A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to receive sensor data including wheel speed measurements, suspension displacement measurements, and tire leak detection data from a vehicle, estimate a rough road measurement based on a deviation of a wheel speed with respect to an average wheel speed and or based on suspension displacement sensor signals and generate temporal spatial map data indicative of a location and a roughness severity metric of a roadway portion based on the rough road measurement and tire leak detection data.

The present application discloses a method and an apparatus for generating a high precision map. According to an embodiment, the method comprises: acquiring three-dimensional (3D) laser point cloud data and information related to a grid map for generating the high precision map; determining position information of each piece of the 3D laser point data of the 3D laser point cloud data in the grid map; rendering each pixel point in the grid map, by using the reflection value of corresponding 3D laser point data of the 3D laser point cloud data, in order to generate each of grid images in the grid map; identifying, by using a machine learning algorithm, traffic information of each of the grid images in the grid map; clustering the traffic information of each of the grid images in the grid map to obtain the traffic information of the grid map; and loading the traffic information of the grid map into the grid map to generate the high precision map. The embodiment implements the generating of a high precision map with a high precision and a plurality of dimensions.

Systems, methods, and non-transitory computer-readable media can determine at least one potential route for navigating a vehicle within a geographic region. A score that measures a comfort level associated with the potential route can be determined, wherein the score is determined based on at least one sensor map that segments the geographic region into a grid of cells, and wherein the comfort level for the potential route is determined based at least in part on cells through which the vehicle travels while navigating along the potential route. A determination is made whether to use the potential route for navigating the vehicle based at least in part on the score.

Disclosed herein high definition map abnormality inference and corresponding driving method, device and mobility apparatus. The method includes: determining an abnormality of a high definition map applied to a mobility apparatus based on at least one of road information or surrounding object information recognized in driving of the mobility apparatus; determining whether exceptional object information recognized on a road is present; determining a road condition to be a temporary change situation and determining a first driving control process of the mobility apparatus corresponding to the temporary change situation, when the abnormality of the high definition map and exceptional object information are present; and determining the road condition to be a permanent change situation and determining a second driving control process corresponding to the permanent change situation, when the abnormality of the high definition map is present and the exceptional object information is not present.

A sensing apparatus for detecting speed bumps or potholes on a road is disclosed. The sensing apparatus comprises a telemetric sensing system configured to collect a plurality of position vectors by telemetric sensing of a road surface, wherein each position vector extends from a common origin to a respective point on the road surface and wherein each position vector has a length and a direction. The sensing apparatus further comprises a processing circuitry configured to detect a road flatness exception by evaluating the lengths of the position vectors. Thus, an improved apparatus for detecting road irregularities such as speed bumps or potholes on a road is provided. The sensing apparatus remains functional in low light conditions, e.g., at night, and also in the presence of reverberation of the vehicle. In an implementation form, the telemetric sensing system may comprise one or more radar sensors and/or one or more lidar sensors for collecting the plurality of position vectors using radar and/or lidar measurements.

A traveling body includes a range sensor and circuitry. The range sensor is disposed on a front face of the traveling body in a traveling direction in which the traveling body travels on a road surface and disposed obliquely downward at an inclination angle relative to the road surface. The circuitry determines presence of a continuous obstacle in a case where measurement results of a plurality of planes scanned by the range sensor include a plurality of feature values indicating unevenness relative to the road surface similar to each other. The circuitry generates an obstacle map of a front region of the traveling body not scanned by the range sensor, based on sequential feature information indicating that a predetermined number of feature values indicating the unevenness are sequentially present and controls the traveling body to travel on the road surface based on the obstacle map.

A computer system operates to determine a traction value for each of a plurality of regions of a road network. The computer system identifies a region of the road network for which the traction value is known. The computer system may direct a vehicle to operate over a region of the road network where the traction value is known, in order to obtain sensor data that is indicative of a traction capability of the vehicle.

Embodiments of the present description provide methods and apparatuses for detecting map calibration errors. In one aspect, a method includes: acquiring a map including at least one map element, determining at least one of feature information of the at least one map element or a navigation line according to the map, and determining whether a map element is calibrated wrongly according to the at least one of the feature information or the navigation line.

A method of determining trajectories (1810-1813) through at least one junction of a transportation network for display on a visual representation of a digital map, the digital map comprising data that is a digital representation of the transportation network. The method comprises obtaining positional information relating to the movement of a plurality of mobile devices with respect to time on the transportation network through an area (1801) comprising the at least one junction, the border of the area being divided into a plurality of segments. The positional information is used to create an entry histogram (1802) by determining a count of positional information that enters the area at each segment of the border and an exit histogram (1804) by determining a count of positional information that exits the area at each segment of the border; the histograms subsequently being used to define one or more entry (1806) and exits (1808) gates into and out of the area (1801). A trajectory through the area is assigned to pairs of entry and exit gates using the positional information.

An approach is provided for verifying reported ramp closures. The approach involves, for example, processing a ramp closure report to determine reported ramp link(s) associated with the ramp closure report. The approach also involves constructing a ramp network comprising the reported ramp link(s) and other ramp link(s) connected to the reported ramp link(s). The approach further involves collecting probe data collected from one or more sensors of one or more vehicles traveling within the ramp network. The approach further involves identifying one or more driving patterns based on one or more vehicle paths determined from the probe data. The approach further involves performing a verification of a closure status of the reported ramp link(s) based on the one or more driving patterns. The approach further involves providing the verification of the closure status as an output.