12 results about "Traffic sign recognition" patented technology

The invention relates to a method for controlling a driver assistance system in a road vehicle (22) having an electronically controllable drive motor (24) and an electronically controllable service brake system having wheel brake cylinders (30, 32, 34, 36) which can be actuated with pressure medium, and having a camera-based vehicle recognition, pedestrian recognition and traffic sign recognition, as well as a speed regulation function and an emergency brake function, wherein, when the road vehicle (22) approaches an identified bus stop or tram stop (2), the controller (26) of the driver assistance system, in addition to generating and outputting a warning signal to the driver of the road vehicle (22), also carries out at least one further measure to avoid a collision with a pedestrian (14.1, 14.2, 14.3). As an additional measure, according to the invention, the wheel brake cylinders (30, 32, 34, 36) of the service brake system of the road vehicle (22) are pre-filled with a predetermined fluid pressure before reaching the bus stop or tram stop (2) in order to prepare for a possible emergency brake. The invention also relates to a device (40) for carrying out the method.

This invention provides a traffic sign recognition method and related apparatus. The method includes: acquiring real-time image data and environmental data collected during vehicle movement; concatenating the preprocessed environmental data into an environmental vector, and generating a modulation vector based on the environmental vector; applying channel attention modulation to a feature map using the modulation vector to obtain a modulated multi-scale image feature map; performing feature recalibration processing on the multi-scale image feature map to obtain a recalibrated feature map; performing traffic sign detection based on the recalibrated feature map and outputting an interpretable detection result for the traffic sign; and performing online incremental learning on the target detection model based on the interpretable detection result according to preset triggering conditions, and collaborating with a cloud server through federated aggregation. Based on this, this invention can intelligently recognize traffic signs, obtain interpretable detection results, and support online incremental learning.

Embodiments of the present application provide a traffic sign recognition method, an electronic device, a storage medium and a program product. The method comprises: obtaining attribute information of multiple traffic signs on the same road from map update data; determining corner point road coordinates and sign face projection areas of the multiple traffic signs in a road coordinate system according to the attribute information; performing clustering processing on the multiple traffic signs according to the corner point road coordinates and the sign face projection areas, to obtain multiple target clustering clusters; for each target clustering cluster, determining a target type of a target traffic sign based on the corner point road coordinates and the sign face projection areas of the target traffic sign included in the target clustering cluster; and the target type is a side-by-side combined type or a composite combined type. The method is used to improve the recognition accuracy of traffic signs.

The invention relates to a vehicle (2) with a system (1) for camera-based traffic sign recognition, in which, during driving operation, an actual image of the traffic sign (B) is displayed by means of a camera (5). ist ) is detectable, wherein the system (1) is based on the detected traffic sign actual image (B ist ) a speed recommendation (v E ) for the driver. According to the invention, the system (1) has an analysis module (13) which, in the event of a driver-initiated speed correction measure that leads to a deviation from the speed recommendation (v E ) differing actual speed (v ist ) performs an error analysis to check whether or not there is a fault in the traffic sign recognition system (1).

This application provides a method, apparatus, control device, storage medium, and product for traffic sign recognition, belonging to the field of artificial intelligence technology. The method first enhances the acquired road image. This enhances the contrast, saturation, and brightness of the area containing the traffic signs in the first road image, creating a strong contrast between the traffic signs and the image background. This facilitates the feature extraction network in capturing effective semantic information, thereby simplifying the recognition of traffic signs in the road image. Then, based on the enhanced road image, a sign recognition model is used for recognition. Since the traffic sign recognition model employs a lightweight convolutional neural network (CNN), which has fewer parameters and lower computational complexity, its operating efficiency and inference speed can be improved, enabling real-time traffic sign recognition and thus enhancing the safety of autonomous driving.

The invention relates to the technical field of machine vision, in particular to a traffic sign automatic identification method and system based on machine vision. After it is detected that a target traffic sign is shielded or damaged, loading multi-source image data of different visual angles and different time, obtaining a preset category label, extracting a sign area image, and obtaining a preliminary feature vector through a feature extraction network; spatial position parameter codes are combined to form position vectors, the position vectors are spliced, and complete feature representation is output through an attention fusion module; inputting a pre-trained condition generation type reconstruction model to generate a non-occlusion complete identification image; and if the reconstruction confidence reaches the standard, outputting a reconstructed image and a category label for traffic decision making. And the integrity, the accuracy and the robustness of traffic sign recognition in a complex road environment are greatly improved.

This invention proposes a traffic sign recognition system and method based on an encoder-decoder anti-perturbation neural network. The traffic sign collection system of this invention acquires traffic sign images and correct labels. An encoder-decoder anti-perturbation traffic sign recognition neural network is constructed. For each traffic sign image and its label, the network calculates the loss between the predicted label and the true label, constructs a loss function model, and optimizes the network to improve its anti-perturbation performance, resulting in a highly robust traffic sign image recognition system. This invention utilizes an encoder-decoder network to embed the semantics of some interfering images into difficult-to-recognize traffic sign images. The network trained on these images can recognize traffic signs that are worn, dirty, or obscured by fog, thus improving the anti-perturbation performance of traffic sign recognition.

The application discloses a method and system for unmanned vehicle to identify traffic signs under extreme weather, wherein the method comprises the following steps: acquiring a traffic sign image to be identified under extreme weather; the extreme weather refers to rainy and snowy weather or fog and haze weather; for the rainy and snowy weather, the traffic sign image to be identified is subjected to rainy and snowy preprocessing; for the fog and haze weather, the traffic sign image to be identified is subjected to fog and haze preprocessing; and the preprocessed image is subjected to identification by using a trained traffic sign identification model to obtain an identified traffic sign.

The application discloses a traffic road sign recognition multi-task model inference optimization method based on operator optimization and related equipment thereof. The method comprises the following steps: acquiring a traffic road condition image and inputting the traffic road condition image into a traffic road sign multi-task recognition model; when a backbone network extracts features of the traffic road condition image, for each type of operator operated by the backbone network, an operator acceleration interface matched with the operator is called to execute the operation of the operator, and a traffic road sign global feature map is obtained; when tasks of each task branch are executed based on the traffic road sign global feature map, for each type of operator of each task branch, a task operator acceleration interface matched with the operator is called to execute the operation of the operator, and a task result of the task branch is output. As can be seen, when the multi-task model performs inference, the operator acceleration interface of the embedded microcontroller is called, so that the embedded microcontroller plays a role in accelerating the inference performance of the traffic road sign recognition multi-task, thereby improving the recognition efficiency of the multi-task model.

The invention relates to a method for adapting the semantic meaning of a traffic sign determined by an ego-vehicle from visual traffic sign recognition and / or from digital map data by using historical behavioral data from a plurality of other vehicles, wherein the behavioral data are determined by means of at least one acquisition unit arranged outside the ego-vehicle over a predetermined period under different conditions and made available to an evaluation unit.wherein, by means of the evaluation unit, a comparison is made between the data from visual traffic sign recognition and the behavioral data of the multitude of other vehicles in the vicinity of a geolocation of the ego-vehicle, and wherein, in the event of a deviation, information for adjusting the semantic meaning determined from visual traffic sign recognition and / or from digital map data is transmitted to a control and / or output device of the ego-vehicle.

The present invention discloses a method and a device for recognizing traffic signs and is related to the field of automotive engineering. The specific implementation of this method comprises: capturing an image containing a traffic sign to be identified; extracting an odd number of feature values ​​greater than 1 from the image, each target number of feature values ​​corresponding to a trained classification model; the target number is equal to the odd number minus 1; inputting each target number of feature values ​​into the corresponding trained classification model; and determining whether the traffic sign to be identified is a standard road sign for vehicles, based on the classification results of each classification model.This implementation method improves the accuracy of traffic sign recognition, avoids traffic accidents due to misinterpretations of traffic signs during autonomous driving, and increases the safety of autonomous driving.

A system, method and vehicle for identifying traffic signs during autonomous driving, comprising a camera module configured to obtain a first traffic sign identification result; a sensor configured to obtain behavior information of the vehicle and nearby vehicles; a training module connected to the sensor, the training module configured to output a traffic sign identification parameter based on the behavior information of the vehicle and the nearby vehicles; and a recurrent neural network module connected to the training module and the camera module, wherein the recurrent neural network module is configured to output a second traffic sign identification result based on the traffic sign identification parameter and the first traffic sign identification result. The training parameters of the second traffic sign identification result include the traffic sign identification parameter and the first traffic sign identification result. By combining other sensors for traffic sign identification training, the accuracy of the second traffic sign identification result is improved.