A method of controlling operation of a vehicle includes monitoring one or more features of a road segment, the vehicle configured to communicate with a plurality of objects in a wireless communication network, the vehicle configured to generate a communication based on a reference value of a parameter related to at least one of an environment around the vehicle and a behavior of the vehicle. The method also includes determining, based on the monitoring, a condition of the road segment, the condition including at least a curvature of the road segment, inputting the condition into a machine learning model configured to adjust the reference value based on the condition and output an adjusted reference value, and comparing the adjusted reference value to a current parameter value, and based on the adjusted reference value matching the current parameter value, transmitting an alert to one or more of the plurality of objects.

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.

Predicting future infrastructure performance of a pathway article based on data for infrastructure performance features that influence predicted infrastructure performance at a future point in time. To predict infrastructure performance at a future point in time, a computing device receives one or more sets of infrastructure performance data for a pathway article that correspond respectively to infrastructure performance features. The infrastructure performance features may influence predicted infrastructure performance of the pathway article at a future point in time and the infrastructure performance data may correspond to a roadway portion. The computing device may generate, based at least in part on applying the one or more sets of infrastructure performance data to a model, at least one infrastructure performance prediction value that indicates predicted infrastructure performance of the pathway article at the future point in time.

In one embodiment, a computing system of a vehicle may receive perception data associated with a scenario encountered by a vehicle while operating in an autonomous driving mode. The system may identify the scenario based at least on the perception data. The system may generate a performance metric associated with a vehicle navigation plan to navigate the vehicle in accordance with the identified scenario. In response to a determination that the performance metric associated with the vehicle navigation plan fails to satisfy one or more criteria for navigating the vehicle in accordance with the identified scenario, the system may trigger a disengagement operation related to disengaging the vehicle from the autonomous driving mode. The system may generate a disengagement record associated with the triggered disengagement operation. The disengagement record may include information associated with the identified scenario encountered by the vehicle related to the disengagement operation.

A moving object escape prevention method includes: controlling, by a processor of a moving object, to drive the moving object based on autonomous driving; detecting, by the processor, whether a collision occurred by the moving object; in response to detecting the collision, transmitting, by the processor, a collision occurrence notification signal and position information of the moving object to an Intelligent Transportation System Infrastructure (ITSI); receiving, by the processor, escape-related information from the ITSI. The receiving escape-related information includes: determining, by the ITSI, whether or not the moving object escapes based on position information of the moving object; receiving, by the processor, accident handling information from the ITSI upon determining that the moving object does not escape, and receiving, by the processor, an escape warning message from the ITSI when the position information of the moving object changes.

An autonomous driving control apparatus for a vehicle includes: a processor that demands a user of a vehicle to take a control authority of the vehicle during an autonomous driving control when a current driving condition is in a limit situation during the autonomous driving control, and starts a minimum risk maneuver to disable reactivation of the autonomous driving control when the control authority is not transferred to the user; and a storage to store a set of instructions to be executed by the processor and data for determination and performance by the processor. In particular, the processor automatically flashes an emergency light when the minimum risk maneuver is started, and controls automatic flashing of the emergency light to not be released by the user when the vehicle is not in a stopped state.

Systems and methods for an autonomous vehicle are provided. In one aspect, an autonomous vehicle includes a perception sensor and a processor configured to: receive detected roadway conditions data including roadway grade data from the perception sensor, retrieve mapped data having grade data, and determine that the roadway has a grade based on the detected roadway grade data and the retrieved roadway grade data. The processor can be further configured to, in response to determining that the roadway has a grade, determine that the grade of the roadway is greater than or equal to a predetermined high grade value and less than a predetermined grade limit, and in response to determining that the grade of the roadway is greater than or equal to the predetermined high grade value and less than the predetermined grade limit, operate the autonomous vehicle to change lane to a right-most lane.

A method for controlling braking of an autonomous vehicle includes: recognizing, by a driving situation recognizer, a vehicle stop situation based on environment information around the vehicle; generating, by a deceleration profile generator, a n.sup.th-order polynomial-based deceleration profile having a plurality of inflection points (n being a natural number equal to or greater than three) when the vehicle stop situation is recognized; correcting, by a corrector, the n.sup.th-order polynomial-based deceleration profile by setting at least one of a response time of a decelerator, a mass of the vehicle during driving or a deceleration performance of a brake to a control variable; and executing, by a controller, braking of the vehicle based on the corrected n.sup.th-order polynomial-based deceleration profile.

Aspects of the disclosure relate to generating a puddle occupancy grid including a plurality of cells. For instance, a first probability value for a puddle being located at a first location generated using sensor data from a first sensor may be received. A second probability value for a puddle being located at a second location generating using sensor data from a second sensor different from the first sensor may be received. A first cell may be identified from the plurality of cells using the first location. The first cell may also be identified using the second location. A value for the cell may be generated using the first probability value and the second probability value.

An automated vehicle control distributed network node, that includes at least two modems for communicating with two neighboring roadside nodes on the same side of the roadway; at least one antenna for communicating with vehicles via a wireless connection; pattern recognition processing operative to detect patterns using image data from a plurality of high speed, high resolution video cameras that include night vision; vehicle prediction processing, operatively coupled to the pattern recognition processing, operative to predict vehicle location, velocity and direction using the pattern recognition processing; and a vehicle controller, operatively coupled to the vehicle prediction processing to receive vehicle prediction data, and to the at least one antenna, operative to send acceleration, deceleration and steering control signals to a plurality of vehicles in response to vehicle prediction data received from the vehicle prediction processing.