Approximately one-quarter of the total greenhouse gases (GHG) emissions in Europe can be attributed to the transport sector, with petroleum-derived fuels dominating road transport. In the current environmental and economic context, the use of less polluting alternative fuels simultaneously providing security of supply and optimal energy storage is encouraged. Natural gas (NG) technology for transportation is mature and extended through the use of the compressed form in urban and light vehicles. The introduction of liquefied natural gas (LNG) could broaden the use of natural gas for longer distances due to its higher energy density. In addition, the use of LNG in heavy-duty vehicles reduces the GHG emissions per kilometer by up to 20% and eliminates almost 100% of the sulfur oxides and particulate matter while also reducing the noise in inner cities compared with the use of diesel trucks. This paper reviews the key environmental, technical and socioeconomic aspects of LNG deployment as alternative fuel for road freight transport. Although it is necessary to continue research to develop a reliable database to estimate the actual environmental impact of LNG, the main difficulties for its deployment are market-related. From this market perspective, the prospects for LNG introduction in the European scenario have also been analyzed. Ensuring price stability and reducing uncertainty for investments are keys. Steps taken to date for developing an open and transparent international NG market are paving the way. In addition, the installation of new LNG terminals would significantly contribute to the security of supply and meeting diversification targets. Finally, some projections for the LNG implementation in the Spanish road freight transport are introduced, concluding that the fuel switch in long-haul trucks could reduce GHG emissions by 12% and diesel fuel consumption by 42% in the long term.

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Liquefied natural gas: Could it be a reliable option for road freight transport in the EU?

Substitution effect of New-Energy Vehicle Credit Program and Corporate Average Fuel Consumption Regulation for Green-car Subsidy

Assessing the global sustainability of different electricity generation systems

The effect of global oil price shocks on China's metal markets

A hybrid computational approach for detailed scheduling of products in a pipeline with multiple pump stations

When matching the SIFT feature points, there will be lots of mismatches. The RANSAC algorithm can be used to remove the mismatches by finding the transformation matrix of these feature points. But when the data space contains a lot of mismatches, finding the right transformation matrix will be very difficult. What's more, the probability of finding the error model is very large. Aiming at solving the problem, this paper proposed an improved RANSAC algorithm. Before using the RANSAC algorithm, we removed parts of the error feature points by two methods, one is eliminating features not belonging to the target area and the other is removing the crossing points. The two methods aimed to improve the proportion of feature points matched correctly. Experiments showed that, the improved RANSAC algorithm could find the model more accurately, improve efficiency, and make the feature point matching more accurately.

SIFT Feature Point Matching Based on Improved RANSAC Algorithm

Oil-saving pathways until 2030 for road freight transportation in China based on a cost-optimization model

A process-based model for estimating the well-to-tank cost of gasoline and diesel in China

The moving object detection is a prerequisite and difficult point to realize tracking in the video tracking system. In order to detect moving object effectively, an object tracking algorithm is proposed based on combination of dynamic template matching and Kalman filter. First, get the area of the moving object by using inter-frame difference method and extract the SIFT feature points. Then, find the location of the candidate object that is most matched with the object template in the search area by Kalman filter and match it with the object template in the current frame. Finally, the feature points' loss rate will serve as the limited threshold, and we update template according to dynamic template updating strategy. By the number of the frames of the target's matching failures we determine whether the moving target is disappeared. Several experiments of the object tracking show that the approach is accurate and fast, and it has a better robust performance during the attitude changing, the size changing and the shelter instance.

Object Tracking Algorithm Based on Combination of Dynamic Template Matching and Kalman Filter

In an unstructured environment, there are many challenges for obstacle detection. This paper presents an improved method to detect obstacles based on stereo vision in unstructured environments based on salient obstacle extraction. This method can achieve same or higher level of accuracy of obstacle detection compared to the existing salient obstacle detection with significant reduction of computation time. This method consists of two stages. In the first stage, it extracts the salient obstacles which stand out from the background in the stereo images using a fast salient obstacle detection method. In the second stage, it refines the detection of small obstacles by computing the geometric relationships among 3D points using an improved space-variant resolution (SVR) with the continuity and the height constraints. The experiment results show that this improved method can reduce computation time and improve detection accuracy.

Yaping Dai

Papers

SIFT Feature Point Matching Based on Improved RANSAC Algorithm

Oil-saving pathways until 2030 for road freight transportation in China based on a cost-optimization model

A process-based model for estimating the well-to-tank cost of gasoline and diesel in China

Object Tracking Algorithm Based on Combination of Dynamic Template Matching and Kalman Filter

Two-Stage Obstacle Detection Based on Stereo Vision in Unstructured Environment