A control device for a vehicle includes an upper limit value setting unit configured to set an upper limit value of an acceleration or deceleration of the vehicle, and a vehicle controller configured to control the vehicle such that the acceleration or deceleration does not exceed the upper limit value. The upper limit value setting unit is configured to change the upper limit value according to at least one predetermined condition.

A vehicle control system uses reliability of an input signal of an autonomous vehicle to safely travel through an intersection or a crossroad. The system includes a first calculating unit that calculates reliability for behavior information of a front vehicle and a second calculating unit calculates reliability for state information of a traffic light in the crossroad or the intersection based on a surrounding vehicle. A third calculating unit calculates reliability for brake light information of the front vehicle and a fourth calculating unit calculates reliability for flow information of the surrounding vehicle passing the crossroad or the intersection. A determining unit generates a vehicle control signal according to the calculated reliability.

In one embodiment, when an autonomous driving vehicle (ADV) is parked, the ADV can determine, based on criteria, whether to operate in an open-space mode or an on-lane mode. The criteria can include whether the ADV is within a threshold distance and threshold heading relative to a vehicle lane. If the criteria are not satisfied, then the ADV can enter the open-space mode. While in the open-space mode, the ADV can maneuver it is within the threshold distance and the threshold heading relative to the vehicle lane. In response to the criteria being satisfied, the ADV can enter and operate in the on-lane mode for the ADV to resume along the vehicle lane.

An example operation includes one or more of receiving, by a server, data related to an environment associated with a target transport, analyzing, by the server, the data to determine if at least one adverse condition related to the environment exists, and responsive to existence of the at least one adverse condition, sending, by the server, a recommendation related to operation of the target transport in a safe mode to overcome the at least one adverse condition to the target transport.

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.

A system for method for future forecasting using action priors that include receiving image data associated with a surrounding environment of an ego vehicle and dynamic data associated with dynamic operation of the ego vehicle. The system and method also include analyzing the image data and detecting actions associated with agents located within the surrounding environment of the ego vehicle and analyzing the dynamic data and processing an ego motion history of the ego vehicle. The system and method further include predicting future trajectories of the agents located within the surrounding environment of the ego vehicle and a future ego motion of the ego vehicle within the surrounding environment of the ego vehicle.