Driving risk classification is usually used for evaluating and reducing traffic accidents. It is of
great significance to improve urban traffic problems, such as traffic jams and road accidents …

This work presents the development of a classification method that can contribute to precise
and increased awareness of the situational context of vehicles, for it to be used in …

Trajectory prediction for traffic participants is a critical task for autonomous vehicles. The
long-term trajectory prediction is challenging due to limited data and the dynamic …

This study used methodologies of descriptive and quantitative statistics to identify the
contributing factors most affecting occupational accident outcomes among electrical …

The unique traffic situation at roundabouts causes complex interactions between merging
vehicles, thereby increasing the likelihood of conflicts. Reliable prediction of conflict risk …

Dangerous driving behaviors are diverse and complex. Determining how to analyze the
driving behavior of public drivers objectively and accurately has always been a research …

Remaining driving range (RDR) research has continued to consistently evolve with the
development of electric vehicles (EVs). Accurate RDR prediction is a promising approach to …

Recognition of abnormal driving behavior is an important application area as it can support
driving reliability and improve safety. In the last decade, deep learning methods have been …

The essence of English for engineering is English for professional purposes (ESP). The
assessment of the level of education in engineering English classrooms is one of the key …

Driving behavior is considered one of the most important factors in all road crashes,
accounting for 40% of all fatal and serious accidents. Moreover, aggressive driving is the …