In this paper, a human-machine adaptive shared control method is proposed for automated vehicles (AVs) under automation performance degradation. First, a novel risk assessment module is proposed to monitor driving behavior and evaluate automation performance degradation for AVs. Then, an adaptive control authority allocation module is developed. In the event of any performance degradation detection, the allocated control authority of the automation system is decreased based on the assessed risk to reduce the potential risk of vehicle motion. Consequently, the control authority allocated to the human driver is adaptively increased and thus requires more driver engagement in the control loop to compensate for the automation degradation and ensure AV safety. Experimental validation is conducted under different driving scenarios. The testing results show that the proposed approach is able to effectively compensate for the performance degradation of vehicle automation through the human-machine adaptive shared control, ensuring the safety of automated driving
This contribution introduces a centralized input constrained optimal control framework based on multiple control barrier functions (CBFs) to coordinate connected and automated agents at intersections. For collision avoidance, we propose a novel CBF which is safe by construction. The given control scheme provides provable guarantees that collision avoidance CBFs and CBFs to constrain the agents' velocity are jointly feasible (referred to as control-sharing property) subject to input constraints. A simulation study finally provides evidence that the proposed control scheme is safe.
This paper presents an adaptive haptic shared control framework wherein a driver and an automation system are physically connected through a motorized steering wheel. The automation system is modeled as an intelligent agent that is not only capable of making decisions but also monitoring the human's behavior and adjusting its behavior accordingly. To enable the automation system to smoothly exchange the control authority with the human partner, this paper introduces a novel self-regulating impedance controller for the automation system. To determine an optimal modulation policy, a cost functio…
The purpose of this paper is to develop a shared control takeover strategy for smooth and safety control transition from an automation driving system to the human driver and to approve its positive impacts on drivers' behavior and attitudes. A "human-in-the-loop" driving simulator experiment was conducted to evaluate the impact of the proposed shared control takeover strategy under different disengagement conditions. Results of thirty-two drivers showed shared control takeover strategy could improve safety performance at the aggregated level, especially at non-driving related disengagements. F…
In this paper we seek to quantify the ability of learning to improve safety guarantees endowed by Control Barrier Functions (CBFs). In particular, we investigate how model uncertainty in the time derivative of a CBF can be reduced via learning, and how this leads to stronger statements on the safe behavior of a system. To this end, we build upon the idea of Input-to-State Safety (ISSf) to define Projection-to-State Safety (PSSf), which characterizes degradation in safety in terms of a projected disturbance. This enables the direct quantification of both how learning can improve safety guarante…
With ever-expanding computation and communication capabilities of modern embedded platforms, Internet of Things (IoT) technologies enable development of Reconfigurable Manufacturing Systems---a new generation of highly modularized industrial equipment suitable for highly-customized manufacturing. Sequential control in these systems is largely based on discrete events, while their formal execution semantics is specified as Control Interpreted Petri Nets (CIPN). Despite industry-wide use of programming languages based on the CIPN formalism, formal verification of such control applications in the…
Safety-Ensured Robotic Control Framework for Cutting Task Automation in Endoscopic Submucosal Dissection
Division of Mechanical System Engineering, Myongji University, Yongin-si, Gyeonggi-do, Republic of Korea（机械系统工程系，明州大学，韩国京畿道 Yongin市）；SDU Robotics, the Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark（SDU机器人，马士克麦金尼莫勒研究所，丹麦南部大学，Odense）；Department of Mechanical Engineering, Myongji University, Yongin-si, Gyeonggi-do, Republic of Korea（机械工程系，明州大学，韩国京畿道 Yongin市）
The market introduction of automated vehicles has motivated intense research efforts into the safety of automated vehicle systems. Unlike driver assistance systems, SAE Level 3+ systems are not only responsible for executing (parts of) the dynamic driving task (DDT), but also for monitoring the automation system's performance at all times. Key components to fulfill these surveillance tasks are system monitors which can assess the system's performance at runtime, e.g. to activate fallback modules in case of partial system failures. In order to implement reasonable monitoring strategies for an a…
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；University of Michigan（密歇根大学）
Communication and cooperation among team members can be enhanced significantly with physical interaction. Successful collaboration requires the integration of the individual partners' intentions into a shared action plan, which may involve a continuous negotiation of intentions and roles. This paper presents an adaptive haptic shared control framework wherein a human driver and an automation system are physically connected through a motorized steering wheel. By virtue of haptic feedback, the driver and automation system can monitor each other actions and can still intuitively express their con…
Modern nonlinear control theory seeks to endow systems with properties of stability and safety, and have been deployed successfully in multiple domains. Despite this success, model uncertainty remains a significant challenge in synthesizing safe controllers, leading to degradation in the properties provided by the controllers. This paper develops a machine learning framework utilizing Control Barrier Functions (CBFs) to reduce model uncertainty as it impact the safe behavior of a system. This approach iteratively collects data and updates a controller, ultimately achieving safe behavior. We va…
NXP Semiconductors（恩智浦半导体）；Eindhoven University of Technology（埃因霍温理工大学）
Modern Automated Driving (AD) systems rely on safety measures to handle faults and to bring vehicle to a safe state. To eradicate lethal road accidents, car manufacturers are constantly introducing new perception as well as control systems. Contemporary automotive design and safety engineering best practices are suitable for analyzing system components in isolation, whereas today's highly complex and interdependent AD systems require novel approach to ensure resilience to multi-point failures. We present a holistic safety concept unifying advanced safety measures for handling multiple-point fa…
