Embodiments relate to methods for efficiently encoding sensor data captured by an autonomous vehicle and building a high definition map using the encoded sensor data. The sensor data can be LiDAR data which is expressed as multiple image representations. Image representations that include important LiDAR data undergo a lossless compression while image representations that include LiDAR data that is more error-tolerant undergo a lossy compression. Therefore, the compressed sensor data can be transmitted to an online system for building a high definition map. When building a high definition map, entities, such as road signs and road lines, are constructed such that when encoded and compressed, the high definition map consumes less storage space. The positions of entities are expressed in relation to a reference centerline in the high definition map. Therefore, each position of an entity can be expressed in fewer numerical digits in comparison to conventional methods.

To enable guidance to be given more understandably, depending on the guidance location.

Provision of an image recognizing unit for analyzing the state of a guidance object that serves as a landmark for a guidance location, through image recognition using an image captured for the direction forward of a vehicle; a guidance information generating unit for generating guidance information that includes a supplementary explanation regarding the state of the guidance object, depending on the result of the analysis; and an output processing unit for outputting the guidance information.

A system for controlling a smart mobility by risk level includes: an information collection unit to collect position and speed information of the smart mobility and camera sensing information including a front traffic sign or object; a front detection unit to detect a front obstacle or inclination using the camera sensing information and to output a detection result; a control unit to change a warning to a user and/or a method of controlling the smart mobility by risk level based on the information collected by the information collection unit and the detection result; and a warning unit to warn the user by changing a method by risk level under the control of the control unit.

In one embodiment, a method includes receiving an image of an object captured in a geographic location. The method includes determining the geographic location associated with the image. The geographic location is represented in a map that includes one or more ambient light measurements corresponding to one or more geographic locations. The method includes using the one or more ambient light measurements corresponding to the geographic location in the map associated with the image to generate a color corrected image. The method includes determining a classification of the object using the color corrected image.

Disclosed are methods, systems, and non-transitory computer readable media that control an autonomous vehicle via at least two sensors. One aspect includes capturing an image of a scene ahead of the vehicle with a first sensor, identifying an object in the scene at a confidence level based on the image, determining the confidence level of the identifying is below a threshold, in response to the confidence level being below the threshold, directing a second sensor having a field of view smaller than the first sensor to generate a second image including a location of the identified object, further identifying the object in the scene based on the second image, controlling the vehicle based on the further identification of the object.

Disclosures herein may be directed to a method, technique, or apparatus directed to a computer-assisted or autonomous driving (CA/AD) vehicle that includes a system controller, disposed in a first CA/AD vehicle, to manage a collaborative three-dimensional (3-D) map of an environment around the first CA/AD vehicle, wherein the system controller is to receive, from another CA/AD vehicle proximate to the first CA/AD vehicle, an indication of at least a portion of another 3-D map of another environment around both the first CA/AD vehicle and the another CA/AD vehicle and incorporate the at least the portion of the 3-D map proximate to the first CA/AD vehicle and the another CA/AD vehicle into the 3-D map of the environment of the first CA/AD vehicle managed by the system controller.

System, method, and device for controlling a vehicle. In one example, the system includes an electronic processor configured to capture, via a camera, a first image, determine, within the first image, a road traffic factor, and generate, based on sensor information from one or more sensors of the vehicle, a second image depicting an environment surrounding the vehicle. The second image includes the road traffic factor. The electronic processor is also configured to, determine, based on the detected road traffic factor and the second image, a predicted trajectory of a traffic participant proximate to the vehicle, and generate a steering command for the vehicle based on the predicted trajectory.

A two-layer retroreflective article construction is enabled that produces higher wide-entrance-angle performance for signs and pavement markings. A single-layer overlay is enabled for existing signs and pavement markers that improve their entrance angle performance. Materials used in the construction of an article or an overlay are transparent to radiation in the range of 400 to 1000 nanometers and utilize TIR (total internal reflection). Minimum performance specifications are proposed that extend sign sheeting retroreflectivity specifications to entrance angles of 60 degrees. An innovative traffic sign design is enabled that increases the positioning performance of safety systems and automated navigation systems.

An approach is provided for fully-automated learning to match heterogeneous feature spaces for mapping. The approach involves determining a first feature space comprising first features and a second feature space comprising second features, and classified by a feature detector into a first attribution category and a second attribution category, respectively. The approach further involves calculating a first similarity score for the first feature space based on a first distance metric applied to the first features, and a second similarity score for the second feature space based on a second distance metric applied to the second features. The approach also involves determining a transformation space comprising a first weight to be applied to the first similarity score and a second weight to be applied to the second similarity score based on matching the first attribution category and the second attribution category.

A host vehicle including a plurality of sensors communicably coupled to the host vehicle. Additionally, the host vehicle includes processing circuitry configured to map-match a location of the host vehicle while the host vehicle is operating on a highway, receive obstruction information from the plurality of sensors, the plurality of sensors having a predetermined field of view corresponding to a host vehicle field of view, estimate a driver field of view based on the host vehicle field of view, determine whether a speed of the host vehicle is safe based on the driver field of view and the obstruction information, and modify driver operation in response to a determination that the speed of the host vehicle is not safe based on the driver field of view.