Methods and systems are provided for an oil diagnostic. In one example, a method may include intrusively increasing an oil pump displacement to determine if an internal oil leak is occurring. The method may further include adjusting a parking routine to determine if an external oil leak is determining.

In a vehicle system, a driver information acquisition unit acquires information about a driver of a vehicle. A detection unit detects that there is a possibility that the vehicle has been damaged. An identification unit identifies a driver of the vehicle when it is detected that there is a possibility that the vehicle has been damaged, based on the acquired driver information.

Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode or an autonomous vehicle. For instance, a polygon representative of the shape and location of a first object may be received. A polyline contour representation of a portion of a polygon representative of the shape and location of a second object may be received. The polyline contour representation may be in half-plane coordinates and including a plurality of vertices and line segments. Coordinates of the polygon representative of the first object may be converted to the half-plane coordinate system. A collision location between the polyline contour representation and the polygon representative of the first object may be determined using the converted coordinates. The autonomous vehicle may be controlled in the autonomous driving mode to avoid a collision based on the collision location.

Methods and systems are provided for an oil diagnostic. In one example, a method may include intrusively increasing an oil pump displacement to determine if an internal oil leak is occurring. The method may further include adjusting a parking routine to determine if an external oil leak is determining.

Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode or an autonomous vehicle. For instance, a polygon representative of the shape and location of a first object may be received. A polyline contour representation of a portion of a polygon representative of the shape and location of a second object may be received. The polyline contour representation may be in half-plane coordinates and including a plurality of vertices and line segments. Coordinates of the polygon representative of the first object may be converted to the half-plane coordinate system. A collision location between the polyline contour representation and the polygon representative of the first object may be determined using the converted coordinates. The autonomous vehicle may be controlled in the autonomous driving mode to avoid a collision based on the collision location.

A computer-readable medium stores instructions executable by one or more processors to implement an aggregate self-driving control architecture (SDCA) for controlling an autonomous vehicle. The aggregate SDCA includes a plurality of SDCAs each including a different motion planner. Each motion planner is configured to receive signals descriptive of a current state of an environment through which the autonomous vehicle is moving, and each SDCA is configured to generate candidate decisions for controlling the autonomous vehicle by using the respective motion planner to process the received signals. The aggregate SDCA also includes a decision arbiter configured to receive the candidate decisions generated by the SDCAs, generate decisions for controlling the autonomous vehicle by processing the candidate decisions, and provide signals indicative of the generated decisions to one or more operational subsystems of the vehicle to effectuate maneuvering of the vehicle.

A computer-readable medium stores instructions executable by one or more processors to implement an aggregate self-driving control architecture (SDCA) for controlling an autonomous vehicle. The aggregate SDCA includes a plurality of SDCAs each including a different motion planner. Each motion planner is configured to receive signals descriptive of a current state of an environment through which the autonomous vehicle is moving, and each SDCA is configured to generate candidate decisions for controlling the vehicle by using the respective motion planner to process the received signals. The aggregate SDCA also includes a decision arbiter configured to receive candidate decisions output by the SDCAs, generate decisions for controlling the vehicle by dynamically selecting from among the candidate decisions based on a current state of a desired mode signal, and provide signals indicative of the generated decisions to one or more operational subsystems of the vehicle to effectuate maneuvering of the vehicle.

A computer-readable medium stores instructions executable by one or more processors to implement a self-driving control architecture for controlling an autonomous vehicle. A perception component receives sensor data and generates signals descriptive of a current state of the environment. Based on those signals, a prediction component generates signals descriptive of one or more predicted future environment states. A motion planner generates decisions for maneuvering the vehicle toward a destination, at least by using the signals descriptive of the current and future environment states to set values of one or more independent variables in an objective equation. The objective equation includes terms corresponding to different driving objectives over a finite time horizon. Values of one or more dependent variables in the objective equation are determined by solving the equation subject to a set of constraints, and values of the dependent variables are used to generate decisions for maneuvering the vehicle.

A computer-readable medium stores instructions executable by one or more processors to implement a self-driving control architecture for controlling an autonomous vehicle. A perception and prediction component receives sensor data, and generates (1) an observed occupancy grid indicating which cells are currently occupied in a two-dimensional representation of the environment, and (2) predicted occupancy grids indicating which cells are expected to be occupied later. A mapping component provides navigation data for guiding the vehicle toward a destination, and a cost map generation component is configured to generate, based on the observed occupancy grid, the predicted occupancy grid(s), and the navigation data, cost maps that each specify numerical values representing a cost, at a respective instance of time, of occupying certain cells in a two-dimensional representation of the environment. A motion planner generates a grid path through the environment based on the cost maps, and corresponding decisions for maneuvering the vehicle.

A device for autonomously driving a transportation vehicle having at least one sensor system for sensing a surrounding area of the vehicle, at least one controller for controlling at least one actuator system of the vehicle, and a memory for storing a map of the surroundings, wherein the at least one controller evaluates surroundings data collected by the at least one sensor system, determines the location of the vehicle by the evaluated surroundings data in the map of the surroundings, and controls the at least one actuator system of the vehicle so that a predefined trajectory is travelled autonomously, wherein the controller has an obstacle-detection device to detect obstacles in the surroundings of the vehicle, wherein the at least one sensor system has at least one acceleration sensor, and the obstacle-detection device uses measurement data collected by the at least one acceleration sensor to detect collisions with obstacles.