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.

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.

An autonomous driving system for an autonomous vehicle includes an automated driving controller wirelessly connected to a towing taxi. The automated driving controller determines the autonomous driving system is non-functional. In response to determining the autonomous driving system is non-functional, the automated driving controller generates a notification indicating the autonomous driving system is non-functional. The automated driving controller receives, from the towing taxi, a current data string including a data point corresponding to a current point in time in combination with a predicted data point for each of one or more predicted points of time in the future. The current data string is compared with a previous data string recorded at a previous point in time. In response to determining the current data string matches the previous data string, the automated driving controller determines one or more driving maneuvers for the autonomous vehicle based on the current data string.

In various examples, a current claimed set of points representative of a volume in an environment occupied by a vehicle at a time may be determined. A vehicle-occupied trajectory and at least one object-occupied trajectory may be generated at the time. An intersection between the vehicle-occupied trajectory and an object-occupied trajectory may be determined based at least in part on comparing the vehicle-occupied trajectory to the object-occupied trajectory. Based on the intersection, the vehicle may then execute the first safety procedure or an alternative procedure that, when implemented by the vehicle when the object implements the second safety procedure, is determined to have a lesser likelihood of incurring a collision between the vehicle and the object than the first safety procedure.

A method includes receiving one or more images from a camera positioned on a back portion of the vehicle and receiving a driver planned path from a user interface. The driver planned path includes a plurality of waypoints. The method also includes transmitting one or more commands to a drive system causing the vehicle to autonomously maneuver along the driver planned path. The method also includes determining a current vehicle position and an estimated subsequent vehicle position. The method also includes determining a path adjustment from the current vehicle position to the estimated subsequent vehicle position. The method also includes transmitting instructions to the drive system causing the vehicle to autonomously maneuver towards the estimated subsequent vehicle position based on the path adjustment.

Systems and methods are provided for adjusting the creep torque to maneuver a vehicle to a target location. In various embodiments, the creep torque adjustment mode is deactivated when the driver changes the direction of travel. The change in direction also causes the parameters of the creep torque control to be reinitiated to their initial values. In various embodiments, the creep torque mode is increased from a low creep towards a target creep. If the driver engages the brakes, the input torque is set to zero, and when the driver releases the brake, the minimum creep torque is set to the value that creep torque had risen to just before the brake was applied. This allows the driver to control the acceleration and speed, by just braking. In various embodiments, the creep control controls reverse creep to aid in hooking up a vehicle to a trailer.

The present invention provides a driving assist apparatus, a driving assist method, and a driving assist system capable of realizing driving assist in consideration of a delay in a driver's operation regardless of a configuration of a vehicle. A driving assist apparatus includes a standard running route acquisition portion configured to acquire a standard running route calculated based on curve information ahead of a vehicle that is acquired by an external world recognition portion, and an actuator control output portion configured to acquire a standard vehicle motion amount when the vehicle runs the standard running route, calculate an instruction that guides a motion amount of the vehicle toward the standard vehicle motion amount based on the standard vehicle motion amount and a current vehicle motion amount of the vehicle, and output the instruction to an actuator portion configured to provide at least one of a curving force and a braking force to the 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.

The present disclosure relates to a method and system for integrated path planning and path tracking control of an autonomous vehicle. The method includes: obtaining five input control variables and eleven system state variables of an autonomous vehicle at current time; constructing a vehicle path planning-tracking integrated state model according to the obtained variables at the current time; enveloping external contours of two autonomous vehicles using elliptical envelope curves to determine elliptical vehicle envelope curves of the two autonomous vehicles, respectively; determining time to collision (TTC) between the vehicles according to elliptical vehicle envelope curves and vehicle driving states; establishing an objective function of a model prediction controller (MPC) according to the model; and solving the objective function based on the TTC, and determining input control variables to the MPC at the next time. Autonomous vehicle collision avoidance can be achieved according to the present disclosure.

This document describes near-object detection using ultrasonic sensors. Specifically, when an object is in a near-object range or distance from a vehicle, an object-detection system of the vehicle can utilize raw range measurements and various parameters derived from the raw range measurements. The various parameters may include an average, a slope, and a variation of the range. In the near-object range, using the parameters derived from the raw range measurements may lead to increases in the accuracy and performance of a vehicle-based object-detection system. The increased accuracy in near-object detection capability enhances safe driving.