limits of each vehicle guidance function are defined by means of limit operation parameters of the vehicle guidance function. Current traffic situation information describing dynamic objects in the navigation environment is determined by the infrastructure device by means of environment sensors of the navigation environment. The current traffic situation information is used, together with a digital map describing stationary objects and properties of the navigation environment, to determine at least one piece of risk information for each motor vehicle.

Systems and methods are provided for implementing hybrid prediction. Hybrid prediction integrates two deep learning based trajectory prediction approaches: grid-based approaches and graph-based approaches. Hybrid prediction techniques can achieve enhanced performance by combining the grid and graph approaches in a manner that incorporates appropriate inductive biases for different elements of a high-dimensional space. A hybrid prediction framework processor can generate trajectory predictions relating to movement of agents in a surrounding environment based on a prediction model generating using hybrid prediction. Trajectory predictions output from the hybrid prediction framework processor can be used to control an autonomous vehicle. For example, the autonomous vehicle can perform safety-aware and autonomous operations to avoid oncoming objects, based on the trajectory predictions.

Systems, device, and methods for trajectory association and tracking are provided. A method can include obtaining input data indicative of a respective trajectory for each of one or more first objects for a first time step and input data indicative of a respective trajectory for each of one or more second objects for a second time step subsequent to the first time step. The method can include generating, using a machine-learned model, a temporally-consistent trajectory for at least one of the one or more first objects or the one or more second objects based at least in part on the input data and determining a third predicted trajectory for the at least one of the one or more first objects or the one or more second objects for at least the second time step based at least in part on the temporally-consistent trajectory.

An autonomous driving control apparatus installable in a vehicle includes a path determining section, an obstacle determining section that determines whether an obstacle on the planned driving path is a passage acceptable obstacle or a passage unacceptable obstacle, the passage acceptable obstacle being previously set as an obstacle that the vehicle is allowed to come into contact with while passing, the passage unacceptable obstacle being previously set as an obstacle that the vehicle is not allowed to come into contact with while passing, and a control instructing section that gives an instruction of control to a maneuver controller to perform at least one of controlling a speed of the vehicle and controlling a steering of the vehicle to control a maneuver of the vehicle. If the obstacle is determined to be the passage acceptable obstacle, the control instructing section gives an instruction of the control to pass over the obstacle.