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.

While operating a vehicle on a travel path, a width of the travel path is determined based on a map. Upon detecting a presence or an absence of a target vehicle in the travel path, a lateral position for the vehicle on the travel path is determined based on the width of the travel path and the presence or absence of the target vehicle. The vehicle is controlled to operate according to the determined lateral position on the travel path and a speed of the vehicle that is based on the determined lateral position on the travel path.

A vehicle control device comprises a unit to detect a distance to an obstacle; a unit to detect a speed of the vehicle; and a reduction support unit to, based on the speed and the distance, perform support for reduction of a speed of the vehicle. The reduction support unit changes the distance at which the reduction support is to be performed between a case where driving during parking and a case where not driving during parking. The distance at which the reduction support is to be performed is, for a first speed range, longer in a case where driving during parking than in a case where not driving during parking, and, for a second speed range, longer in a case where not driving during parking than in a case where driving during parking.

When executing the autonomous lane change control of lane change of the subject vehicle from the subject vehicle lane in which the subject vehicle travels to adjacent lane, the autonomous lane change control is executed so as to accelerate the lateral speed of the subject vehicle in the subject vehicle lane and thereafter decelerate the lateral speed in the subject vehicle lane. As a result, it takes longer for the driver of the following vehicle to confirm the lateral movement of the preceding subject vehicle during the lane change of the subject vehicle from the start of the lane change than a lane change in which the lateral speed is decelerated in the adjacent lane. Therefore, it becomes easier to recognize the lane change.

A vehicle control device includes a recognizer that is configured to recognize a surrounding situation of a host vehicle and a driving controller that is configured to control acceleration or deceleration and steering of the host vehicle on the basis of a recognition result of the recognizer. The driving controller is configured to cause the host vehicle to operate in at least any of a first driving state and a second driving state in which a rate of automation is higher or tasks required of an occupant are fewer than in the first driving state, and is configured to transition a driving state of the host vehicle to the first driving state on the basis of movement of a rearward vehicle that travels rearward of the host vehicle recognized by the recognizer in a direction of a vehicle width in a case where the host vehicle is operating in the second driving state.

A travel control apparatus is configured to control a travel of a vehicle so as to travel along a target path. The travel control apparatus is configured to perform: acquiring a change schedule information of a traffic light installed over each of a plurality of merge lanes that merge with a main line and configured to be able to change an indication form between a first indication form permitting the vehicle to merge with the main line and a second indication form instructing the vehicle to stop before a stop line; determining a merge lane on which the vehicle travels among the plurality of merge lanes, based on the change schedule information of a traffic light located in a travel direction of the vehicle; and generating a target path of the vehicle leading to the stop line of the merge lane.

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.

A safe following distance estimation system and an estimation method thereof are provided. The safe following distance estimation system adapted for an autonomous vehicle includes a sensor and a processor. The sensor senses an adjacent vehicle to generate first sensing data, and senses the autonomous vehicle to generate second sensing data. The processor estimates a first friction parameter between wheels of the adjacent vehicle and a pavement according to pavement material data, and estimates a second friction parameter between wheels of the autonomous vehicle and the pavement according to the second sensing data. The processor calculates a safe following distance between the autonomous vehicle and the adjacent vehicle according to the first sensing data, the second sensing data, the first friction parameter, the second friction parameter.

An autonomous vehicle detects a tailgating vehicle and uses various response mechanisms. A vehicle is identified as a tailgater based on whether its characteristics meet a variable threshold. When the autonomous vehicle is traveling at slower speeds, the threshold is defined in distance. When the autonomous vehicle is traveling at faster speeds, the threshold is defined in time. The autonomous vehicle responds to the tailgater by modifying its driving behavior. In one example, the autonomous vehicle adjusts a headway buffer (defined in time) from another vehicle in front of the autonomous vehicle. In this regard, if the tailgater is T seconds too close to the autonomous vehicle, the autonomous vehicle increases the headway buffer to the vehicle in front of it by some amount relative to T.

A system and method for controlling a hybrid electric vehicle using a driving tendency are provided. The method includes determining a driving tendency level based on data to determine a driving tendency of a driver and determining a target engine torque using an engine torque map based on a vehicle speed and a required torque. Whether the driving tendency level corresponds to a predetermined level is determined as well as whether the required torque is equal to or greater than a torque that corresponds to an optimal operating point of an engine when the driving tendency level corresponds to the predetermined level. The target engine torque is then adjusted when the required torque is equal to or greater than the torque that corresponds to the optimal operating point of the engine.