A vehicle driving behavior monitoring and warning system that includes a host vehicle having a communication device that receives operating information of remote vehicles that are located within a predetermined zone of interest, a warning device and an electronic controller. The electronic controller is connected to the communication device and the warning device. The electronic controller evaluates operating information received by the communication device and determine whether or not one of the remote vehicles is operating with questionable driving behavior and determines whether or not the remote vehicle poses a potential threat to the host vehicle. In response to determining that the remote vehicle is operating with questionable driving behavior and poses a potential threat to the host vehicle, the electronic controller operates the warning device warning the operator of the host vehicle of the remote vehicle.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

In an overhead-structure recognition device to be mounted to a vehicle, a determination unit is configured to, in response to a vertical distance between an object of interest and a high-reflectivity object being greater than or equal to a predefined value of vertical distance, determine that the object of interest is an overhead structure which is a structure located above the vehicle that does not obstruct travel of the vehicle. The object of interest corresponds to a subset of interest among a plurality of subsets acquired by dividing range point cloud data. The high-reflectivity object is an object other than the object of interest, among objects corresponding to the respective subgroups, whose reflectance is greater than or equal to a predefined value of reflectance.

A method includes identifying an object that is invading a lane that an autonomous vehicle is occupying, and generating a constraint about a point of crossing, where the constraint has a direction and a length, and the point of crossing represents a location of where the object and the autonomous vehicle will collide if the object maintains its current trajectory and the autonomous vehicle maintains its current trajectory. The method includes applying the constraint to a motion plan associated with the autonomous vehicle, and issuing one or more commands to adjust movement of the autonomous vehicle in response to encountering the constraint.

A vehicle control apparatus is provided with: a recognizer configured to recognize a surrounding situation of a host vehicle; a controller programmed to perform a deceleration control when a deceleration target is recognized by the recognizer; and a detector configured to detect a slip of the host vehicle. The controller sets a first controlled variable, which is a controlled variable associated with the deceleration control when the slip of the host vehicle is detected without execution of the deceleration control, so as to suppress an extent of deceleration of the host vehicle, in comparison with a second controlled variable, which is the controlled variable when the slip of the host vehicle is not detected without execution of the deceleration control.

A traveling control apparatus performs a target-following control process on a target to be followed detected by a target detecting unit. Further, the traveling control apparatus calculates a probability that the target to be followed is within an own lane, and determines whether a degree of recognition by the target detecting unit of the target to be followed is in a weakly recognized state where the degree of recognition is weaker than a predetermined degree. The apparatus sets a reliability of the target to be followed on the basis of the probability calculated by a probability calculating process and a determination result by a determining process, and controls acceleration of an own vehicle so that a jerk which is a differential value of the acceleration becomes smaller as the reliability of the target to be followed is lower while the target-following control process is performed.

A method for minimizing disturbance due to wind forces of a trailer being towed by a vehicle. The method also includes receiving, at a data processing hardware data from a sensor system for the tow vehicle. The method also includes determining, at the data processing hardware, a passing object profile. The method also includes predicting, at the data processing hardware, a wind force profile based upon the sensor data the passing object profile. The method also includes determining, at the data processing hardware, at least one preventative action for the vehicle to minimize the effect of disturbance on the trailer.

In order to generate an improved travel path with sufficient accuracy, a vehicle travel path generation device includes a first travel path generation part (60) which approximates a lane on which a host vehicle (1) travels to output first travel path information, a second travel path generation part (70) which approximates a road division line ahead of the host vehicle (1) to output second travel path information, a travel path weight setting part (90) which sets a weight between the first travel path information and the second travel path information, and an integrated path generation part (100) which generates an integrated path information using the first travel path information, the second travel path information, and the weight by the travel path weight setting part (90), wherein the travel path weight setting part (90) sets the weight, on the basis of at least one of outputs from a bird's-eye view detection travel path weight setting part (91), a vehicle state weight setting part (92), a path distance weight setting part (93) and a peripheral environment weight setting part (94).

Provided are autonomous vehicles (AV), computer program products, and methods for maneuvering an AV in a roadway, including receiving forecast information associated with predicted trajectories of one or more actors in a roadway, determining a relevant trajectory of an actor based on correlating a forecast for predicted trajectories of the actor with the trajectory of the AV, regenerate a distance table for the relevant trajectory previously generated for processing constraints, generate a plurality of margins for the AV to evaluate, the margins based on a plurality of margin types for providing information about risks and effects on passenger comfort associated with a future proximity of the AV to the actor, classifying an interaction between the AV and the actor based on a plurality of margins, and generating continuous scores for each candidate trajectory that is also within the margin of the actor generated for the relevant trajectory.

The disclosure relates to a lane change method and system, a storage medium, and a vehicle. The lane change method includes the following steps: receiving consecutive frames of condition information, the condition information including velocity information of a current vehicle, state information of an adjacent vehicle, and lane information; with the condition information as an input to a neural network, processing the condition information by means of the neural network, to obtain an initial lane change strategy; and correcting the initial lane change strategy based on a predetermined rule and the condition information, to generate and output a corrected lane change strategy. According to this lane change method, intelligent, safe and efficient lane change may be achieved during an autonomous driving or driving assistance process.