The technology employs a variable motion control envelope that enables an on-board computing system of a self-driving vehicle to estimate future vehicle driving behavior along an upcoming path, in order to maintain a desired amount of control during autonomous driving. Factors including intrinsic vehicle properties, extrinsic environmental influences and road friction information are evaluated. Such factors can be evaluated to derive an available acceleration model, which defines an envelope of maximum longitudinal and lateral accelerations for the vehicle. This model, which may identify dynamically varying acceleration limits that can be affected by road conditions and road configurations, may be used by the on-board control system (e.g., a planner module of the processing system) to control driving operations of the vehicle in an autonomous driving mode.

Embodiments include apparatuses, methods, and systems for computer assisted or autonomous driving (CA/AD). An apparatus for CA/AD may include a sensor interface, a communication interface, and a driving strategy unit. The sensor interface may receive sensor data indicative of friction between a road surface of a current location of a CA/AD vehicle and one or more surfaces of one or more tires of the CA/AD vehicle. The communication interface may receive, from an external road surface condition data source, data indicative of friction for a surface of a road section ahead of the current location of the CA/AD vehicle. The driving strategy unit may determine, based at least in part on the sensor data and the data received from the external road surface condition data source, a driving strategy for the CA/AD vehicle beyond the current location of the CA/AD vehicle. Other embodiments may also be described and claimed.

An emergency maneuver control system includes a path planning control device, which is designed to determine an emergency maneuver trajectory in a highly automated or autonomous operating mode of the vehicle; a longitudinal guidance actuator control device which is coupled to the path planning control device and is designed to provide longitudinal guidance control commands derived from the emergency maneuver trajectory and, in the event of an emergency maneuver situation, to cause the at least one longitudinal guidance actuator to execute the longitudinal guidance control commands; and a transverse guidance actuator control device which is coupled to the path planning control device and is designed to provide transverse guidance control commands derived from the emergency maneuver trajectory and, in the event of an emergency maneuver situation, to cause the at least one transverse guidance actuator to execute the transverse guidance control commands.

A method for controlling autonomous driving of a vehicle according to the present invention comprises the steps of: constructing a safe stopping distance table including multiple preset stopping levels corresponding to multiple stopping distances determined based on multiple stopping variables required for calculating a safe stopping distance; acquiring current stopping variables required for calculating the safe stopping distance during autonomous driving; determining any one of the preset stopping levels as a current stopping level based on the acquired current stopping variables; selecting a certain stopping distance corresponding to the determined current stopping level as a current stopping distance, using the safe stopping distance table, and controlling a driving speed of the vehicle so that a distance between the vehicle and a preceding vehicle is maintained as the selected current stopping distance.

A vehicle control method and system, including: receiving road friction information indicating road friction estimates for a plurality of regions surrounding the vehicle; detecting and determining a predicted trajectory for an object within the plurality of regions surrounding the vehicle; wherein the predicted trajectory for the object is determined based in part on the road friction estimates for the plurality of regions surrounding the vehicle; and modifying operation of the vehicle based on the predicted trajectory for the object. The predicted trajectory for the object is determined based in part on a risk map for the plurality of regions surrounding the vehicle that is generated from a road friction map for the plurality of regions surrounding the vehicle.

A method for autonomous navigation of an autonomous vehicle includes: accessing a first scan image containing data captured by a sensor on the autonomous vehicle at a first time; identifying a first group of points in the first scan image representing an object in a field proximal the autonomous vehicle; characterizing a first motion of the object at the first time based on the first group of points; characterizing an uncertainty of the first motion of the object at the first time; calculating a predicted second uncertainty of a second motion of the object at a second time based on the first motion of the object and motion of the autonomous vehicle at the first time; and, in response to the predicted second uncertainty falling below the uncertainty, muting the object from braking consideration for object avoidance by the autonomous vehicle at the second time.

A road surface condition prediction system includes a collector which collects pieces of moisture information on moisture on a road surface obtained by detecting the moisture on the road surface of a road on which moving bodies travel, and pieces of position information each indicating a position on the road surface at which the moisture is detected, one or more of the pieces of moisture information and one or more of the pieces of position information being collected from each of the moving bodies; and a predictor which predicts a moisture condition of a target road surface at a time after a time at which moisture on the target road surface is detected, based on moisture information obtained by detecting the moisture on the target road surface, the target road surface being a road surface at a position indicated by at least one of the pieces of position information.

A control device for controlling a moving body in a system of monitoring the moving body from a remote location via a network includes a policy database configured to store policy information for controlling the moving body, a policy information calculation unit configured to calculate policy information indicating details of control according to a quality of the network on the basis of surrounding conditions of the moving body and to store the policy information in the policy database, and a control execution unit configured to acquire the quality of the network and to execute control corresponding to the quality of the network with reference to the policy information.

A method and system for training a neural network for predicting braking distance of a vehicle. The method comprises performing one or more training stages for the neural network in the vehicle, a first set of weights and biases received from a server is used as an initial set of weights and biases for the neural network during a first training stage; updating the set of weights and biases of the neural network after every training stage and sending the updated set of weights and biases of the neural network to the server after a certain number of training stages have been performed.

Apparatus (101) for controlling movement of a vehicle (100), a system (201) and vehicle 5 (100) comprising the apparatus (101), and a method (500, 600) for controlling the movement of a vehicle (100) are disclosed. The apparatus (101) comprises a controller (10) configured to receive first signals from a receiving means (202) in dependence on received transmitted signals from a remote control device (200) indicating a requested motion of a vehicle and to receive second signals indicative of a value of traction of the vehicle. A maximum speed 10 value for the vehicle is determined in dependence on the value of traction of the vehicle and/or on one or both of the detected pitch and roll angles of the vehicle (100). The controller (10) provides an output signal for controlling speed of the vehicle (100) based on the requested motion. The output signal is limited dependent upon the maximum speed value determined by the controller (10).