We present a work-in-progress approach to improving driver attentiveness in cars provided with automated driving systems. The approach is based on a control loop that monitors the driver's biometrics (eye movement, heart rate, etc.) and the state of the car; analyses the driver's attentiveness level using a deep neural network; plans driver alerts and changes in the speed of the car using a formally verified controller; and executes this plan using actuators ranging from acoustic and visual to haptic devices. The paper presents (i) the self-adaptive system formed by this monitor-analyse-plan-execute (MAPE) control loop, the car and the monitored driver, and (ii) the use of probabilistic model checking to synthesise the controller for the planning step of the MAPE loop.
翻译:我们提出了一个改进自动驾驶系统提供的汽车驾驶员对驾驶员的注意的进行中工作的办法,其基础是监测驾驶员生物鉴别学(眼动、心率等)和汽车状况的控制循环;利用深神经网络分析驾驶员的注意水平;使用正式核查的控制器规划驾驶员的警报和汽车速度的变化;使用从声音和视觉到机能装置的动画器执行这项计划;该文件介绍了(一)由监测仪分析仪-计划执行(MAPE)控制循环、汽车和被监测的驾驶员组成的自我适应系统;以及(二)使用概率模型检查,将控制员合成MAPE循环的规划步骤。