Digital Evaluation of Sitting Posture Comfort in Human-vehicle System under Industry 4.0 Framework

Most of the previous studies on the vibration ride comfort of the human-vehicle system were focused only on one or two aspects of the investigation. A hybrid approach which integrates all kinds of investigation methods in real environment and virtual environment is described. The real experimental environment includes the WBV（whole body vibration） test, questionnaires for human subjective sensation and motion capture. The virtual experimental environment includes the theoretical calculation on simplified 5-DOF human body vibration model, the vibration simulation and analysis within ADAMS/VibrationTM module, and the digital human biomechanics and occupational health analysis in Jack software. While the real experimental environment provides realistic and accurate test results, it also serves as core and validation for the virtual experimental environment. The virtual experimental environment takes full advantages of current available vibration simulation and digital human modelling software, and makes it possible to evaluate the sitting posture comfort in a human-vehicle system with various human anthropometric parameters. How this digital evaluation system for car seat comfort design is fitted in the Industry 4.0 framework is also proposed.

Decision-Making in Driver-Automation Shared Control:A Review and Perspectives

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making processes.The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans.Many open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared schemes.Finally,we give a further discussion on the key future challenges and opportunities.They are likely to shape new potential research directions.

Adaptive active inceptor design under shared control architecture for nonlinear pilot-induced oscillations

This paper presents a Shared Control Architecture(SCA)between a human pilot and a smart inceptor for nonlinear Pilot Induced Oscillations(PIOs),e.g.,category II or III PIOs.One innovation of this paper is that an intelligent shared control architecture is developed based on the intelligent active inceptor technique,i.e.,Smart Adaptive Flight Effective Cue(SAFE-Cue).A deep reinforcement learning approach namely Deep Deterministic Policy Gradient(DDPG)method is chosen to design a gain adaptation mechanism for the SAFE-Cue module.By doing this,the gains of the SAFE-Cue will be intelligently tuned once nonlinear PIOs triggered;meanwhile,the human pilot will receive a force cue from the SAFE-Cue,and will consequently adapting his/her control policy.The second innovation of this paper is that the reward function of the DDPG based gain adaptation approach is constructed according to flying qualities.Under the premise of considering failure situation,task completion qualities and pilot workload are also taken into account.Finally,the proposed approach is validated using numerical simulation experiments with two types of scenarios:lower actuator rate limits and airframe damages.The Inceptor Peak Power-Phase(IPPP)metric is adopted to analyze the human-vehicle system simulation results.Results and analysis show that the DDPG based sharing control approach can well address nonlinear PIO problems consisting of Categories Ⅱ and Ⅲ PIO events.

Prediction of nonlinear pilot-induced oscillation using an intelligent human pilot model

This paper proposes a method to predict nonlinear Pilot-Induced Oscillation(PIO)using an intelligent human pilot model.This method is based on a scalogram-based PIO metric,which uses wavelet transforms to analyze the nonlinear characteristics of a time-varying system.The intelligent human pilot model includes three modules:perception module,decision and adaptive module,and execution module.Intelligent and adaptive features,including a neural network receptor,fuzzy decision and adaptation,are also introduced into the human pilot model to describe the behavior of the human pilot accommodating the nonlinear events.Furthermore,an algorithm is proposed to describe the procedure of the PIO prediction method with nonlinear evaluation cases.The prediction results obtained by numerical simulation are compared with the assessments of flight test data to validate the utility of the method.The flight test data were generated in the evaluation of the Smart-Cue/Smart-Gain,which is capable of reducing the PIO tendencies considerably.The results show that the method can be applied to predict the nonlinear PIO events by human pilot model simulation.