Exploring the Chinese-English Translation of Shanghai Road Signs From the Perspective of Skopos Theory

This paper applies the Skopos theory to discussing the principles of Chinese-English translation of road signs,namely conforming to international conventions and English expression norms,providing information,and showing local culture at the same time.

ROAD SIGNS

As world automakers speed into operations in China,analysts are reminding them to proceed with caution "We have no intention of letting up on the accelerator," Kevin E. Wale, President and Managing Director of the GM China Group, said in early January, discussing the American automaker's plans for China. Similar to GM, China's top-selling foreign automaker, Ford says it is ready to shift its comprehensive expansion in the

Clever Cars Can Read Road Signs

目前澳大利亚的科学家发明了一种电子智能装置,它可以帮助司机发现交通标志并提醒司机注意这些标志。这样一来,车辆驾驶的安全性提高了,司机恐怕也不能在违反交通法规时再借口说没有看到交通标志了。

THE RESEARCH ON NEW REFLECT ROAD MARK COATING

As the developing of the traffic and transport, road signs coating materials become important more and more. This paper introduces the development and current situation of the traffic marking coating and discusses its raw material and application operation. C 5 petroleum resin made in china is used as major material in a hot melt traffic marking coating. To get the best material composition, orthogonal test is used and wear resistant, water resistant, acidity proof, alkaline proof, adhesive strength, fluid characteristic of the coating is researched.

Traffic Sign Recognition for Autonomous Vehicle Using Optimized YOLOv7 and Convolutional Block Attention Module

The infrastructure and construction of roads are crucial for the economic and social development of a region,but traffic-related challenges like accidents and congestion persist.Artificial Intelligence(AI)and Machine Learning(ML)have been used in road infrastructure and construction,particularly with the Internet of Things(IoT)devices.Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing trafficrelated problems.This study aims to use You Only Look Once version 7(YOLOv7),Convolutional Block Attention Module(CBAM),the most optimized object-detection algorithm,to detect and identify traffic signs,and analyze effective combinations of adaptive optimizers like Adaptive Moment estimation(Adam),Root Mean Squared Propagation(RMSprop)and Stochastic Gradient Descent(SGD)with the YOLOv7.Using a portion of German traffic signs for training,the study investigates the feasibility of adopting smaller datasets while maintaining high accuracy.The model proposed in this study not only improves traffic safety by detecting traffic signs but also has the potential to contribute to the rapid development of autonomous vehicle systems.The study results showed an impressive accuracy of 99.7%when using a batch size of 8 and the Adam optimizer.This high level of accuracy demonstrates the effectiveness of the proposed model for the image classification task of traffic sign recognition.

An Investigation of Drivers’ Comprehension of Distracted Driving Slogans

Distracted driving occurs when a driver diverts the primary attention from driving to another task. Using mobile devices such as a cellphone for texting, calls, or other manipulation while driving has the highest potential for distraction because it combines both forms of distractions, manual, visual, and cognitive. Some states in the US have posted slogans including “W8 2 TXT, it’s a law”, “Don’t Drive inTEXTicated”, “PLS dnt txt n drv”, “Don’t tempt F8 that txt can w8”, and “DNT TXT & DRV” along highways to convey the dangers and laws regarding distracted driving to minimize incidences of distracted-related crashes This study surveyed 347 people using the five distraction slogans in a college town. The results showed that younger drivers have a higher level of comprehension compared to older drivers. Further, the results showed that drivers with university education or more years of driving experience have a higher comprehension level of distraction signs compared to their counterparts.

Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imperative requirements for intelligent driving technology. The reliable vehicle ego-localization, including the lane recognition and the vehicle position and attitude estimation, at the complex traffic intersection is significant for the intelligent driving of the vehicle. In this article, we focus on the complex road environment of the city, and propose a pose and position estimation method based on the road sign using only a monocular camera and a common GPS （global positioning system）. Associated with the multi-sensor cascade system, this method can be a stable and reliable alternative when the precision of multi-sensor cascade system decreases. The experimental results show that, within 100 meters distance to the road signs, the pose error is less than 2 degrees, and the position error is less than one meter, which can reach the lane-level positioning accuracy. Through the comparison with the Beidou high-precision positioning system L202, our method is more accurate for detecting which lane the vehicle is driving on.