Jianwei Gong

A novel lane detection based on geometrical model and Gabor filter

Many people die each year in the world in single vehicle roadway departure crashes caused by driver inattention, especially on the freeway. Lane Departure Warning System (LDWS) is a useful system to avoid those accident, in which, the lane detection is a key issue. In this paper, after a brief overview of existing methods, we present a robust lane detection algorithm based on geometrical model and Gabor filter. This algorithm is based on two assumptions: the road in front of vehicle is approximately planar and marked which are often correct on the highway and freeway where most lane departure accidents happen [1]. The lane geometrical model we build in this paper contains four parameters which are starting position, lane original orientation, lane width and lane curvature. The algorithm is composed of three stages: the first stage is called off-line calibration which just runs once after the camera is mounted and fixed in the vehicle. The parameters of camera used for lane detection is accurately estimated by the 2D calibration method [2]; The second stage is called lane model parameters estimation and lane model candidates construction, the first three parameters, starting position, lane original orientation and lane width will be estimated using dominant orientation estimation [3] and local Hough transform. Then the construction of lane model candidates is implemented for the final lane model matching; the third stage is model matching. The proposed lane module matching algorithm is implemented to match the best fitted lane model. The combination of these modules can overcome the universal lane detection problems due to inaccuracies in edge detection such as shadow of tree and passengers on the road. Experimental results on real road will be presented to prove the effectiveness of the proposed lane detection algorithm.

The recognition and tracking of traffic lights based on color segmentation and CAMSHIFT for intelligent vehicles

The recognition and tracking of traffic lights for intelligent vehicles based on a vehicle-mounted camera are studied in this paper. The candidate region of the traffic light is extracted using the threshold segmentation method and the morphological operation. Then, the recognition algorithm of the traffic light based on machine learning is employed. To avoid false negatives and tracking loss, the target tracking algorithm CAMSHIFT (Continuously Adaptive Mean Shift), which uses the color histogram as the target model, is adopted. In addition to traffic signal pre-processing and the recognition method of learning, the initialization problem of the search window of CAMSHIFT algorithm is resolved. Moreover, the window setting method is used to shorten the processing time of the global HSV color space conversion. The real vehicle experiments validate the performance of the presented approach.

Road detection using support vector machine based on online learning and evaluation

Road detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for front-view road detection. Specifically, we propose using Support Vector Machines (SVM) for road detection and effective approach for self-supervised online learning. The proposed road detection algorithm is capable of automatically updating the training data for online training which reduces the possibility of misclassifying road and non-road classes and improves the adaptability of the road detection algorithm. The algorithm presented here can also be seen as a novel framework for self-supervised online learning in the application of classification-based road detection algorithm on intelligent vehicle.

A GA Based Combinatorial Auction Algorithm for Multi-Robot Cooperative Hunting

How to make a trade off between the security of sys- tems and the usability of users is an important issue in network security configuration. To resolve this problem, an optimization method of security configuration based on game theory is proposed. Firstly, a security configuration model based on non-cooperative game is built which in- fers the optimal strategy of systems and users respectively by calculating their strategies and incentives. Secondly, to optimize security configuration further, it cooperatively optimizes the individual optimal strategy by cooperative game, thus eliminating the cases that individual optimal is not the overall optimal. An illustrated experiment shows that this method can coordinate the security of network systems and usability of users so that the security configu- ration of system is optimized magnificently.In order to improve the hunting efficiency of multi- robot cooperative hunting in complicated environment: multi-target and dynamic continues surrounding, a combinatorial auction model based on genetic algorithm (GACA) was presented in this paper. The model adopted genetic algorithm to solve the winner determination problem in combinatorial auction. We also compared the combinatorial auction model based task allocation method with the traditional single item auction model in solving dynamic and complex task allocation problem in multi-robot cooperation. The simulation experiments were conducted in a self- developed visible multi-robot simulation platform, OpenSim, and the results showed the whole process of hunting was very smooth, and the cost time cost by our algorithm was much shorter than the compared method.

High Speed Lane Recognition under Complex Road Conditions

To improve the speed and stability of lanes recognition under complex road conditions, a rapid detection algorithm combined with dynamic window and prior knowledge is proposed. The method of grid is used to segment initial image and define the region of interest (ROI), then all the pixels are eliminated except those on the intersection of the grid lines, thus feature pixels of the lane edge in these intersections will be detected and can be used to generate some dynamic windows with the dilatation algorithm. Then, images are processed in these dynamic windows to obtain lane edge feature. Slope and intercept of the lane can be attained by Hough transformation. To obtain the correct lane information, the information obtained in current processing cycle will be judged and filtered by the prior knowledge. Experiments in structured road showed that the speed of image processing was about 22ms/frame and the proposed algorithm could meet the real-time and stability requirements of high-speed vehicle vision navigation system.

An iterative linear quadratic regulator based trajectory tracking controller for wheeled mobile robot

We present an iterative linear quadratic regulator (ILQR) method for trajectory tracking control of a wheeled mobile robot system. The proposed scheme involves a kinematic model linearization technique, a global trajectory generation algorithm, and trajectory tracking controller design. A lattice planner, which searches over a 3D (x, y, θ) configuration space, is adopted to generate the global trajectory. The ILQR method is used to design a local trajectory tracking controller. The effectiveness of the proposed method is demonstrated in simulation and experiment with a significantly asymmetric differential drive robot. The performance of the local controller is analyzed and compared with that of the existing linear quadratic regulator (LQR) method. According to the experiments, the new controller improves the control sequences (ν, ω) iteratively and produces slightly better results. Specifically, two trajectories, ‘S’ and ‘8’ courses, are followed with sufficient accuracy using the proposed controller.

Hybrid Map-Based Navigation Method for Unmanned Ground Vehicle in Urban Scenario

To reduce the data size of metric map and map matching computational cost in unmanned ground vehicle self-driving navigation in urban scenarios, a metric-topological hybrid map navigation system is proposed in this paper. According to the different positioning accuracy requirements, urban areas are divided into strong constraint (SC) areas, such as roads with lanes, and loose constraint (LC) areas, such as intersections and open areas. As direction of the self-driving vehicle is provided by traffic lanes and global waypoints in the road network, a simple topological map is fit for the navigation in the SC areas. While in the LC areas, the navigation of the self-driving vehicle mainly relies on the positioning information. Simultaneous localization and mapping technology is used to provide a detailed metric map in the LC areas, and a window constraint Markov localization algorithm is introduced to achieve accurate position using laser scanner. Furthermore, the real-time performance of the Markov algorithm is enhanced by using a constraint window to restrict the size of the state space. By registering the metric maps into the road network, a hybrid map of the urban scenario can be constructed. Real unmanned vehicle mapping and navigation tests demonstrated the capabilities of the proposed method.

Color rank and census transforms using perceptual color contrast

Rank and census transforms provide high resistance to radiometric distortion, vignette, and noise because they are based on the relative ordering of local pixel intensity values rather than the pixel values themselves. These transforms are widely used in many computer vision applications. An important step of computing these transforms is to compare or rank two grayscale values, which is very much like measuring color difference in color image. Color difference between two color points at any part of a uniform color space corresponds to the perceptual difference between the two colors by the human vision system. Based on this idea, we propose to use perceptual color contrast to implement color rank and census transforms and achieve this without significantly increasing the amount of data to process and without complicated computations. Furthermore, we demonstrate the feasibility of using these new transforms to find correspondences for stereo vision.

Development and Implementation of Remote Control System for An Unmanned Heavy Tracked Vehicle

A remote control system for a unmanned heavy tracked vehicle was developed. Autonomous driving system is accomplished by rebuilding of the original turning accessories. A friendly human-machine interactive interface was designed to control the vehicle easily. In order to follow desired path, a control strategy was proposed, in which the local autonomous and remote control modes are combined together. A teaching playback method based on preview heading involving with the location modification made by remote operator was presented to reduce the path deviation. Experiments show that this system is able to perform the outdoor task with the proposed control strategy.

Robotic wheeled vehicle ripple tentacles motion planning method

This paper describes a nonholonomic robotic wheeled vehicle ripple tentacle motion planning method, aiming to improve the vehicles trajectory smoothness and avoid frequent weight parameters adjustment in different environments. In the regular tentacle motion planning algorithm, the planning result is selected among the drivable tentacles using a weighted sum cost function. Though the method is simple and easy to understand, it is difficult to adjust the weighted coefficients in different environments. To solve this problem, a geometrical ripple tentacles technique is used to choose a tentacle as a sub-optimal path. Compared with the regular tentacles algorithm, the proposed ripple tentacle algorithm can get a better performance in vehicles trajectory smoothness with an acceptable runtime expense. And another two traits can also distinguish this method: (a) it can avoid weight parameter adjustment in different environments and varied vehicles states, and (b) it can be used in both unknown environment and partly known environment with goal point and global reference path. In the totally unknown environment, it acts as a pure obstacle avoidance algorithm, and when there is a global path, it can follow the reference path and avoid hazards simultaneously.