Estimation of tire-road peak adhesion coefficient for intelligent electric vehicles based on camera and tire dynamics information fusion

The tire-road peak adhesion coefficient (TRPAC) describes the tire adhesion limit that a road can provide. The TRPAC is a key parameter for precise vehicle motion control and an important basis for decision-making and planning of intelligent vehicles. Considering the critical and difficult problems in the estimation of TRPAC, such as slow convergence and low accuracy, a TRPAC estimation method based on the fusion of vehicle dynamics and machine vision is proposed in this paper. Based on the observability theory of nonlinear systems, local weak observability of the dynamics-based estimator is analyzed to explain the limitation of a single dynamics-based estimator. The framework of dynamics-image-based fusion estimator is then proposed, including the fusion of data, model and decision levels. A dynamics-based fusion estimator is designed by considering the coupling relationship of longitudinal and lateral tire forces to adapt the conditions of complex excitations. Start-and-stop strategy for the dynamics-based fusion estimator is designed by setting excitation thresholds for different types of road surfaces, which are identified using vision information. Parameter self-tuning for the dynamics-based fusion estimator based on the image-based estimator is proposed to improve convergence speed and reduce oscillation. The results of the simulation and vehicle test show that the road estimation error of the proposed method is within 0.03 and the convergence time is within 0.5 s. Compared with other existing estimators, the fusion estimator achieved better accuracy, sensitivity and stability, particularly when complex excitations were present. 

Tire-Road Peak Adhesion Coefficient Estimation Method Based on Fusion of Vehicle Dynamics and Machine Vision

The embodiment of the invention discloses an information output method and device for an unmanned vehicle. The unmanned vehicle comprises a laser radar, and a specific embodiment of the method comprises the steps of determining a target area around the unmanned vehicle from a target map based on positioning information of the unmanned vehicle; acquiring point cloud data of obstacles in a preset area around the unmanned vehicle through the laser radar, and the preset area comprises the target area; based on the target area and the point cloud data, determining a point cloud data set of an obstacle existing in the target area, and determining the point cloud data set as a target point cloud data set; and outputting the target point cloud data set. According to the embodiment, the utilizationrate of the vehicle-mounted terminal processor is reduced, and the safety of vehicle driving is improved.

information output method and device for an unmanned vehicle

An embodiment of the invention provides a method for generating a grid map used for unmanned vehicle navigation control, and belongs to the field of automatic driving. The method comprises the steps of performing segmentation clustering on original point clouds according to the positions of the original point clouds, and dividing the original point clouds into multiple blocks; extracting feature data of each block in the blocks; and according to the feature data of each block, classifying point clouds in each block, and projecting classified point cloud data to the grid map. According to the method, regions occupied by barriers can be marked on the map accurately, and the type of the barrier in each region of the map can be marked.

Method for generating grid map used for unmanned vehicle navigation control

The invention discloses a moving object real-time detection algorithm based on a three-dimensional laser point cloud. The moving object real-time detection algorithm comprises the steps of: (1) outputting position information of points scanned in an FOV space by means of a three-dimensional laser sensor, and converting all the output data into a standard point cloud data format; (2) recording a plurality of frames of original point cloud data, and accumulating the plurality of frames of original point cloud data to obtain a piece of background point cloud data; (3) acquiring real-time point cloud data according to the original point cloud data; (4) comparing the real-time point cloud data with the background point cloud data to obtain differential point cloud data; (5) and assembling the differential point cloud data, and carrying out clustering processing according to distances among points separately to obtain a plurality of sets of clustering points, wherein each set represents a moving object. The moving object real-time detection algorithm can obtain real-time information of the moving objects in the FOV space of the laser sensor precisely.

Moving object real-time detection algorithm based on three-dimensional laser point cloud

The embodiment of the invention discloses a multiline laser radar vehicle object recognition method and a vehicle anti-collision device. A laser radar is a multiline laser radar, and the method comprises the steps: obtaining the detection data of the multiline laser radar, wherein the detection data comprises the coordinate data of a plurality of point clouds of at least two scanning layers; carrying out the clustering of the plurality of point clouds based on the coordinate data of the plurality of point clouds through building a grid map, so as to enable the plurality of point clouds to be classified in at least one class; carrying out the fitting of the coordinate data of the plurality of point clouds of each scanning layer forming any object to form a line; extracting the features of each scanning layer forming each object, and extracting the features of the line; recognizing whether the object is a vehicle or not based on the extracted features through a machine learning method: transmitting alarm information if the object is the vehicle. According to the embodiment of the invention, the method determines the shape and class of the object nearby the vehicle through the detection data of the multiline laser radar, so as to take an effective driving strategy and safety measures, and to avoid the damage and loss of the persons and properties inside and outside the vehicle.

Multiline laser radar vehicle object recognition method and vehicle anti-collision device

The type of road terrain is an important piece of environment perception information for smart vehicles. In this paper, an approach to recognize the road terrain by using road surface images is proposed. The texture features were extracted from images captured by a camera for the road terrain classification task. The support vector machine method was selected for terrain classification. The simulation experiments indicated that this approach worked very well on terrains with distinguished texture. However, this approach could not recognize the road terrain when the damaged captured image changed frequently due to the luminance or when the moving speed of the vehicle was too fast.

Image Feature Based Machine Learning Approach for Road Terrain Classification

To understand one of the autonomous tasks for self-driving car is to classify the vehicles. This paper presents a pipeline of Lidar point data's clustering and classifying, basically conducted by combining the grid-occupy segmentation and Support Vector Machine learning method. To improve the classification, a novel feature related to vehicle's plate intensity characteristic is investigated. After point cloud clustering, different classifiers were utilized to evaluate the accuracy and our proposed method produced better performance.

Improving Vehicle Detection in Point Cloud Data with Novel Features

One of the motivations of OptoBot Lab is to train primer students into qualified engineers or researchers. The series training programs have been designed by supervisors and implemented with tutoring for students to test and practice their knowledge from textbooks. An environment perception experiment using a 32 layers laser scanner is described in this paper. The training program design and laboratory operation is introduced. The four parts of the experiments which are preparation, sensor calibration, 3D space reconstruction, and object recognition, are the participating students’ main tasks for different teams. This entire program is one of the series training programs that play significant role in establishing solid research skill foundation for opto-electronic students.

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An optoelectronic detecting based environment perception experiment for primer students using multiple-layer laser scanner

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Multiline laser radar vehicle object recognition method and vehicle anti-collision device

Image Feature Based Machine Learning Approach for Road Terrain Classification

Improving Vehicle Detection in Point Cloud Data with Novel Features

An optoelectronic detecting based environment perception experiment for primer students using multiple-layer laser scanner