A system for controlling operation of a vehicle during a turning maneuver includes an object detection sensor, a vehicle turning angle sensor, and a processor. A memory is communicably coupled to the processor and stores a turning alert zone module configured to determine if a speed of the vehicle is within a predetermined range of speeds and, if the speed of the vehicle is within the predetermined range of speeds, determine if a vehicle turning angle is above a predetermined threshold value. If the turning angle is above the predetermined threshold value, and using the turning angle, a turning alert zone is determined. The system may then determine if at least a portion of an object resides in the turning alert zone and, if at least a portion of an object resides in the turning alert zone, control an operation of the vehicle (for example, a braking operation).

Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.

A driving assistance device configured to execute deceleration assistance for a driver's vehicle when the driver's vehicle turns right or left at an intersection is configured to recognize, based on a detection result from an external sensor of the driver's vehicle, an adjacent vehicle traveling in an adjacent lane adjacent to a traveling lane of the driver's vehicle, determine whether the adjacent vehicle turns in the same direction of the driver's vehicle at the intersection based on the detection result from the external sensor when the adjacent vehicle is recognized and the driver's vehicle turns right or left at the intersection, and execute the deceleration assistance to cause a vehicle-to-vehicle distance between the driver's vehicle and the adjacent vehicle to reach a distance equal to or larger than a target driver's vehicle-to-adjacent vehicle distance when the driving assistance device determines that the adjacent vehicle turns in the same direction.

Aspects of the disclosure relate to identifying and adjusting speed plans for controlling a vehicle in an autonomous driving mode. In one example, an initial speed plan for controlling speed of the vehicle for a first predetermined period of time corresponding to an amount of time along a route of the vehicle is identified. Data identifying an object and characteristics of that object is received from a perception system of the vehicle. A trajectory for the object that will intersect with the route at an intersection point at particular point in time is predicted using the data. A set of constraints is generated based on at least the trajectory. The speed plan is adjusted in order to satisfy the set of constraints for a second predetermined period of time corresponding to the amount of time. The vehicle is maneuvered in the autonomous driving mode according to the adjusted speed plan.

A controller causes a host vehicle to decelerate when a velocity of an oncoming vehicle corresponding to a position of the oncoming vehicle distant from a stationary object by a predetermined distance is greater than or equal to a velocity threshold corresponding to the position of the oncoming vehicle distant from the stationary object by the predetermined distance in a case in which a passing position is present within a predetermined region, and causes the host vehicle to keep the velocity or accelerate when the velocity of the oncoming vehicle corresponding to the position of the oncoming vehicle distant from the stationary object by the predetermined distance is less than the velocity threshold corresponding to the position of the oncoming vehicle distant from the stationary object by the predetermined distance in the case in which the passing position is present within the region.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

A method includes identifying an object that is invading a lane that an autonomous vehicle is occupying, and generating a constraint about a point of crossing, where the constraint has a direction and a length, and the point of crossing represents a location of where the object and the autonomous vehicle will collide if the object maintains its current trajectory and the autonomous vehicle maintains its current trajectory. The method includes applying the constraint to a motion plan associated with the autonomous vehicle, and issuing one or more commands to adjust movement of the autonomous vehicle in response to encountering the constraint.

Systems and methods of collision detection are provided which combine the concept of collaborative support with common/existing in-vehicle imaging and proximity sensor technologies. In particular, an “intermediary” vehicle may signal one or more objects (e.g. other vehicles, pedestrians, etc.) which are within the line of sight of the intermediary vehicle (and thus its imaging and proximity sensors), but not within the line of sight of each other, to a potential collision between the two objects.

Image data are input to a machine learning program. The machine learning program is trained with a virtual boundary model based on a distance between a host vehicle and a target object and a loss function based on a real-world physical model. An identification of a threat object is output from the machine learning program. A subsystem of the host vehicle is actuated based on the identification of the threat object.

Provided is an apparatus for assisting travelling of a vehicle, the apparatus including: a camera disposed in the vehicle, having a field of view of a front of the vehicle, and acquiring image data; and a controller including a processor for processing the image data, wherein the controller is configured to: identify a lane being travelled on by the vehicle and a preceding vehicle travelling in front of the vehicle based on the image data processed, and in response to the preceding vehicle crossing a center line, generate a control signal for controlling the vehicle based on the preceding vehicle crossing the center line.