A hybrid aggregation and compression technique for road network databases

Vector data and in particular road networks are being queried, hosted and processed in many application domains such as in mobile computing. Many client systems such as PDAs would prefer to receive the query results in unrasterized format without introducing an overhead on overall system performance and result size. While several general vector data compression schemes have been studied by different communities, we propose a novel approach in vector data compression which is easily integrated within a geospatial query processing system. It uses line aggregation to reduce the number of relevant tuples and Huffman compression to achieve a multi-resolution compressed representation of a road network database. Our experiments performed on an end-to-end prototype verify that our approach exhibits fast query processing on both client and server sides as well as high compression ratio.

Detection and classification of road signs in natural environments

An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies the detected road signs. This paper presents an automatic neural-network-based road sign recognition system. First, a study of the existing road sign recognition research is presented. In this study, the issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign recognition problem are reviewed, and a comparison of the features of these methods is given. Second, the developed road sign recognition system is described. The system is capable of analysing live colour road scene images, detecting multiple road signs within each image, and classifying the type of road signs detected. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space, and then detects road signs using a Multi-layer Perceptron neural-network. The classification module determines the type of detected road signs using a series of one to one architectural Multi-layer Perceptron neural networks. Two sets of classifiers are trained using the Resillient-Backpropagation and Scaled-Conjugate-Gradient algorithms. The two modules of the system are evaluated individually first. Then the system is tested as a whole. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 95.96% using the scaled-conjugate-gradient trained classifiers.     

一种适合于移动定位的路径搜索算法

随着3G时代的来临,在快速发展LBS业务中,一个重要功能是获得手机与手机之间或手机到地图上指定位置的最短路径.通过将路网划分成主干网与支路网,并且使用二级R*树来索引路网边,给出了一种获取手机周边道路与计算最短路径的算法.分析了该算法的复杂度,在适当划分主干网与支路网后,该算法可以缩小搜索空间,提高搜索性能.     

基于标值点过程的遥感影像道路网络提取

针对高分辨率遥感影像道路网络的特点,采用基于贝叶斯理论的全自动方法从遥感影像中提取道路.根据道路的局部和全局特征,使用标值点过程对道路建模,采用结合可逆跳跃马尔可夫链蒙特卡罗算法的模拟退火算法优化求得全局最优解.提出新的预处理方法得到道路的位置和方向信息,提出基于预处理的生灭转移核以降低算法的搜索空间,提出基于连接的移动转移核以加快算法的收敛速度.实验结果表明.该方法可以快速、有效地从不同的遥感影像(光学、SAR)提取道路网络.     

Dual geometric perception for cross-domain road segmentation

Road segmentation plays an important role in navigation systems and autonomous driving. However, many methods in road segmentation are based on supervised learning and suffer from performance degradation in the real world. There is a certain domain gap (distribution shift problem) between the source domain (training data) and the target domain (testing data). In this paper, we propose a Dual-Geometric Perception (DGP) approach for cross-domain road segmentation, which jointly uses semantic and dual-geometric information to learn the domain-invariant feature for road segmentation. First, we propose an RGB-N dual stream network structure, which effectively fuses normal vector information and RGB information to reduce domain gap. Moreover, a dual geometric adversarial learning strategy is proposed to utilize depth-aware and normal vector features to perform better domain alignment. Furthermore, a self-training learning strategy is used to further improve the model’s generalizability in the target domain. Extensive experiments demonstrate that our proposed DGP achieves superior performance on lane-to-lane and lane-to-sidewalk road domain adaptation tasks.     

Traffic accidents involving fatigue driving and their extent of casualties

The rapid progress of motorization has increased the number of traffic-related casualties. Although fatigue driving is a major cause of traffic accidents, the public remains not rather aware of its potential harmfulness. Fatigue driving has been termed as a “silent killer.” Thus, a thorough study of traffic accidents and the risk factors associated with fatigue-related casualties is of utmost importance. In this study, we analyze traffic accident data for the period 2006–2010 in Guangdong Province, China. The study data were extracted from the traffic accident database of China's Public Security Department. A logistic regression model is used to assess the effect of driver characteristics, type of vehicles, road conditions, and environmental factors on fatigue-related traffic accident occurrence and severity. On the one hand, male drivers, trucks, driving during midnight to dawn, and morning rush hours are identified as risk factors of fatigue-related crashes but do not necessarily result in severe casualties. Driving at night without street-lights contributes to fatigue-related crashes and severe casualties. On the other hand, while factors such as less experienced drivers, unsafe vehicle status, slippery roads, driving at night with street-lights, and weekends do not have significant effect on fatigue-related crashes, yet accidents associated with these factors are likely to have severe casualties. The empirical results of the present study have important policy implications on the reduction of fatigue-related crashes as well as their severity.     

Factors contributing to young moped rider accidents in Denmark

Young road users still constitute a high-risk group with regard to road traffic accidents. The crash rate of a moped is four times greater than that of a motorcycle, and the likelihood of being injured in a road traffic accident is 10–20 times higher among moped riders compared to car drivers. Nevertheless, research on the behaviour and accident involvement of young moped riders remains sparse.Based on analysis of 128 accident protocols, the purpose of this study was to increase knowledge about moped accidents. The study was performed in Denmark involving riders aged 16 or 17. A distinction was made between accident factors related to (1) the road and its surroundings, (2) the vehicle, and (3) the reported behaviour and condition of the road user. Thirteen accident factors were identified with the majority concerning the reported behaviour and condition of the road user. The average number of accident factors assigned per accident was 2.7. Riding speed was assigned in 45% of the accidents which made it the most frequently assigned factor on the part of the moped rider followed by attention errors (42%), a tuned up moped (29%) and position on the road (14%). For the other parties involved, attention error (52%) was the most frequently assigned accident factor. The majority (78%) of the accidents involved road rule breaching on the part of the moped rider.The results indicate that preventive measures should aim to eliminate violations and increase anticipatory skills among moped riders and awareness of mopeds among other road users. Due to their young age the effect of such measures could be enhanced by infrastructural measures facilitating safe interaction between mopeds and other road users.     

基于随机前沿模型对"一带一路"沿线国家电力投资潜力研究

"一带一路"能源安全合作背景下,基于随机前沿模型,测算并分析了2003—2014年中国对"一带一路"沿线国家能源电力投资效率和潜力。研究表明:中国对"一带一路"沿线国家的电力投资效率比较显著,而且呈逐年递增的趋势,其投资潜力能够得到较好的实现,但高投资效率的国家较少,特别是亚洲地区国家,电力投资效率都不超过0.8,说明"一带一路"能源电力投资潜力巨大。     

A real-time versatile roadway path extraction and tracking on an FPGA platform

This paper presents an algorithm for roadway path extraction and tracking and its implementation in a Field Programmable Gate Array (FPGA) device. The implementation is particularly suitable for use as a core component of a Lane Departure Warning (LDW) system, which requires high-performance digital image processing as well as low-cost semiconductor devices, appropriate for the high volume production of the automotive market. The FPGA technology proved to be a proper platform to meet these two contrasting requirements. The proposed algorithm is specifically designed to be completely embedded in FPGA hardware to process wide VGA resolution video sequences at 30 frames per second. The main contributions of this work lie in (i) the proper selection, customization and integration of the main functions for road extraction and tracking to cope with the addressed application, and (ii) the subsequent FPGA hardware implementation as a modular architecture of specialized blocks. Experiments on real road scenario video sequences running on the FPGA device illustrate the good performance of the proposed system prototype and its ability to adapt to varying common roadway conditions, without the need for a per-installation calibration procedure.     

On-line road boundary modeling with multiple sensory features, flexible road model, and particle filter

This paper describes a method of robustly modeling road boundaries on-line for autonomous navigation. Since sensory evidence for road boundaries might change from place to place, we cannot depend on a single cue but have to use multiple sensory features. It is also necessary to cope with various road shapes and road type changes. These requirements are naturally met in the proposed particle filter-based method, which makes use of multiple features with the corresponding likelihood functions and keeps multiple road hypotheses as particles. The proposed method has been successfully applied to various road scenes with cameras and a laser range finder. To show that the proposed method is applicable to other sensors, preliminary results of using stereo instead of the laser range finder are also described.