Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study

Lane change is a highly demanding driving task. A number of traffic accidents are induced by erroneous maneuvers. An automated lane-change system has the potential to reduce driver workload and improve safety. challenge acceptance system. From perspective human factors, an with different styles would user acceptance, because drivers could drive in scenarios. This paper proposes method design for analyzing modeling truck-driver behavior. truck simulator experiment 12 participants was conducted identify driver-model parameters. The lane were classified into three types: aggressive, medium, conservative. proposed evaluated another same participants. Moreover, effects decision-making on experience from perspectives both ego surrounding vehicles. evaluation results demonstrate that can be distinguished drivers. Overall, as being safe reliable. main contribution this study it provides insights styles. Furthermore, these observations applied commercial trucks.