Abstract: A decision generating device is configured to execute the following operations. A driving image of a vehicle and a driving status corresponding to the driving image are obtained. An image recognition is performed on the driving image by using an image recognition model to generate object information of objects in the driving image. A predictive driving information is generated based on the object information and the driving status by using a prediction model, the predictive driving information includes object movement predictions of the objects and a movement prediction of the vehicle, and the prediction model is generated based on a trained generative content model. A driving decision is generated based on the predictive driving information.

Abstract: An image recognition edge device is provided, which includes a memory and a processor. The processor accesses multiple instructions to perform the following operations: downsampling an input image to generate a downsampled image; inputting the downsampled image into an object recognition model through multiple convolutional layers thereby sequentially generating multiple feature tensors; selecting a part of the feature tensors to form a first feature tensor pyramid; selecting another part of the feature tensors to form a second feature tensor pyramid; inputting the first feature tensor pyramid and the second feature tensor pyramid into a combined fully connected layer, generating a first image detection label by a first fully connected output layer based on the first feature tensor pyramid, and generating a second image detection label by the second fully connected output layer based on the second feature tensor pyramid.

Abstract: A decision generating device is configured to execute the following operations. A driving image of a vehicle and a driving status corresponding to the driving image are obtained. An image recognition is performed on the driving image by using an image recognition model to generate object information of objects in the driving image. A predictive driving information is generated based on the object information and the driving status by using a prediction model, the predictive driving information includes object movement predictions of the objects and a movement prediction of the vehicle, and the prediction model is generated based on a trained generative content model. A driving decision is generated based on the predictive driving information.

Abstract: A collision warning system and method are provided. The system includes an image capturing apparatus, a sensing apparatus, and a computing apparatus. The computing apparatus analyzes a vehicle side image to generate a plurality of objects and an object coordinate corresponding to each of the objects. The computing apparatus calculates an aerial view coordinate corresponding to each of the objects based on the object coordinates and a pitch angle corresponding to the image capturing apparatus. The computing apparatus calculates a predicted collision time between the vehicle and each of the objects based on a vehicle speed, a turning angle, and the aerial view coordinates. The computing apparatus generates a plurality of warning regions and determines a warning scope corresponding to each of the warning regions based on the predicted collision times.

Abstract: An image recognition edge device is provided, which includes a memory and a processor. The processor accesses multiple instructions to perform the following operations: downsampling an input image to generate a downsampled image; inputting the downsampled image into an object recognition model through multiple convolutional layers thereby sequentially generating multiple feature tensors; selecting a part of the feature tensors to form a first feature tensor pyramid; selecting another part of the feature tensors to form a second feature tensor pyramid; inputting the first feature tensor pyramid and the second feature tensor pyramid into a combined fully connected layer, generating a first image detection label by a first fully connected output layer based on the first feature tensor pyramid, and generating a second image detection label by the second fully connected output layer based on the second feature tensor pyramid.

Abstract: A map scanning method including the following step: scanning, by a scanning device, a location area of a vehicle to obtain location data, wherein the scanning device includes a lidar sensor, and the lidar sensor is configured to scan the location area to obtain a scan point cloud data of the location data; comparing, by a processor, the location data with a basic map to obtain a first point cloud area corresponding to the location data in the basic map; Comparing the scan point cloud data with the first point cloud area to calculate a matching degree value; and when the matching degree value is less than an update threshold, updating the first point cloud area according to the scan point cloud data.

Abstract: A collision warning system and method are provided. The system includes an image capturing apparatus, a sensing apparatus, and a computing apparatus. The computing apparatus analyzes a vehicle side image to generate a plurality of objects and an object coordinate corresponding to each of the objects. The computing apparatus calculates an aerial view coordinate corresponding to each of the objects based on the object coordinates and a pitch angle corresponding to the image capturing apparatus. The computing apparatus calculates a predicted collision time between the vehicle and each of the objects based on a vehicle speed, a turning angle, and the aerial view coordinates. The computing apparatus generates a plurality of warning regions and determines a warning scope corresponding to each of the warning regions based on the predicted collision times.

Abstract: A map scanning method including the following step: scanning, by a scanning device, a location area of a vehicle to obtain location data, wherein the scanning device includes a lidar sensor, and the lidar sensor is configured to scan the location area to obtain a scan point cloud data of the location data; comparing, by a processor, the location data with a basic map to obtain a first point cloud area corresponding to the location data in the basic map; Comparing the scan point cloud data with the first point cloud area to calculate a matching degree value; and when the matching degree value is less than an update threshold, updating the first point cloud area according to the scan point cloud data.

Abstract: A system and method for generating basic information for positioning and a self-positioning determination device are disclosed. The system includes an information supplying device and a computing device. The information supplying device recognizes an object position and an object category of each of a plurality of reference objects and accordingly generates a reference unique feature value for each reference object, and the computing device generates basic information for positioning according to the reference unique feature values. The self-positioning determination device recognizes an object position and an object category of a current object and accordingly generates a current unique feature value for the current object, and determines a position of itself according to the current unique feature value, the basic information for positioning, and a distance and an angle between the self-positioning determination device and the current object.

Abstract: The present disclosure relates to an imaging system and a detection method. The detection method includes the following steps. Receiving, by a processing unit of the image system, multiple recognition label data sets transmitted from multiple terminal devices. Determining a matching degree value between the recognition label data sets and an image data, and obtaining multiple weight values from a storage unit corresponding to the terminal devices. Setting the weight values and the corresponding matching degree values as multiple label points, and classifying the marker points into multiple cluster groups by a clustering algorithm. Calculating a centroid of the largest cluster group. The centroid of the largest cluster group corresponds to a clustering weight value and a clustering matching value. When the clustering weight value or the clustering matching value meets an adjustment condition, adjusting a neural network unit according to the largest cluster group.

Abstract: A system and method for generating basic information for positioning and a self-positioning determination device are disclosed. The system includes an information supplying device and a computing device. The information supplying device recognizes an object position and an object category of each of a plurality of reference objects and accordingly generates a reference unique feature value for each reference object, and the computing device generates basic information for positioning according to the reference unique feature values. The self-positioning determination device recognizes an object position and an object category of a current object and accordingly generates a current unique feature value for the current object, and determines a position of itself according to the current unique feature value, the basic information for positioning, and a distance and an angle between the self-positioning determination device and the current object.

Abstract: The present disclosure relates to an imaging system and a detection method. The detection method includes the following steps. Receiving, by a processing unit of the image system, multiple recognition label data sets transmitted from multiple terminal devices. Determining a matching degree value between the recognition label data sets and an image data, and obtaining multiple weight values from a storage unit corresponding to the terminal devices. Setting the weight values and the corresponding matching degree values as multiple label points, and classifying the marker points into multiple cluster groups by a clustering algorithm. Calculating a centroid of the largest cluster group. The centroid of the largest cluster group corresponds to a clustering weight value and a clustering matching value. When the clustering weight value or the clustering matching value meets an adjustment condition, adjusting a neural network unit according to the largest cluster group.