A system of linearizing a trajectory of an autonomous vehicle about a reference path includes a computing device and a computer-readable storage medium. The computer-readable storage medium includes one or more programming instructions that, when executed, cause the computing device to receive a reference path for an autonomous vehicle, where the reference path defines a proposed trajectory for the autonomous vehicle in a Cartesian reference frame, identify an objective based on the received reference path, where the objective comprises a longitudinal component and a lateral component, project the objective into a curvilinear coordinate frame described by the received reference path, decouple the longitudinal component and the lateral component, linearize the lateral component about the reference path, generate a new reference path for the autonomous vehicle by fusing the linearized longitudinal component and the linearized lateral component, and map the new reference path back to the Cartesian reference frame.

A method automatically controls an actuator of a control system of an automotive device. The method includes determining a reference trajectory, determining a position of the device with respect to the reference trajectory, acquiring a parameter relating to a force exerted by a driver on a manual control device of the control system, and calculating a controlling setpoint of the actuator. The controlling setpoint is calculated as a function of the parameter and of the position of the device with respect to the reference trajectory.

A trajectory planning method for an autonomous vehicle includes a lateral displacement planner that plans a preliminary traveling trajectory for an autonomous driving vehicle based on perceptual data, positioning data, and map information, where the preliminary traveling trajectory includes N road points, and each road point includes coordinate information of the road point and allowable lateral error information of the road point. A longitudinal speed planner inherits all or some of the N road points output by the lateral displacement planner and determines speed information of n road points based on traffic road condition information and the perception data to obtain a target traveling trajectory. The speed information is a drivable speed of the autonomous driving vehicle at each of the n road points. The target traveling trajectory includes the n road points, each of the n road points including the drivable speed of the autonomous driving vehicle at the road point.

A vehicle-based safety intervention system receives sensor data collected or generated by an onboard computing system of a vehicle. The sensor data is divided into a plurality of blocks, each of the blocks having a duration. A driver behavioral model is applied to one or more of the plurality of blocks to generate one or more driver behavioral parameters. A trend of the one or more driver behavioral parameters is extracted from the plurality of blocks. Based on the extracted trend, it is determined that a driver's performance when operating the vehicle is unsatisfactory or will be unsatisfactory in the future. A vehicle-based intervention is generated based on the determination that the driver's performance is unsatisfactory or will be unsatisfactory in the future.

Techniques for vehicle deceleration planning are discussed. The techniques include determining a first location and a first velocity of a vehicle. The techniques further include determining a second location and a second velocity of an object. Based on the first location, the second location, the first velocity, and the second velocity, a relative stopping distance between the vehicle and the object can be determined. If the relative stopping distance is less than a threshold distance, the first maximum deceleration value can be increased to a second maximum deceleration value, and the techniques determine a trajectory for the vehicle based at least in part on the second maximum deceleration value.

A controlling and warning system based on traffic conditions feedback and a method thereof are disclosed. In the controlling and warning system, a camera disposed on a rear of a vehicle body is configured to generate and transmit a rear video to a controlling host, and the controlling host identifies a vehicle object in the rear video and calculates a separation distance between the vehicle object and the vehicle body; the vehicle object is inputted into a large-sized vehicle recognition model which is built based on artificial intelligence neural network and trained completely, to recognize whether the vehicle object is a large-sized vehicle; when the vehicle object is recognized as the large-sized vehicle and the separation distance reaches to a safe distance, a warning signal is generated. Therefore, the technical effect of improving the warning immediacy of an approaching large vehicle can be achieved.

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.

Categorizing driving behaviors of other road users includes maintaining a first history of first lateral-offset values of a road user with respect to a center line of a lane of a road; determining a first pattern based on the first history of the first lateral-offset values; determining a driving behavior of the road user based on the first pattern; and autonomously performing, by a host vehicle, a driving maneuver based on the driving behavior of the road user. The first history can be maintained for a predetermined period of time. An apparatus includes a processor that is configured to track a trajectory history of a road user; determine, based on the trajectory history, a driving behavior of the road user; and transmit a notification of the driving behavior.

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 vehicle includes: recognizing a forward vehicle in response to the processing of image data captured by an image sensor disposed at the vehicle so as to have a field of view of the outside of the vehicle; obtaining a distance from the forward vehicle in response to the processing of detecting data captured by a radar disposed at the vehicle so as to have a detecting area of the outside of the vehicle; obtaining a change amount of vertical movement of the forward vehicle in the image data in response to the distance from the forward vehicle that is equal to or less than a reference distance; obtaining a height of an obstacle on a road surface corresponding to the change amount; obtaining the height of the obstacle on the road surface in the image data in response to the distance from the forward vehicle that exceeds the reference distance; identifying a driving speed of the vehicle; identifying a reference height corresponding to the driving speed of the vehicle; and outputting deceleration guide information in response to the height of the obstacle on the road surface that is greater than or equal to the reference height.