A vehicle control device includes a recognizer that is configured to recognize a peripheral situation of a vehicle and a driving controller that is configured to execute automated driving for controlling a speed and steering of the vehicle on the basis of a recognition result of the recognizer. The recognizer is configured to recognize a real-time environment including a situation of a nearby vehicle or a situation of a nearby road on which the vehicle travels, and the driving controller is configured to shunt the vehicle to a shunt location to suspend the automated driving in a case where the real-time environment corresponds to a predetermined condition, and resume the automated driving in a case where the predetermined condition is resolved after the shunt.

A control system (10) is suitable for use in one's own motor vehicle (12) and is configured and intended to use the environmental data provided to determine a position and a speed of a first motor vehicle (28) which is traveling directly ahead of one's own motor vehicle (12) in a first Lane (36), wherein one's own motor vehicle (12) is in the said lane (36). Furthermore, the control system is at least configured and intended to determine a position and a speed of a second motor vehicle (30) which is traveling in a lane (38) adjacent to the first lane (36) from the environmental data provided. Furthermore, the control system is configured and intended to detect from the environmental data provided whether there is a zipper situation. The control system is configured and intended to increase a target distance of one's own motor vehicle (12) to the first motor vehicle, if an amount of a relative speed of the second motor vehicle (30) relative to one's own motor vehicle (12) or relative to the first motor vehicle (28) is less than a predetermined first value, if the second motor vehicle (30) is located between one's own motor vehicle (12) and the first motor vehicle (28) in a longitudinal direction which extends along the adjacent lane (38), and if it was detected that the zipper situation applies.

A traffic signal recognition method and a traffic signal recognition device estimate whether or not a vehicle can be decelerated at a predetermined deceleration acceleration and can stop before a stop line based on a position of the stop line corresponding to a traffic signal located in a traveling direction of the vehicle, select the traffic signal corresponding to the stop line as a target traffic signal in a case where it is estimated that the vehicle cannot stop before the stop line, set detection area corresponding to the target traffic signal on an image obtained by capturing the traveling direction of the vehicle, and determine a display state of the target traffic signal by executing image processing on the detection area.

The invention relates to a method for lane-changing prediction of a target vehicle, the method including: receiving a velocity and a position of the target vehicle; respectively obtaining, based on the velocity and the position of the target vehicle, a first lane-changing probability and a second lane-changing probability of the target vehicle by using a first machine learning model and a second machine learning model; and determining a possibility of lane changing of the target vehicle based on the first lane-changing probability and the second lane-changing probability, the first machine learning model and the second machine learning model being pre-trained and being different from each other. The invention further relates to a device for lane-changing prediction of a target vehicle, a computer storage medium, and a vehicle.

A path planning device configured to plan a traveling path on a future route of a vehicle includes a lane evaluation unit, a score accumulation unit, and a lane selection unit. The lane evaluation unit is configured to estimate a risk score of each chronological section of each traveling lane parallel traveling lanes, the risk score representing a traveling risk. The score accumulation unit is configured to accumulate the risk scores for each traveling lane. The lane selection unit is configured to select the traveling lane for the traveling path based on evaluation accumulated values. The lane evaluation unit is configured to estimate a base score based on static information. The static information contains a traveling difficulty for the vehicle at a change node. The lane evaluation unit is configured to estimate the base score such that the traveling risk is higher as the traveling difficulty is higher.

A method for automated lane keeping includes automatically positioning a vehicle at a normal position in a lane of a roadway with a lane-keeping system of the vehicle, and storing lane-offset data for a predetermined portion of the roadway. The lane-offset data correspond to an offset position of the vehicle in the lane of the roadway that is different from the normal position. The method further includes detecting that the vehicle is operating on the predetermined portion of the roadway, and automatically positioning the vehicle at the offset position with the lane-keeping system when the vehicle is operated on the predetermined portion of the roadway.

A system will generate a vector map of a geographic area using a method that includes receiving a birds-eye view image of a geographic area. The birds-eye view image comprises various pixels. The system will process the birds-eye view image to generate a spatial graph representation of the geographic area, and it will save the node pixels and the lines to a vector map data set. The processor may be a component of a vehicle such as an autonomous vehicle. If so, the system may use the vector map data set to generate a trajectory for the vehicle as the vehicle moves in the geographic area.

Disclosed in the present disclosure are a method and an apparatus for determining a vehicle speed control model training sample. The method includes: a lane in map data is divided into a first lane area and a second lane area, and to-be-measured vehicle speed control features indicated by the two lane areas are different; the to-be-measured vehicle speed control features are determined as target vehicle speed control feature when a difference between a first empirical highest vehicle speed distribution and a second empirical highest vehicle speed distribution is greater than or equal to a preset difference threshold; and starting coordinates and ending coordinates of each preset lane section in the map data, and the target vehicle speed control features and a vehicle speed control label in the preset lane section are taken as a vehicle speed control model training sample.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that generates path prediction data for agents in the vicinity of an autonomous vehicle using one or more machine learning models. One of the methods includes identifying an agent in a vicinity of an autonomous vehicle navigating through an environment and determining that the agent is within a vicinity of a crossing zone across a roadway. The crossing zone can be a marked crossing zone or an unmarked crossing zone. For example, the crossing zone can be an unmarked crossing zone that has been identified based on previous observations of agents crossing the roadway. In response to determining that the agent is within a vicinity of a crossing zone: (i) features of the agent and of the crossing zone can be obtained; (ii) a first input that includes the features can be processed using a first machine learning model that is configured to generate a first crossing prediction that characterizes future crossing behavior of the agent, and (iii) a predicted path for the agent for crossing the roadway can be determined from at least the first crossing prediction.

A vehicle control device includes: a target traveling path setting unit that sets a target traveling path of an own vehicle; a reference position setting unit that sets a reference position of the own vehicle for specifying a position of the own vehicle with respect to the target traveling path; and a control unit that controls a steering assist amount of a steering wheel, based on a positional deviation being a deviation between the target traveling path set by the target traveling path setting unit and the reference position of the own vehicle set by the reference position setting unit. The reference position setting unit changes the reference position according to a vehicle speed.