A system adapted to detect road condition in a vehicle and a method thereof uses geometrical laser projections and an image processing system. The system includes a laser source, an imaging unit and at least a processing unit. The laser source is adapted to project geometrical laser projections on the road. The imaging unit is adapted to capture images of the geometrical projections. The processing unit is configured to calculate a surface reflectance for the projected geometrical projection. Further it is configured to compute geometrical parameters of the projections at regular time intervals based on the captured images. It determines a road condition based on the surface reflectance and the geometrical parameters.

Methods and systems for distributed detection of road conditions and damage are described. In one embodiment, a method for distributed detection of road conditions and damage is provided. The method includes receiving, from one or more mobile devices, a plurality of reports of anomalies associated with roads in a geographic area. The method also includes storing the received reports of anomalies in a database and comparing each report of an anomaly to stored reports of previous anomalies in the database. The method further includes determining whether each report of an anomaly indicates road damage or a temporary problem. The method includes generating a prioritized list of locations of anomalies associated with one or more roads that have been determined to have road damage that needs maintenance and/or repair.

A vehicle control system having a subsystem controller for initiating control of a first group of at least one vehicle subsystem in a selected one of a plurality of subsystem control modes each corresponding to one or more different driving conditions; and an estimator module for evaluating at least one driving condition indicator to determine the extent to which each of the subsystem control modes is appropriate and for providing an output indicative of the subsystem control mode that is most appropriate. The estimator module is configured to increase the probability to which the at least one off-road driving mode is determined appropriate in dependence on at least one terrain indicator. In an automatic response mode the subsystem controller selects the most appropriate one of the subsystem control modes for each subsystem of the first group in dependence on the output.

A vehicle is provided including an electronic power steering system, an electronic throttle control system, and a stability control system.

A road surface evaluation apparatus includes a microprocessor configured to perform: acquiring driving information of each of vehicles, including a position and an acceleration of each of the vehicles while traveling, and a map information including road information on a road where the vehicles travel; evaluating a surface roughness of the road based on acquired accelerations of the vehicles; estimates a surface change location where the surface roughness has changed, based on a difference between a first road surface information including the evaluated surface roughness evaluated based on the acceleration of the vehicles acquired within a past predetermined period and a second road surface information including the evaluated surface roughness based on the acceleration of the vehicles acquired later than the past predetermined period; and outputting a road surface change information including the estimation result, in association with the road information.

A method is disclosed for analyzing historical accident information to adjust driving actions of an autonomous vehicle over a travel route in order to avoid accidents which have occurred over the travel route. Historical accident information for the travel route can be analyzed to, for example, determine accident types which occurred over the travel route and determine causes and/or probable causes of the accident types. In response to determining accident types and causes/probable causes of the accident types over the travel route, adjustments can be made to the driving actions planned for the autonomous vehicle over the travel route. In addition, in an embodiment, historical accident information can be used to analyze available travel routes and select a route which presents less risk of accident than others.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statues for performing safe driving operation. Example embodiments disclosed herein provide enhanced high-precision operation of an AV in low-speed environments, such as a toll booth facility or heavy traffic. One example method disclosed herein includes a control computer identifying a starting point of the toll booth facility on the roadway and a plurality of toll lanes associated with the toll booth facility; selecting a particular toll lane; determining a trajectory for the AV that extends through the particular toll lane; and in response to the autonomous vehicle arriving at the starting point for the toll booth facility, transmitting, over a subsystem interface to one or more drive subsystems of the AV, instructions configured to cause the drive subsystems to operate together to cause the AV to travel according to the trajectory.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: predict, at a trained machine learning classifier, a target slip value based on a predicted slip slope and a predicted road texture, wherein the predicted slip slope and the predicted road texture are determined using sensor data representing tire forces and modify at least one vehicle action based on the target slip value when a confidence level value corresponding to the target slip value is greater than or equal to a confidence level threshold.

An agricultural work machine includes a geographic position sensor that detects a geographic location of the agricultural work machine. An in-situ sensor detects a value of a dynamic response characteristic of the agricultural work machine corresponding to the geographic location. A predictive model generator generates a predictive model that models a relationship between the terrain feature characteristic and the dynamic response characteristic based on a value of the terrain feature characteristic in a prior information map at the geographic location and a value of the dynamic response characteristic sensed by the in-situ sensor at the geographic location. A predictive map generator generates a functional predictive dynamic response map of the field, that maps predictive values of the dynamic response characteristic to the different geographic locations in the field, based on the values of the terrain feature characteristic in the prior information map and based on the predictive model.

A vehicle control system including a spatial monitoring system includes on-vehicle cameras that capture images, from which are recovered a plurality of three-dimensional (3D) points. A left ground plane normal vector is determined for a left image, a center ground plane normal vector is determined for a front image, and a right ground plane normal vector is determined for a right image. A first angle difference between the left ground plane normal vector and the center ground plane normal vector is determined, and a second angle difference between the right ground plane normal vector and the center ground plane normal vector is determined. An uneven ground surface is determined based upon one of the first angle difference or the second angle difference, and an alignment compensation factor for the left camera or the right camera is determined. A bird's eye view image is determined based upon the alignment compensation factor.