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 and apparatus for controlling battery state of charge (SOC) in a hybrid electric vehicle are provided to enable the efficient use of energy, the maximization of energy recovery, and the improvement of fuel efficiency and operability without the improvement of capacity and performance of electrical equipment or a main battery in a hybrid electric vehicle. The apparatus includes a collecting device that collects information regarding the slope or the road type and information regarding the vehicle speed. A controller determines charge and discharge modes based on the driving information and determines a charging upper and lower limit SOC based on the road slope or road type information a road section on which the vehicle is traveling and the vehicle speed information in the road section. A charge or discharge command is output based on the charging upper limit SOC and the charging lower limit SOC.

Techniques for augmenting a reality captured by an image capture device are disclosed. In one example, a system includes an image capture device that generates a two-dimensional frame at a local pose. The system further includes a computation engine executing on one or more processors that queries, based on an estimated pose prior, a reference database of three-dimensional mapping information to obtain an estimated view of the three-dimensional mapping information at the estimated pose prior. The computation engine processes the estimated view at the estimated pose prior to generate semantically segmented sub-views of the estimated view. The computation engine correlates, based on at least one of the semantically segmented sub-views of the estimated view, the estimated view to the two-dimensional frame. Based on the correlation, the computation engine generates and outputs data for augmenting a reality represented in at least one frame captured by the image capture device.

In some examples, a method may include receiving retroreflected light that indicates at least one retroreflective property of a retroreflective article, wherein retroreflected light is captured at a first distance. The method may include determining a first set of information based at least in part on the at least one retroreflective property of the retroreflective article. The method may include receiving, from the light capture device, an image that includes at least one object, wherein the image is captured at a second distance. The method may include determining, based at least in part on the spatially resolvable property, a second set of information that corresponds to the object in the image. The method may include performing, by a computing device, at least one operation based at least in part on the second set of information.

A method and server for training a machine-learning algorithm (MLA) to detect objects in sensor data acquired by a second sensor mounted on a second vehicle located at a second distance from the objects, the MLA having been trained to detect the objects in sensor data acquired by a first sensor mounted on a first vehicle located at a first distance from the objects. First sensor data acquired by the first sensor on the first vehicle is aligned with second sensor data acquired by the second sensor on the second vehicle. The MLA determines objects and objects classes in the aligned first sensor data. The object classes in the aligned first sensor data are assigned to corresponding portions in the aligned second sensor data. The MLA is trained on the labelled portions in the aligned second sensor data to recognize and classify objects at the second distance.

An information processing method for a vehicle includes capturing and storing images of an area in front of the vehicle, acquiring information on the speed of the vehicle, and transmitting an image to an information processing apparatus when a judgment is made that a change in speed equal to or greater than a threshold occurs while the vehicle is traveling in a predetermined section, the image being captured during a predetermined period that includes the time when the judgment is made.

Systems and methods are provided for vehicle navigation. In one implementation, a navigation system for a host vehicle may comprise at least one processor. The processor may be programmed to receive from a camera onboard the host vehicle a plurality of captured images representative of an environment of the host vehicle. The processor may provide each of the plurality of captured images to a target object analysis module including at least one trained model configured to generate an output for each of the plurality of captured image. The processor may receive from the target object analysis module the generated output. The processor may further determine at least one navigational action to be taken by the host vehicle based on the output generated by the target object analysis module. The processor may cause the at least one navigational action to be taken by the host vehicle.

The method of the invention comprises: obtaining a sequence of at least two images, with different levels of illumination; extracting the region containing the sign in the image; calculating the luminance values of the signs; and obtaining the difference in luminance of the sign corresponding to the two levels of illumination. The value obtained is the luminance of the sign (11) corresponding to an illumination equal to the difference between the illuminations, or additional illumination. This result is based on the additive property of luminance, according to which the luminance of a sign is the sum of the luminance produced by each source of illumination. A basic illumination device (5), an additional illumination device (7), at least one camera for taking images, and image recording, positioning and synchronism systems are required to implement the method.

Systems and methods of video data extraction and processing from vehicles are described. The video data is captured using a video capture device at a vehicle. Sensor data is captured using one or more vehicle sensors at the vehicle. A data message is sent from the vehicle to a vehicle management server, the data message allowing the vehicle management server to access the video data and the sensor data. One or more model outputs are generated by providing the video data to one or more machine-learning models at the vehicle management server. An event record associated with an event is constructed based on the one or more model outputs using a vehicle rules engine. A vehicle management message is generated based on the event record and is sent to the vehicle.

Advanced driver assistance systems (ADAS) and methods for object detection such as traffic lights, speed signs, in an automotive environment, are disclosed. In an embodiment, ADAS includes camera system for capturing image frames of at least a part of surroundings of vehicle, memory comprising image processing instructions and processing system for detecting one or more objects in a coarse detection followed by a fine detection. Coarse detection includes detecting presence of the one or more objects in non-consecutive image frames of the image frames, where non-consecutive image frames are determined by skipping one or more frames of the image frames. Upon detection of presence of the one or more objects in coarse detection, fine detection of the one or more objects is performed in a predetermined number of neighboring image frames of a frame in which the presence of the objects is detected in coarse detection.