In this paper, we propose a novel, efficient stereo visual-odometry algorithm for ground vehicles moving in outdoor environments. To avoid the drawbacks of computationally-expensive outlier-removal steps based on random-sample schemes, we use a single-degree-of-freedom kinematic model of the vehicle to initialize an Iterative Closest Point (ICP) algorithm that is utilized to select high-quality inliers. The motion is then computed incrementally from the inliers using a standard linear 3D-to-2D pose-estimation method without any additional batch optimization. The performance of the approach is evaluated against state-of-the-art methods on both synthetic data and publicly-available datasets (e.g., KITTI and Devon Island) collected over several kilometers in both urban environments and challenging off-road terrains. Experiments show that the our algorithm outperforms state-of-the-art approaches in accuracy, runtime, and ease of implementation.

This paper presents a novel method to realize on-board visual odometry system. Vehicular kinematic constrain is used in the motion estimation algorithms. The work is a extension from planar steering model to 3-dof in which vehicle's motion is modeled more reasonable and accurate. By virtue of appropriate simplification, the close-form solution of motion parameters can be obtained only need to find real roots of a cubic equation. Then optimization based refine method can bring the winner solution to accurate solution utilizing inliers founded. The algorithm has been tested on both simulation platform and real car test and achieved promising results.

The 2011 Intelligent Vehicle Future Challenge (11'IVFC) tested self-driving systems in real urban scenarios. The entry of Beijing Institute of Technology: BIT-III finished the 10-kilometer long track in 28 minutes without human operation and obeyed traffic regulations in most circumstances. This paper presented the design and implementation of BIT-III. As a universal self-driving system, BIT-III valued extensibility and featured modularized system architecture. For a better compatibility with diverse sensing devices, BIT-III classified perception to be either OGM (Occupancy Grid Map)-oriented or object-oriented based on the output mode. To work in environments with uncertainties, BIT-III gave first priority to safety and stability in driving, and realized them in the core-level components as the instinct of the system. Even in the unknown environment in the ll'IVFC, BIT-III was able to drive smoothly without crashes.

This paper presents an approach to detect and recognize traffic signs present in the urban scenes in China. The algorithm is composed of three steps that are color segmentation, shape detection and pictogram recognition. In the first step Ridge Regression is used to obtain a precise segmentation in RGB color space and achieves the same good performance as many machine learning based methods while using less computation time. Recognition process include a novel feature extraction involves OTSU method, and the feature extracted is robust against illumination variations and distortions. The algorithm has been run on several thousands of images with promising results.

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.

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. 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; 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 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.