A traffic light detection system for a vehicle is provided. The system may include at least one processing device programmed to receive, from at least one image capture device, a plurality of images representative of an area forward of the vehicle, the area including a traffic light fixture having at least one traffic light. The at least one processing device may also be programmed to analyze at least one of the plurality of images to determine a status of the at least one traffic light, and determine an estimated amount of time until the vehicle will reach an intersection associated with the traffic light fixture. The at least one processing device may further be programmed to cause a system response based on the status of at least one traffic light and the estimated amount of time until the vehicle will reach the intersection.

A system for a vehicle is provided. The system may include a memory and at least one processor configured to: access a plurality of images of a forward-facing view from the vehicle, the plurality of images corresponding to image data obtained by a camera; determine from the images a first lane marking on a first side of a lane, the lane through which the vehicle can navigate, and a second lane marking on a second side of the lane opposite of the first side; navigate the vehicle autonomously relatively centered between the first and second lane markings; determine from the plurality of images that an object is on the first side or the second side of the lane, and the object beyond the first or second lane marking; and navigate the vehicle autonomously to travel over a driving path that is offset from a center of the lane.

A target determination apparatus mounted in a vehicle includes a first relative speed acquisition device configured to acquire a relative speed of a target forward of the vehicle with respect to the vehicle as a first relative speed using a millimeter-wave radar, a second relative speed acquisition device configured to acquire a relative speed of the target with respect to the vehicle as a second relative speed using a lidar, and a determination device configured to, in a case where the difference between the first relative speed and the second relative speed exceeds a threshold, determine the target as an upper structure located above the height of the vehicle.

A driving assist device includes: a driving operation element; circuitry configured to acquire traveling state relevant information indicating a traveling state, control the own vehicle such that the own vehicle travels in a state where a target traveling condition is met, and determine whether the traveling state changed by the operation of the driving operation element is a specific state; and a request generation device configured to generate a condition change request when the predetermined operation or input is performed while the own vehicle is in the driving assist control, wherein the circuitry is configured to change the target traveling condition based on the traveling state relevant information when the condition change request is generated in a case where it is determined that the changed traveling state is the specific state.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

An inter-vehicle distance control device that achieves inter-vehicle distance control satisfying the driver includes a preceding vehicle velocity computation part that computes a preceding vehicle velocity on the basis of a host vehicle velocity and a relative velocity of the preceding vehicle, a target inter-vehicle setting art that sets a target inter-vehicle distance from the preceding vehicle on the basis of the preceding vehicle velocity, a target track generator that generates a target track and a target track differential, the target track defining a time history lasting until the initial value of the inter-vehicle distance reaches the target inter-vehicle distance, and a feedback controller that computes a feedback acceleration command by multiplying a deviation of the inter-vehicle distance from the target track and a deviation of the relative velocity from the target track differential value by a gain. The feedback acceleration command is output as an acceleration command.

A traffic light detection system for a vehicle is provided. The system may include at least one processing device programmed to receive, from at least one image capture device, a plurality of images representative of an area forward of the vehicle, the area including a traffic light fixture having at least one traffic light. The at least one processing device may also be programmed to analyze at least one of the plurality of images to determine a status of the at least one traffic light, and determine an estimated amount of time until the vehicle will reach an intersection associated with the traffic light fixture. The at least one processing device may further be programmed to cause a system response based on the status of at least one traffic light and the estimated amount of time until the vehicle will reach the intersection.

A device according to the present disclosure is configured to, when it is determined that the road on which a vehicle travels is an uphill road and the speed of the vehicle is controlled such that the vehicle follows a preceding vehicle, in a case where the preceding vehicle is no longer detected, inhibit the acceleration of the vehicle or decelerate the vehicle until it is determined that the road on which the vehicle travels is no longer the uphill road, or alternatively, until a certain period of time elapses.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

A driving assistance device includes a unit to acquire a degree of a velocity change of a subject vehicle; a unit to acquire a relative velocity of the subject vehicle with respect to a preceding vehicle; a unit to acquire an inter-vehicle distance between the subject vehicle and the preceding vehicle; a unit to determine whether the subject vehicle is in a proximity state with respect to the preceding vehicle based on the relative velocity and the inter-vehicle distance; and an alarm control unit to set an alarm timing that is a timing at which an alarm is output to a driver of the subject vehicle, based on a timing at which it is determined that the proximity state has occurred. The alarm control unit changes the alarm timing based on the degree of the velocity change of the subject vehicle acquired after determination that the proximity state has occurred.