A control system for a vehicle traveling in a first direction on a road segment is presented. The vehicle has a driver support function for autonomously maneuvering the vehicle, and the driver support function is capable of operating within an operational design domain (ODD) including a road safety barrier metric and a road characteristics metric. The control system comprises control circuitry configured to obtain data comprising information about a surrounding environment of the vehicle. The information includes road safety barrier data and road characteristics data. Further, the control circuitry is configured to determine a fulfilment of the ODD based on the obtained data by determining a fulfilment of the road safety barrier metric and the road characteristics metric based on the road safety barrier data and the road characteristics data, respectively.

A computing device including interface for receiving from a sensor device sensor data representative of an environment surrounding a host vehicle, and a processor configured to obtain a planned driving action for accomplishing a navigational goal of a host vehicle operating in a first lane of a roadway, identify, from the sensor data, a moving target vehicle located in a second lane of the roadway, identify, based on the target vehicle speed and direction, the target vehicle predicted trajectory indicating a cut-in movement of the target vehicle from the second lane to the first lane, identify an intersection of a planned trajectory for the host vehicle with the predicted trajectory for the target vehicle, and determine a safety action of the host vehicle to respond to the movement of the target vehicle; and cause the safety action to be performed in the host vehicle.

Systems and methods are disclosed for navigating a host vehicle. In one implementation, at least one processor may be programmed to obtain images representative of an environment of a host vehicle; identify, from the images, a feature of a roadway used to navigate the host vehicle on a path, the identified feature of the roadway having an ambiguity along the path; obtain map data associated with the environment to resolve the ambiguity of the identified feature; and generate a trajectory to navigate the host vehicle on the roadway, using the map data associated with the environment.

Systems and methods are provided for navigating a host vehicle. In some embodiments, the system may include at least one processing device programmed to: receive at least one image representative of an environment of the host vehicle; determine a navigational action of the host vehicle; analyze the at least one image to identify a target vehicle in the environment of the host vehicle; determine a next-state distance between the host vehicle and the target vehicle that would result if the navigational action was taken; determine a maximum braking capability of the host vehicle, a maximum acceleration capability of the host vehicle, and a speed of the host vehicle; determine a stopping distance for the host vehicle; determine a speed of the target vehicle; and implement the navigational action if the determined stopping distance for the host vehicle is less than the next-state distance summed with a target vehicle travel distance.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

A vehicle control system includes a vehicle-mounted sensor, a map database, a control rule database, and an electronic control unit. The electronic control unit is configured to recognize the position of a vehicle on a map; control traveling of the vehicle by using one of a plurality of control rules based on the position of the vehicle on the map, map information, and a detection result of the vehicle-mounted sensor; recognize a road section in the traveling direction of the vehicle based on the position of the vehicle on the map and the map information; specify a control rule used in the road section based on the recognized road section and control rule data; and control traveling of the vehicle in the road section by using the specified control rule.

A stability control method of a vehicle includes: determining possibility of broadside collision; as a result of the determining, when the possibility of broadside collision is present, applying a standby hydraulic pressure to a hydraulic brake device; when broadside collision is detected, performing evasion steering using the hydraulic brake device in consideration of a direction of the broadside collision; and performing stability control after the evasion steering is performed.

The present disclosure may be directed to a computer-assisted or autonomous driving (CA/AD) vehicle that receives a plurality of indications of a condition of one or more features of a plurality of locations of a roadway, respectively, encoded in a plurality of navigation signals broadcast by a plurality of transmitters as the CA/AD vehicle drives past the locations enroute to a destination. The CA/AD vehicle may then determine, based in part on the received indications, driving adjustments to be made and send indications of the driving adjustments to a driving control unit of the CA/AD vehicle.

A system configured to avoid a rear-cross traffic collision includes an obstacle detection unit detecting a position of an obstacle by receiving electromagnetic waves reflected off a reflection point of the obstacle; a direction estimation unit estimating a traveling direction of the obstacle on the basis of the position of the obstacle detected by the obstacle detection unit; and a collision determination unit determining possibility of a collision with the obstacle on the basis of the traveling direction of the obstacle estimated by the direction estimation unit.

Systems and methods for automated driving vehicle parking detection are described herein. The systems and methods are directed to detecting an available space, the available space comprising a space dimension larger than the automated driving vehicle, detecting a road marker associated with the available space, and determining that the available space is not a parking space based on the road marker and the space dimension. The systems and methods are also directed to guiding the automated driving vehicle to park in the available space in response to determining that the available space is not the parking space, and activating an occlusion prevention subsystem.