A system for maneuvering a vehicle has a detection system, a prediction system, and a vehicle control system. The detection system is configured to detect a nearby vehicle adjacent to the vehicle. The prediction system is configured to calculate a predicted trajectory of the nearby vehicle upon receiving a detection result from the detection system. The vehicle control system is configured to maneuver the vehicle based on the predicted trajectory upon receiving a control signal from the prediction system. The vehicle control system maneuvers the vehicle to keep a specified distance away from the nearby vehicle. A method for maneuvering a vehicle includes detecting a nearby vehicle adjacent to the vehicle, calculating a predicted trajectory of the nearby vehicle, and maneuvering the vehicle based on the predicted trajectory to keep a specified distance away from the nearby vehicle.

Methods of determining relevance of objects that a vehicle's perception system detects are disclosed. A system on or in communication with the vehicle will identify a time horizon, and a look-ahead lane based on a lane in which the vehicle is currently traveling. The system defines a region of interest (ROI) that includes one or more lane segments within the look-ahead lane. The system identifies a first subset that includes objects located within the ROI, but not objects not located within the ROI. The system identifies a second subset that includes objects located within the ROI that may interact with the vehicle during the time horizon, but not excludes actors that may not interact with the vehicle during the time horizon. The system classifies any object that is in the first subset, the second subset or both subsets as a priority relevant object.

A movable carrier auxiliary system includes at least one optical image capturing system disposed on a movable carrier, at least one warning module, and at least one displaying device. The optical image capturing system includes an image capturing module and an operation module, and has at least one lens group including at least two lenses having refractive power. The image capturing module captures and produces an environmental image surrounding the movable carrier. The operation module electrically connected to the image capturing module detects at least one lane marking in the environmental image to generate a detecting signal. The warning module electrically connected to the operation module receives the detecting signal to determine whether a moving direction of the movable carrier deviates from a lane, and generate a warning signal when the moving direction deviates from the lane. The displaying device electrically connected to the warning module displays the warning signal.

A non-transitory computer readable storage medium has instructions executed by a processor to obtain the relative speed between a first traffic object and a second traffic object. The separation distance between the first traffic object and the second traffic object is received. The relative speed and the separation distance are combined to form a quantitative measure of hazard encountered by the first traffic object. The obtain, receive and combine operations are repeated to form cumulative measures of hazard associated with the first traffic object. The cumulative measures of hazard are analyzed to derive a first traffic object safety score for the first traffic object.

Methods and devices for operating a Self-Driving Car (SDC) are disclosed. The method includes generating a first graph-structure having nodes and edges, ranking the edges based on a priority logic into a ranked list of edges, and generating a second graph-structure (i) by iteratively generating attributes for respective ones from the ranked list of edges beginning with a highest priority edge in the ranked list of edges and (ii) until a pre-determined limit is met. The method also includes causing operation of the SDC on the road segment using the second graph-structure.

Techniques for determining vehicle trajectories to operate a vehicle according to a planned path are described herein. In an example, a vehicle computing system may determine a location of the vehicle at a first time. Based on the location, the vehicle computing system may determine an estimated location of the vehicle at a second time, the estimated location of the vehicle including a lateral coordinate and a longitudinal coordinate. The vehicle computing system may determine the longitudinal coordinate based on a vehicle trajectory associated with the first time (e.g., previously determined trajectory) and the lateral coordinate based on the planned path. The vehicle computing system may determine a second vehicle trajectory based in part on the estimated location and the first trajectory, and may control the vehicle according to the second vehicle trajectory.

An apparatus and method for controlling a vehicle speed based on information about forward vehicles that travel in the same lane may be acquired using Vehicle to Everything (V2X) communications in a cooperative adaptive cruise control (CACC) system. The CACC system includes a communication unit receiving vehicle information from neighboring vehicles using V2V communications; an information collection unit collecting vehicle information of the neighboring vehicles and the subject vehicle using sensors; and a control unit determining a forward vehicle and a far-forward vehicle using the sensors, selecting first and second target vehicles for being followed by the subject vehicle based on the vehicle information of the forward vehicle and the far-forward vehicle and the vehicle information of the neighboring vehicles, and controlling the driving speed of the subject vehicle based on speed information of the first and second target vehicles.

In one embodiment, a method, apparatus, and system for planning the trajectory of an autonomous driving vehicle (ADV) in view of an object within a buffer area in front of the ADV is disclosed. A buffer area in front of an ADV is identified. A first object of one or more objects that have entered the buffer area is identified. A first distance cost and a first relative speed cost associated with the first object are determined. A first object cost associated with the first object is determined based on a combination of the first distance cost and the first relative speed cost. A trajectory for the ADV is planned based at least in part on a cost function comprising the first object cost, where the cost function is minimized in the planning. Control signals are generated to drive the ADV based on the planned trajectory.

A vehicle includes a communication device configured to request a neighboring vehicle for first data related to autonomous driving of the neighboring vehicle and to receive the first data from the neighboring vehicle while the vehicle is driving; a sensor device configured to sense second data regarding a state of a user of the vehicle and to detect third data regarding driving information of the neighboring vehicle; and a controller configured to classify risks of the vehicle into classes according to a preset criterion based on the first data, the second data, and the third data, and to score the risks based on the classified classes.

Embodiments of this application disclose a vehicle-based data processing method performed by a computer device. The method includes: determining at least two predicted offsets of a first vehicle, a first traveling state of the first vehicle, and a second traveling state of a second vehicle; determining, according to the first traveling state and the second traveling state, first lane change payoffs of the predicted offsets when the second vehicle is in a yielding prediction state, and determining second lane change payoffs when the second vehicle is in a non-yielding prediction state; and determining a predicted yielding probability of the second vehicle, generating target lane change payoffs of the predicted offsets according to the predicted yielding probability and the first lane change payoffs and the second lane change payoffs of the predicted offsets, and determining a predicted offset having a maximum target lane change payoff as a target predicted offset.