Highway pilot assist has become the front line of competition in advanced driver assistance systems. The increasing requirements on safety and user acceptance are calling for personalization in the development process of such systems. Inspired by a finding on drivers' car-following preferences on lateral direction, a personalized highway pilot assist algorithm is proposed, which consists of an Intelligent Driver Model (IDM) based speed control model and a novel lane-keeping model considering the leading vehicle's lateral movement. A simulated driving experiment is conducted to analyse driver gaze and lane-keeping Behaviours in free-driving and following driving scenario. Drivers are clustered into two driving style groups referring to their driving Behaviours affected by the leading vehicle, and then the personalization parameters for every specific subject driver are optimized. The proposed algorithm is validated through driver-in-the-loop experiment based on a moving-base simulator. Results show that, compared with the un-personalized algorithms, the personalized highway pilot algorithm can significantly reduce the mental workload and improve user acceptance of the assist functions.
翻译:公路试点协助已成为先进的驾驶员协助系统竞争的第一线。对安全和用户接受程度的日益要求要求这些系统在开发过程中实现个性化。根据对驾驶员对横向方向的汽车跟踪偏好的调查结果,提出了个性化高速公路试点协助算法,其中包括基于智能驾驶员模型(IDM)的速度控制模型和考虑到车辆横向运动的新颖的车道维护模式。进行了模拟驾驶实验,在自由驾驶和随后驾驶的情况下分析驾驶员的视线和车道保持行为。驾驶员被分为两个驾驶风格组,指受主要车辆影响的驾驶行为,然后优化每个具体驾驶员的个人化参数。拟议的算法通过基于移动基地模拟器的驾驶员即行车实验得到验证。结果显示,与非个性化算法相比,个性化高速公路试点算法可以大大减少心理工作量,提高用户对协助功能的接受度。