Examining the safety of trucks under crosswind at bridge-tunnel section: A driving simulator study

A truck platoon is a set of virtually linked trucks that travel in tandem with small intervehicle distances. Several studies have proved that traveling in platoons can significantly improve fuel economy due to the reduced aerodynamic drag. However, most literature only provides scattered pieces of information regarding fuel economy in truck platoons. Therefore, a literature survey is needed to understand what has been studied and what problems remain to be further addressed. This paper presents an overview of existing studies to illustrate the state of the art about fuel savings for truck platooning. Specifically, it summarized the methodologies, the contributing factors of fuel consumption, the coordination methods to improve the platooning rate, and the look-ahead control strategies to generate fuel-efficient speed profiles for each vehicle driving in a platoon over different road grades. After that, the autonomous truck platooning was introduced, and we raised and discussed a couple of outstanding questions to be addressed in future work.

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Fuel Economy in Truck Platooning: A Literature Overview and Directions for Future Research

With ongoing massive investment in the field of autonomous vehicles, fully autonomous self-driving vehicles are set to become a reality soon. Despite the benefits in traffic safety and economy, the potential effects of autonomous vehicles, especially autonomous trucks, on transportation infrastructure remain to be determined. In this study, a finite element model was developed based on a typical flexible pavement structure. The effects of autonomous trucks’ lateral distribution within the lane with respect to rutting depth and fatigue damage were estimated by finite element analysis under certain environmental condition. Characteristics of the pavement structure and environmental condition have been collected and presented in this paper. Four possible lateral control modes on managing autonomous trucks’ lateral distribution were proposed, listed as zero-wander mode, uniform mode, double peak Gaussian mode, two-section uniform mode. Considering the difference between autonomous trucks and human-driven trucks, the ratio of autonomous trucks was also evaluated. Based on the simulation results, it has been confirmed that, if controlled appropriately, autonomous trucks could be highly beneficial to asphalt pavements for wider using of pavement. Conversely, if without appropriate lateral control, the effect of autonomous trucks on the typical flexible pavement life could be negative, owing to repeated single point load caused by lane centering and keeping. For example, the uniform mode, double peak Gaussian mode and two-section uniform mode all throw positive effect on the pavement. On the contrary, under the zero wander mode, the time when the rutting depth of the proposed pavement reaches 15 mm (defined as maintenance year) may advance by 1.56 years and fatigue damage at the bottom of asphalt layer increases by 146%. Compared with the normal situation nowadays, the two-section uniform distribution of autonomous trucks, the best performing control mode, delays the maintenance year by 2.3 years. From the aspect of fatigue, a much uniform distribution curve of fatigue within the lane can also be found under this mode. When all trucks’ lateral positions were in control, the two-section uniform mode could reduce the fatigue damage by up to 35% under repeated standard axle load on the flexible pavement. Finally, a framework of dealing with the lateral control was proposed as a reference for different conditions, such as asphalt pavement structures and materials.

Assess the impacts of different autonomous trucks’ lateral control modes on asphalt pavement performance

Car accidents cause a large number of deaths and disabilities every day, a certain proportion of which result from untimely treatment and secondary accidents. To some extent, automatic car accident detection can shorten response time of rescue agencies and vehicles around accidents to improve rescue efficiency and traffic safety level. In this paper, we proposed an automatic car accident detection method based on Cooperative Vehicle Infrastructure Systems (CVIS) and machine vision. First of all, a novel image dataset CAD-CVIS is established to improve accuracy of accident detection based on intelligent roadside devices in CVIS. Especially, CAD-CVIS is consisted of various kinds of accident types, weather conditions and accident location, which can improve self-adaptability of accident detection methods among different traffic situations. Secondly, we develop a deep neural network model YOLO-CA based on CAD-CVIS and deep learning algorithms to detect accident. In the model, we utilize Multi-Scale Feature Fusion (MSFF) and loss function with dynamic weights to enhance performance of detecting small objects. Finally, our experiment study evaluates performance of YOLO-CA for detecting car accidents, and the results show that our proposed method can detect car accident in 0.0461 seconds (21.6FPS) with 90.02% average precision (AP). In additionally, we compare YOLO-CA with other object detection models, and the results demonstrate the comprehensive performance improvement on the accuracy and real-time over other models.

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An Automatic Car Accident Detection Method Based on Cooperative Vehicle Infrastructure Systems

Self-healing properties of ferrite-filled open-graded friction course (OGFC) asphalt mixture after moisture damage

Visual intervention has a significant influence on drivers’ behaviour, which may cause the redistribution of wheel tracks, relieving the stress from the concentration of axial loads so that the rut...

Using the visual intervention influence of pavement marking for rutting mitigation – Part II: visual intervention timing based on the finite element simulation

The wheel tracks on highways are excessively concentrated and lead to rutting on asphalt pavements, which reduces the service life of highway pavements A new method is proposed, which uses the vis

Ming Xu

Papers

Using the visual intervention influence of pavement marking for rutting mitigation – Part II: visual intervention timing based on the finite element simulation

Using the visual intervention influence of pavement markings for rutting mitigation–part I: preliminary experiments and field tests