The major problem in an advanced driver assistance system (ADAS) is the proper use of sensor measurements and recognition of the surrounding environment. To this end, there are several types of sensors to consider, one of which is the laser scanner. In this paper, we propose a method to segment the measurement of the surrounding environment as obtained by a multi-layer laser scanner. In the segmentation, a full set of measurements is decomposed into several segments, each representing a single object. Sometimes a ghost is detected due to the ground or fog, and the ghost has to be eliminated to ensure the stability of the system. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments show that the proposed method demonstrates good performance in many real-life situations.
The advanced driver-assistance system (ADAS) is designed to help drivers while they are driving. To help the drivers, ADAS first comprehends the situation by analyzing the data obtained from the road surroundings. In this process, the road boundary is one of the most important targets to detect for safe driving, but is frequently misdetected on crowded roads. Therefore, a new method for robustly detecting road boundaries on crowded roads is presented in this paper. First, road-boundary detection using a standard Hough transform is described, and its limitations are shown. Second, the cause of the limitations is explained by the measurement model of a laser scanner. Then, the standard Hough transform is modified to reflect the measurement model of the laser scanner; this change reduces the effect of closed obstacles. Finally, the proposed method is tested in the real-world environment, and it shows better performance than previous works in crowded environments.
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.
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