Active perception system for double-axle steering cab-less mining vehicle

An active perception system for a double-axle steering cab-less mining vehicle, the system comprising:

• at least one processor, a memory storing instructions, when executed by the at least one processor, causing the at least one processor to perform operations comprising:
• building a three-dimensional (3D) perception map and extracting a region of interest (ROI) by marking an unstructured road boundary;
• detecting single-frame data by Euclidean clustering-based millimeter wave radar detection, depth learning algorithm-based Lidar detection and depth learning algorithm-based camera detection, comprising:
• receiving point cloud information of a ROI and speed, inclination and steering angle of the vehicle, and obtaining data output by a millimeter wave radar, a Lidar and a camera;
• determining a rotation angle of each sensor according to the steering angle information of the vehicle combined with the point cloud information of the ROI and controlling the rotation angle of each sensor, and determining the rotation angle of each sensor according to an output value of a slope sensor when a vehicle body is tilted or a center of gravity shifts and compensating a visual field blind area caused by a pitch angle of the vehicle;
• performing Euclidean clustering on a point cloud on a road after ground information is removed from the ROI,
• wherein performing Euclidean clustering on the point cloud on the road comprises:
• randomly selecting target points, and
• when a distance between the target points is less than a threshold, putting the target points into a cluster until there are no points with a distance less than the threshold, and outputting a clustering result;
• cutting an original point cloud into grids from a perspective of a top view, pillarizing a 3D point cloud, representing a point cloud in each Pillar by a 9-dimensional vector, inputting the point cloud into a PointNet feature extraction network to generate a two-dimensional (2D) pseudo-image, inputting the pseudo-image into a 2D convolutional neural network (CNN) to further extract point cloud features, performing BoundingBox regression through a single-shot detector (SSD) head and outputting a position and orientation of the obstacle;
• and
• enhancing an image using Mosaic data, changing a size and position of a truth value target, inputting the enhanced image into the network, unifying the size of the image, extracting deep semantic information and shallow spatial information of the image through a residual network, and detecting a category and position of the target;
• predicting a risk index of each detection target by calculating an information entropy of each sensor and acquiring indicators comprising a mark, speed and distance of the target, and determining a state of an obstacle after the risk index feeds back on the information entropy;
• receiving perceived environmental data;
• determining a new speed, inclination and steering angle of the vehicle through a corresponding planning algorithm for vehicle control, and feeding back the new speed, inclination and steering angle of the vehicle to control a rotation direction of each sensor adaptively, so as to increase an effective perception and detection range;
• and
• actively adjusting the vehicle to a forward advancing mode and a backward advancing mode according to a driving mode and comprehensive information of each sensor.