Lateral stability analysis of the tractor/full trailer combination vehicle
This paper presents a linear dynamics model of tractor/full trailer with six degrees of freedom, and a series of simulations are performed on obstacle avoidance manoeuvers in which the combination vehicle moves at a constant forward speed. It is found that the ratio of the peak lateral acceleration of the full trailer's center of gravity (CG) to that of the tractor is 2.3, meanwhile, the articulated angle of the trailer is larger than that of the dolly. This phenomena, defined as the ‘rearward amplification ratio’(RWA), leads vehicle to rollover easily. A larger wheelbase of trailer would be useful to improve the lateral stability of the combination vehicle but it is limited by width of the road. Acting a resistive torque on the turntable can be absolutely effective in improving the dynamics stability of the tractor/full trailer combination vehicle but if acted at hitch point it has little improvement.
An overview of truck and bus safety: 1999 Knoxville symposium
This paper presents a summary of the topics and discussions from the Second International Truck and Bus Safety Symposium held in Knoxville, Tennessee, October 6-8, 1999. This paper is not a technical paper but is based on the symposium proceedings, which can be accessed for more technical information. The symposium provided an opportunity for the many stakeholders in truck and bus safety, such as carriers, shippers, inspectors, law enforcement officials, insurers, regulators, manufacturers and researchers, to network and discuss the key issues associated with truck and bus safety.
Integrating Local CNN and Global CNN for Script Identification in Natural Scene Images
Script identification in natural scene images is a key pre-step for text recognition and is also an indispensable condition for automatic text understanding systems that are designed for multi-language environments. In this paper, we present a novel framework integrating Local CNN and Global CNN both of which are based on ResNet-20 for script identification. We first obtain a lot of patches and segmented images based on the aspect ratios of the images. Subsequently, these patches and segmented images are used as inputs to Local CNN and Global CNN for training, respectively. Finally, to get the final results, the Adaboost algorithm is used to combine the results of Local CNN and Global CNN for decision-level fusion. Benefiting from such a strategy, Local CNN fully exploits the local features of the image, effectively revealing subtle differences among the scripts that are difficult to distinguish such as English, Greek, and Russian. Moreover, Global CNN mines the global features of the image to improve the accuracy of script identification. The experimental results demonstrate that our approach has a good performance on four public datasets.