This disclosure describes techniques for of improving an image readability via one or more patterns that may be taken using different light wavelengths. A first pattern may include alphanumeric characters, barcodes, Quick Response (QR) codes, or a similar unique code that can be used to identify vehicle license plates, road signals, charts, placards, advertisements, or the like, using a light wavelength such as a visible light wavelength. A paired second pattern may include a copy of the first pattern or a different pattern but constructed with a different material that responds to a different light wavelength e.g., Ultra-Violet (UV) light wavelength. In one example, the paired second pattern may be identified and used as a reference for identifying the first pattern. This technique of using multi-patterns for identifying road signals, charts, placards, advertisements, and particularly the vehicle license plates during extreme weather conditions may improve law enforcement operations or other similar purposes.

A system and method for recognizing traffic signs during autonomous driving comprises a camera module obtaining a first recognition, a sensor obtains behavior information of the host vehicle and of nearby vehicles and a connected training module generates parameters for traffic sign recognition according to information as to the behavior of the host vehicle and the nearby vehicles. A recurrent neural network module is connected with the training module and the camera module, and outputs a second traffic sign recognition according to the parameters and the first recognition. Training parameters of the second recognition comprise the traffic sign recognition parameters and the first recognition, the accuracy of the training result being improved by combining other sensors to perform traffic sign recognition training.

Provided is An apparatus for assisting driving of a host vehicle, the apparatus comprising: a camera mounted to the host vehicle and having a field of view in front of the host vehicle, the camera configured to acquire image data; and a controller including a processor configured to process the image data, and configured to receive a map on which the host vehicle travels from a server, compare a speed limit included in the map with a speed limit included in the image data, and control a display of the host vehicle so as to display the speed limit based on the image data.

A method includes determining a target specification map that is associated with a task and that indicates, for each respective region of a plurality of regions around a vehicle equipped with a sensor, a target value of a parameter of the sensor. The method also includes determining a capability specification map that indicates, for each respective region, an attained value of the parameter that the sensor is configured to provide. The method additionally includes comparing the capability specification map to the target specification map to determine, for each respective region, a disparity between the target value and the attained value. The method further includes, based on the comparing, identifying one or more of: a first subset of the plurality of regions where the target value exceeds the attained value or a second subset of the plurality of regions where the attained value meets or exceeds the target value.

Methods described herein relate to disambiguating objects for a particular location. Methods may include receiving, from a vehicle, an indication of an object associated with an object classification for the object in view of the vehicle. The object classification is further associated with an object type. The method further includes receiving, from the vehicle, information pertaining to one or more other objects of the same object type within a predefined distance of the object; determining an expectation count for the object type based at least in part on a count of the object and the one or more objects of the same object type for the particular location; and disambiguating one or more object classifications from a plurality of object classifications based at least in part on the expectation count. A corresponding apparatus and computer program product are also provided.

An adaptive cruise control for a motor vehicle includes a detection module for detecting a vehicle ahead, a production module capable of producing a setpoint, a storage module configured to store a value corresponding to the setpoint, a chronometer configured to be initialized and activated at a moment when the vehicle stops, and a reinitialization module for reinitializing the storage module. This cruise control includes a setup module capable of determining a limit according to the environment of the vehicle. The reinitialization module reinitializes the value stored by the storage module if the chronometer provides a duration greater than the limit.

The disclosure provides a method, a system, and a computer program product for detecting a speed funnel in a region. The method comprises obtaining a plurality of traffic object observations for the region and determining at least one first learned traffic object, based on feature-based clustering of the plurality of traffic object observations. The method also includes generating at least one candidate traffic object group by grouping the at least one first learned traffic object and a second learned traffic object and performing validation of the at least one candidate traffic object group based on a statistical model. The method further includes generating at least one validated candidate traffic object group as a result of the validation of the at least one candidate traffic object group and merging the at least one validated candidate traffic object group with a second validated candidate traffic object group to detect the speed funnel.

An anomaly of a subject can be properly determined even in false image recognition of the subject. An information processing method, performed by at least one processor in an information processing apparatus, comprises acquiring an image including a subject from an imaging apparatus; acquiring respectively items of data from a plurality of sensors provided for the subject or an object near the subject, the items of data being sensed respectively by the plurality of sensors; acquiring a determination result on whether the subject is abnormal or not by inputting the items of data acquired from the plurality of sensors to a learning model utilizing a neural network, wherein the learning model has learned presence or absence of an anomaly in the subject by using respectively past items of data from the plurality of sensors as learning data; and determining whether a recognition result of the subject based on the image is abnormal or not by using the determination result.

An apparatus, method and computer program product are provided for determining a state of visibility of a road object, such as a road sign, using vehicle sensor data. For example, the apparatus determines whether one or more sensors of a first vehicle observes a road sign. If the road sign is not observed by the one or more sensors, the apparatus determines whether one or more second vehicles is obscuring the road sign. If the one or more second vehicles is not obscuring the road sign, the apparatus determines whether the road sign is obscured due to a weather condition. If the road sign is obscured due to the weather condition, the apparatus generates a signal indicating that the road sign was obscured due to the weather condition.

A method and apparatus for generating a direction identifying model, a device, a medium, and a program product are provided. The method includes: acquiring direction-targeted road test data corresponding to a target road, and a guide arrow sign and an accessible road-direction corresponding to the target road; and training a machine learning model by using the road test data and the guide arrow sign as an input of the direction identifying model, and using the accessible road-direction as an output of the direction identifying model, to obtain the direction identifying model.