Analysis of Truck Driver Behavior to Design Different Lane Change Styles in Automated Driving
URL: http://arxiv.org/abs/2012.15164v1
archive: archived pipeline: cataloged verified
Abstract
Lane change is a very demanding driving task and number of traffic accidents are induced by mistaken maneuvers. An automated lane change system has the potential to reduce driver workload and to improve driving safety. One challenge is how to improve driver acceptance on the automated system. From the viewpoint of human factors, an automated system with different styles would improve user acceptance as the drivers can adapt the style to different driving situations. This paper proposes a method to design different lane change styles in automated driving by analysis and modeling of truck driver behavior. A truck driving simulator experiment with 12 participants was conducted to identify the driver model parameters and three lane change styles were classified as the aggressive, medium, and conservative ones. The proposed automated lane change system was evaluated by another truck driving simulator experiment with the same 12 participants. Moreover, the effect of different driving styles on driver experience and acceptance was evaluated. The evaluation results demonstrate that the different lane change styles could be distinguished by the drivers; meanwhile, the three styles were overall evaluated as acceptable on safety issues and reliable by the human drivers. This study provides insight into designing the automated driving system with different driving styles and the findings can be applied to commercial automated trucks.
Summary
Truck-driving-simulator study designing automated lane-change styles from analysis of human truck-driver behavior. Twelve participants performed discretionary lane changes in a decelerating-lead-vehicle scenario; driver-model parameters were fit and clustered into three lane-change styles (aggressive, medium, conservative). The same 12 participants then evaluated an automated lane-change system implementing those styles in a follow-up simulator experiment. Drivers could distinguish the three styles, and all three were rated overall acceptable on safety and reliability, supporting personalized human-centered automation for commercial automated trucks.
Key finding
A driver-model-based automated lane-change controller produced three distinguishable, human-acceptable lane-change styles (aggressive/medium/conservative) for commercial trucks.
Methodology
Two-phase truck driving-simulator study: (1) parameter-identification experiment fitting a lane-change driver model to recorded trajectories, with style classification; (2) evaluation experiment in which the same drivers rated the resulting automated controller across the three derived styles.
Sample size: 12 truck drivers (same participants in both experiments).
Quality score: 5 / 5