Automated driving systems are often used for lane keeping tasks.By these systems,a local path is planned ahead of the vehicle.However,these paths are often found unnatural by human drivers.In response to this,this pap...Automated driving systems are often used for lane keeping tasks.By these systems,a local path is planned ahead of the vehicle.However,these paths are often found unnatural by human drivers.In response to this,this paper proposes a linear driver model,which can calculate node points reflective of human driver preferences and based on these node points a human driver preferred motion path can be designed for autonomous driving.The model input is the road curvature,effectively harnessed through a self-developed Euler-curve-based curve fitting algorithm.A comprehensive case study is undertaken to empirically validate the efficacy of the proposed model,demonstrating its capacity to emulate the average behavioral pat-terns observed in human curve path selection.Statistical analyses further underscore the model's robustness,affirming the authenticity of the established relationships.This paradigm shift in trajectory planning holds promising implications for the seamless integration of autonomous driving systems with human driving preferences.展开更多
基金supported by the European Union within the framework of the National Laboratory for Autonomous Systems.(RRF-2.3.1-21-2022-00002).
文摘Automated driving systems are often used for lane keeping tasks.By these systems,a local path is planned ahead of the vehicle.However,these paths are often found unnatural by human drivers.In response to this,this paper proposes a linear driver model,which can calculate node points reflective of human driver preferences and based on these node points a human driver preferred motion path can be designed for autonomous driving.The model input is the road curvature,effectively harnessed through a self-developed Euler-curve-based curve fitting algorithm.A comprehensive case study is undertaken to empirically validate the efficacy of the proposed model,demonstrating its capacity to emulate the average behavioral pat-terns observed in human curve path selection.Statistical analyses further underscore the model's robustness,affirming the authenticity of the established relationships.This paradigm shift in trajectory planning holds promising implications for the seamless integration of autonomous driving systems with human driving preferences.
