In various examples, systems and methods are disclosed for weighting one or more optional paths based on obstacle avoidance or other safety considerations. In some embodiments, the obstacle avoidance considerations may be computed using a comparison of trajectories representative of safety procedures at present and future projected time steps of an ego-vehicle and other actors to ensure that each actor is capable of implementing their respective safety procedure while avoiding collisions at any point along the trajectory. This comparison may include filtering out a path(s) of an actor at a time step(s)—e.g., using a one-dimensional lookup—based on spatial relationships between the actor and the ego-vehicle at the time step(s). Where a particular path—or point along the path—does not satisfy a collision-free standard, the path may be penalized more negatively with respect to the obstacle avoidance considerations, or may be removed from consideration as a potential path.

An object is to reduce a current detection error of a current sensor while suppressing upsizing of a power conversion device equipped with the current sensor. A power conversion device includes a power conversion circuit; a conductor to transmit current to the circuit; and a coreless current sensor to detect the current. The coreless current sensor includes: a magnetic field detection portion; and a shield portion facing the magnetic field detection portion. The conductor includes: a first conductor portion that passes through a space between the magnetic field detection portion and shield portion; and a second conductor portion connected to the first conductor portion via a first bent portion, and the first bent portion is formed such that the space between the magnetic field detection portion and shield portion is not disposed in a direction perpendicular to a face of the second conductor portion closest to the shield portion.

A vehicle includes a driving device configured to control a speed of the vehicle, a camera configured to detect a surrounding vehicle, and a controller configured to determine the speed of the vehicle. The controller also calculates an image vector variation amount of the surrounding vehicle when the speed of the vehicle is lower than a predetermined speed and calculates a safety distance between the vehicle and a preceding vehicle based on the image vector variation amount of the surrounding vehicle when the image vector variation amount of the surrounding vehicle satisfies a predetermined condition. The controller also controls the driving device to control the speed of the vehicle depending on the calculated safety distance.

Systems and methods for generating motion plans for autonomous vehicles are provided. An autonomous vehicle can include a machine-learned motion planning system including one or more machine-learned models configured to generate target trajectories for the autonomous vehicle. The model(s) include a behavioral planning stage configured to receive situational data based at least in part on the one or more outputs of the set of sensors and to generate behavioral planning data based at least in part on the situational data and a unified cost function. The model(s) includes a trajectory planning stage configured to receive the behavioral planning data from the behavioral planning stage and to generate target trajectory data for the autonomous vehicle based at least in part on the behavioral planning data and the unified cost function.

Systems and methods for generating motion plans for autonomous vehicles are provided. An autonomous vehicle can include a machine-learned motion planning system including one or more machine-learned models configured to generate target trajectories for the autonomous vehicle. The model(s) include a behavioral planning stage configured to receive situational data based at least in part on the one or more outputs of the set of sensors and to generate behavioral planning data based at least in part on the situational data and a unified cost function. The model(s) includes a trajectory planning stage configured to receive the behavioral planning data from the behavioral planning stage and to generate target trajectory data for the autonomous vehicle based at least in part on the behavioral planning data and the unified cost function.

Technologies and techniques for operating a motor vehicle, in which the vehicle driver is monitored by at least one monitoring device of the motor vehicle, and a check is carried out as to whether, according to a predefined criterion, the vehicle driver is distracted by an operator control intention or an operator control action which has already taken place. The at least one monitoring device monitors manual operation of at least one operator control unit and, if the vehicle driver is distracted according to the predefined criterion, the vehicle driver or another occupant is offered or forced to use an alternative operator control type by the motor vehicle.

In one embodiment, a computing system of a vehicle may receive vehicle driving data associated with a vehicle driving in an environment and detected environment data associated with the environment. The system may generate a reference trajectory of the vehicle driving in the environment based on the vehicle driving data. The system may determine driving constraints associated with the environment based on the detected environmental data. The system may generate a trajectory of the vehicle based on the driving constraints. The system may determine a difference in at least one parameter associated with the trajectory relative to at least one corresponding parameter associated with the reference trajectory. The system may adjust weight values associated with cost functions of the trajectory based on the difference between the at least one parameter associated with the trajectory and the corresponding parameter associated with the reference trajectory.

The present disclosure relates to a method for forming a local navigation path for an autonomous vehicle (100, 102), specifically using a plurality of path detection modules (206) and obstacle avoidance modules (208) individually targeted towards specific path and obstacle conditions, respectively. The present disclosure also relates to a navigation path determination system (200) and to a corresponding computer program product.

Provided herein is a system and method for implementing one or more strategies in response to a condition inside a vehicle. The system comprises a sensor to capture data, one or more processors, and a memory storing instructions that cause the one or more processors to detect, based on the data, a condition in the vehicle, determine a level of severity of the condition, and determine one or more strategies in response to a determined level of severity of the condition. The system further implements the one or more strategies to resolve the condition.

The systems and methods disclosed herein are configured to determine if a trailer backup assist system is needed to assist a driver with a procedure to backup a trailer that is connected to a vehicle. The estimation of need for assistance may be determined by an assistance model. If assistance is needed, the systems and methods provide an input to initiate a process to enable the trailer backup assist system.