A cooperative control method for safer on-ramp merging process in heterogeneous traffic flow.

The on-ramp area is a high-risk conflict zone where traffic accidents frequently occur. Connected and automated vehicles (CAVs) have the potential to enhance the safety of the merging process through appropriate cooperative control methods. This paper proposes a cooperative control method for safer on-ramp merging processes in heterogeneous traffic flow.

Data generation for connected and automated vehicle tests using deep learning models.

For the simulation-based test and evaluation of connected and automated vehicles (CAVs), the trajectory of the background vehicle has a direct effect on the performance of CAVs and experiment outcomes. The collected real trajectory data are limited by the sample size and diversity, and may exclude critical attribute combinations that are of vital importance for CAVs' tests. Consequently, it is indispensable to increase the richness of accessible trajectory data.

Iterative learning control for lane-changing trajectories upstream off-ramp bottlenecks and safety evaluation.

This paper proposes an iterative learning control framework for lane changing to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). This framework controls CAVs in the off-ramp bottlenecks by imitating the trajectories optimized by machine learning algorithms. Next Generation Simulation (NGSIM) dataset is utilized as the raw data and filtered by cost function.

Influence of cyber-attacks on longitudinal safety of connected and automated vehicles.

Connected and automated vehicle (CAV) has been a remarkable focal point in recent years, since it is recognized as a potential method to reduce traffic congestion, emission and accident. However, the connectivity function makes CAVs vulnerable to cyber-attacks. An intuitive method to defend cyber-attacks on CAVs is that if the error between expected and measured behaviors exceeds a predetermined threshold, a security scheme should be activated.

Evaluating impacts of different longitudinal driver assistance systems on reducing multi-vehicle rear-end crashes during small-scale inclement weather.

Multi-vehicle rear-end (MVRE) crashes during small-scale inclement (SSI) weather cause high fatality rates on freeways, which cannot be solved by traditional speed limit strategies. This study aimed to reduce MVRE crash risks during SSI weather using different longitudinal driver assistance systems (LDAS). The impact factors on MVRE crashes during SSI weather were firstly analyzed.

Large-Scale Growth of Tubular Aragonite Whiskers through a MgCl₂-Assisted Hydrothermal Process.

In this paper, we have developed a facile MgCl₂-assissted hydrothermal synthesis route to grow tubular aragonite whiskers on a large scale. The products have been characterized by powder X-ray diffraction (XRD), optical microscopy, and scanning electronic microscopy (SEM). The results show the as-grown product is pure tubular aragonite crystalline whiskers with a diameter of 5-10 mm and a length of 100-200 mm, respectively.