A method of monitoring quality of a road segment from driver data is provided. The method includes receiving, by a server over a network, the driver data for a road segment from one or more sensing units in one or more vehicles. The method also includes calculating, in one or more computing devices, one or more quantitative pavement surface characteristics of the road segment from the driver data using a probabilistic inverse analysis framework. The method also includes identifying, in the one or more computing devices, one or more quantitative vehicle properties of the one or more vehicles from the driver data using the probabilistic inverse analysis framework. The method then includes estimating, in the one or more computing devices, one or more road quality characteristics of the road segment based on at least one of the quantitative pavement surface characteristics of the road segment and the quantitative vehicle properties of the one or more vehicles.

In one embodiment, a computer-implemented method for calibrating autonomous driving vehicles at a cloud-based server includes receiving, at the cloud-based server, one or more vehicle calibration requests from at least one user, each vehicle calibration request including calibration data for one or more vehicles and processing in parallel, by the cloud-based server, the one or more vehicle calibration requests for the at least one user to generate a calibration result for each vehicle. The method further includes sending, by the cloud-based server, the calibration result for each vehicle to the at least one user.

Disclosed is a method of providing active services based on big data using a remote start device of a vehicle. The method includes the steps of: collecting information related to the vehicle and a driver; deriving a behavior prediction value for predicting driver's behavior based on the collected information; operating an active service determination unit when the derived behavior prediction value meets a preset condition; determining, by the active service determination unit, proposal of an active service to the driver based on the collected vehicle-related information; determining a type of the active service and a time of providing the active service; transmitting proposal of the determined active service to a driver terminal to be displayed; and starting execution of the determined active service according to a change in the state of the driver terminal.

A method for producing an overtaking probability collection, having the following steps: recording a respective driving characteristic in a multiplicity of motor vehicles passing through at least one route section at a geographical position; assigning the respective motor vehicles to overtaking vehicles or to non-overtaking vehicles on the basis of the respective driving characteristic; determining a ratio between the overtaking vehicles and the non-overtaking vehicles; and entering the ratio into the overtaking probability collection as an overtaking probability for the route section at the geographical position.

Systems and methods relate to, inter alia, calculating a collision risk index for an area based upon historical traffic data. The systems and methods may further generate a notification to automatically engage or disengage an autonomous, or semi-autonomous, vehicle control feature in a vehicle based upon the collision risk index for the area. The systems and methods may further transmit the notification to a device of the vehicle to facilitate automatically engaging or disengaging an autonomous, or semi-autonomous, vehicle control feature in the vehicle as the vehicle approaches the area. As a result, vehicle collisions may be reduced, and vehicle safety enhanced.

A system and method of geofenced control of a vehicle, includes determining a geographic region within which a vehicle is operating; retrieving a geo-profile corresponding to the determined geographic region within which the vehicle is operating; and applying the retrieved geo-profile to the vehicle to alter the driving dynamics of the vehicle to conform to driving characteristics of the determined geographic region within which the vehicle is operating.

In particular embodiments, a computing system may determine a measured driving characteristic of a driving control system based on observations of vehicles driven by the driving control system. The system may determine a difference between the measured driving characteristic and a target driving characteristic, which is based on objectivations of one or more manually controlled vehicles. The system may determine an evaluation objective for the driving control system. The system may determine a weight function for the evaluation objective. The system may determine a score for the driving control system with respect to the evaluation objective by weighting the difference between the measured driving characteristic and the target driving characteristic using the weight function. The system may apply, based on the score, an adjustment to the driving control system to reduce a difference between a subsequently measured driving characteristic of the driving control system and the target driving characteristic.

Autonomous driving includes identifying a traffic behavior of an object in an environment surrounding a vehicle based on an evaluation of information about the environment surrounding the vehicle while the vehicle is in the midst of manual operation, and operating vehicle systems in the vehicle to perform a driving maneuver according to a driving plan for performing the driving maneuver. The autonomous driving further includes receiving a traffic behavior model that describes a predominating traffic behavior of a like population of reference objects, and operating the vehicle systems to perform the driving maneuver according to the driving plan in response to identifying that the traffic behavior of the object does not match the predominating traffic behavior of the like population of reference objects. Under the driving plan, the traffic behavior of the object is addressed.

Providing user assistance in a vehicle includes identifying a traffic behavior of an object in an environment surrounding the vehicle based on an evaluation of information about the environment surrounding the vehicle while the vehicle is in the midst of manual operation, and issuing an alert to a user prompting the user to implement defensive manual operation. The user assistance further includes receiving a traffic behavior model that describes a predominating traffic behavior of a like population of reference objects, and issuing the alert to a user prompting the user to implement defensive manual operation in response to identifying that the traffic behavior of the object does not match the predominating traffic behavior of the like population of reference objects. Under the defensive manual operation, the traffic behavior of the object is addressed.

A method for verifying an operation of an autonomous vehicle using a Quality Control verifying application is provided. The method includes steps of: (a) the monitoring server acquiring a certain driving PVD from the autonomous vehicle, wherein the certain driving PVD is a driving PVD corresponding to the autonomous vehicle's execution of a certain operation event at a certain subsection; (b) the monitoring server (i) performing a process of acquiring first verification result information including information on whether execution is successful for the certain operation event and (ii) performing a process of acquiring second verification result information including information on a result of determining whether the certain driving PVD matches with a certain criterion PVD by comparing the certain driving PVD with the certain criterion PVD; and (c) the monitoring server acquiring third verification result information by determining whether the first verification result information matches with the second result information.