A method of controlling autonomous driving includes: collecting driving information of a subject vehicle that autonomously drives and driving information of at least one vehicle among other vehicles around the subject vehicle, determining driving intention of the at least one vehicle based on the driving information of the at least one vehicle, predicting a driving path of the at least one vehicle based on the driving information of the at least one vehicle, determining a target lane of the subject vehicle based on a driving path of the at least one vehicle, and determining a driving path of the subject vehicle based on the target lane of the subject vehicle and the driving path of the at least one vehicle.

A vehicular control system includes a forward viewing camera and a non-vision sensor. The control, responsive to processing of captured non-vision sensor data and processing of frames of captured image data, at least in part controls the equipped vehicle to travel along a road. The control, responsive at least in part to processing of frames of captured image data, determines lane markers on the road and determines presence of another vehicle traveling along the traffic lane ahead of the equipped vehicle and determines a trajectory of the other vehicle relative to the equipped vehicle. The control stores a trajectory history of the determined trajectory of the other vehicle relative to the equipped vehicle as the other vehicle and the equipped vehicle travel along the traffic lane.

The present invention obtains a processor, a processing method, a warning system, and a straddle-type vehicle capable of improving both the rider's safety and the rider's comfort.

A processor (20) includes: an acquisition section that acquires surrounding environment information corresponding to output of a surrounding environment detector (11) during travel of a straddle-type vehicle (100); a determination section that determines necessity of warning operation provided to the rider and generated by the warning system (1); and a control section that makes an alarm (30) perform the warning operation in the case where the determination section determines that the warning operation is necessary. The acquisition section further acquires helmet posture direction information corresponding to output of a helmet posture direction detector (13) during the travel of the straddle-type vehicle (100). The determination section determines the necessity of the warning operation on the basis of the surrounding environment information and the helmet posture direction information.

Driver assistance systems and autonomous driving systems have been attractive to researchers in the automotive industry to reduce the number of accidents and to increase driving efficiency. One of the most challenging tasks to safely and effectively automate is changing between traffic lanes. The systems and methods of the present application include applications of the Dynamic Windows Approach algorithm to guide a vehicle through an automated lane change procedure with improved safety and efficiency.

A moving object control device includes: a moving object position acquiring unit acquiring moving object position information indicating a position of a moving object; a target position acquiring unit acquiring target position information indicating a target position to which the moving object is caused to travel; and a control generating unit generating a control signal indicating a control content for causing the moving object to travel toward the target position on a basis of model information indicating a model that is trained using a calculation formula for calculating a reward including a term for calculating a reward by evaluating whether or not the moving object is traveling along a reference route by referring to reference route information indicating the reference route, the moving object position information acquired by the moving object position acquiring unit, and the target position information acquired by the target position acquiring unit.

Estimating a lane change intention of a vehicle includes capturing a plurality of different lane change indicator signals, transforming the respective lane change indicator signals into respective associated individual probabilities of a lane change using respective assigned transformation functions, weighting these individual probabilities of a lane change, determining a weighted overall probability of a lane change as the average of the weighted individual probabilities of a lane change, and estimating the existence of a lane change intention depending on the overall probability of a lane change, and outputting an associated lane change estimation signal.

A vehicle behavior estimation method includes: detecting a speed of a first preceding vehicle traveling in front of a host vehicle in a first lane where the host vehicle is traveling; detecting a speed of an adjacent vehicle traveling in a second lane adjacent to the first lane; calculating a relative speed between the first preceding vehicle and the adjacent vehicle; predicting whether or not an absolute value of the relative speed will be at or below a speed threshold value within a predetermined time from a point time when a decrease in the absolute value of the relative speed starts to be detected; and estimating that the adjacent vehicle is likely to change lanes into the first lane when the absolute value of the relative speed is predicted to be at or below the speed threshold value within the predetermined time.

Enclosed are embodiments for navigation with drivable area detection. In an embodiment, a method comprises: receiving a point cloud from a depth sensor, receiving image data from a camera; predicting at least one label indicating a drivable area by applying machine learning to the image data; labeling the point cloud using the at least one label; obtaining odometry information; generating a drivable area by registering the labeled point cloud and odometry information to a reference coordinate system; and controlling the vehicle to drive within the drivable area.

A method for automated management of the longitudinal speed of a first vehicle travelling on a first lane includes: detecting an intention of a second vehicle travelling on a second lane adjacent to the first lane to perform an insertion maneuver on the first lane; estimating a corrected longitudinal distance, the corrected longitudinal distance corresponding to the longitudinal distance that will separate the first vehicle from the second vehicle at the end of the insertion maneuver, the corrected longitudinal distance being calculated as a function of a measured longitudinal distance between the first vehicle and the second vehicle, and as a function of a relative longitudinal speed measured between the second vehicle and the first vehicle; and calculating a longitudinal speed setpoint of the first vehicle as a function of the corrected longitudinal distance.

A vehicle control device recognizes an intersection present in front of a vehicle proceeding in a first direction on a first road, a first another vehicle proceeding in a second direction opposite to the first direction on the first road to approach the intersection, and a second another vehicle traveling after the first another vehicle, controls the vehicle based on a first relative relation between the first another vehicle and the vehicle and a second relative relation between the second another vehicle and the vehicle, determines, when the first and second another vehicles are expected to enter the second road, whether the vehicle enters the second road after the first another vehicle and before the second another vehicle or after the second another vehicle based on relative relations between a basis position and the vehicle, the first and second another vehicles, and controls the vehicle based on a determining result.