Yiming Nie

Ribbon Model based path tracking method for autonomous land vehicle

To address the path tracking problem of autonomous land vehicle, a new vehicle-road model named “Ribbon Model” is constructed under the constraints of road width and vehicle geometry structure. A new vehicle-road evaluation algorithm is developed based on this model, and new path tracking controller is designed. The difficulties of preview distance selection and parameters tuning with speed of pure following controller are avoided in this controller. Performance of the novel method is verified by simulation and vehicle experiments.

Negative obstacle detection from image sequences

Negative obstacle detection has been a challenging topic. In the previous researches, the distance that negative obstacles can be detected is so near that vehicles have to travel at a very low speed. In this paper, a negative obstacle detection algorithm from image sequences is proposed. When negative obstacles are far from the vehicle, color appearance models are used as the cues of detecting negative obstacles, while negative obstacles get closer, geometrical cues are extracted from stereo vision. Furthermore, different cues are combined in a Bayesian framework to detect obstacles in image sequences. Massive experiments show that the proposed negative obstacle detection algorithm is quite effective. The alarming distance for 0.8 m width negative obstacle is 18m, and the confirming distance is 10 m. This supplies more space for vehicles to slow down and avoid obstacles. Then, the security of the UGV running in the field can be improved remarkably.

Multi-lane detection based on multiple vanishing points detection

Lane detection plays a significant role in Advanced Driver Assistance Systems (ADAS) for intelligent vehicles. In this paper we present a multi-lane detection method based on multiple vanishing points detection. A new multi-lane model assumes that a single lane, which has two approximately parallel boundaries, may not parallel to others on road plane. Non-parallel lanes associate with different vanishing points. A biological plausibility model is used to detect multiple vanishing points and fit lane model. Experimental results show that the proposed method can detect both parallel lanes and non-parallel lanes.

Fast binocular vision with filters

Trough binocular vision the fast disparity image is often desired for many applications, but, most algorithms could not easily be application because of complexity. We present an image-processing technique that can fast estimate depth image from binocular vision images. By finding out the lines which present the best matched area in the disparity space image, the depth can be estimated. When detecting these lines, an edge-emphasizing filter is used. The final depth estimation will be presented after the smooth filter. Our method is a compromise between local methods and global optimization.

Vertical Edge Depth Estimation

Through binocular vision the fast disparity image is often desired for many applications, but, stereo vision still is an on studying subject. It is hard to get the precisely result because of the complex of the vision task. Vertical edges of depth images are presented much information of the stereo scene for further image processing. We present an image processing technique that can fast estimate vertical edges depth from binocular vision images. By finding out meaningful dots which present the best matched edges in the disparity space image, the depth can be estimated. When detecting these dots, a cross-emphasizing filter is used. The final depth estimation will be presented after belief propagation.Our method is a compromise between local methods and global optimization.