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Unlike high-end three-dimensional (3-D) scanners with more than 16 layers which are mainly used in academia, low-end 3-D scanners with a few layers are being developed by sensor makers for installation in commercial advanced driver assistance system. The output of a low-end 3-D scanner is completely different from that of a full 3-D scanner and it is rather similar to the output of a 2-D scanner with a single layer. In this paper, a new framework for moving object detection and subsequent tracking using a low-end 3-D scanner with four layers is proposed. The proposed method uses the contours of the objects to obtain a robust association between a detection and a tracking. The proposed method comprises five steps: preprocessing, contour extraction, hypothesis generation, pruning, and moving object detection. In the preprocessing step, outliers, such as the ground or backlights from preceding vehicles, are removed and the scanned points are decomposed into segments, each of which corresponds to a single object. In the track hypothesis generation step, each segment is associated with an existing track maintained over multiple scans. The association method developed here uses the contour shape of the segments and is motivated by the linear programming and dynamic time warping. In the track hypothesis pruning step, unlikely tracks are removed from the hypothesis trees based on the proposed hypothesis scores. In the last step, moving objects are detected based on the track velocity. The proposed method is applied to four challenging real-world scenarios, and its validity is demonstrated via experimentation.

Intelligent vehicle plans motion and navigate itself based on t he surrounding environment perception. Hence, the precise environment recognition is an essential part of self-dr iving vehicle. There exist many vulnerable road users (e.g. vehicle, pedestrians) on vehicular driving environm ent, the vehicle must percept all the dynamic ob-stacles accurately for safety. In this paper, we propose an multiple vehicle tracking algorithm using microwave radar. Our proposed system includes various special features. F irst, exceptional radar measurement model for ve-hicle, concentrated on the corner, is described by mixture dens ity network (MDN), and applied to particle filte r weighting. Also, to conquer the curse of dimensionality of parti cle filter and estimate the time-varying number o fmulti-target states, reversible jump markov chain monte carlo ( RJMCMC) is used to sampling step of the proposed algorithm. The robustness of the proposed algorithm is demonstr ated through several computer simulations.Key Words : Intelligent Vehicle, Multiple Target Tracking (MTT), Markov C hain Monte Carlo (MCMC), Particle Filter, Mixture Density Network (MDN).Received: Jan. 30, 2014Revised : Apr. 14, 2015Accepted: Apr. 14, 2015

Recently, there are many researches on active safety system of intelligent vehicle. To reduce the probability of collision caused by driver's inattention and mistakes, the active safety system gives warning or controls the vehicle toward avoiding collision. For the purpose, it is necessary to recognize and analyze circumstances around. In this paper, we will treat the problem about collision risk assessment. In general, it is difficult to calculate the collision risk before it happens. To consider the uncertainty of the situation, Monte Carlo simulation can be employed. However it takes long computation time and is not suitable for practice. In this paper, we apply neural networks to solve this problem. It efficiently computes the unseen data by training the results of Monte Carlo simulation. Furthermore, we propose the features affects the performance of the assessment. The proposed algorithm is verified by applications in various crash scenarios.Key Words : Collision Risk Assessment, Monte Calro Simulation, Neural Networks, Time-to-Collision(TTC)

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation.