Vehicles with Automated Driving Systems (ADS) operate in a high-dimensional continuous system with multi-agent interactions. This continuous system features various types of traffic agents (non-homogeneous) governed by continuous-motion ordinary differential equations (differential-drive). Each agent makes decisions independently that may lead to conflicts with the subject vehicle (SV), as well as other participants (non-cooperative). A typical vehicle safety evaluation procedure that uses various safety-critical scenarios and observes resultant collisions (or near collisions), is not sufficient enough to evaluate the performance of the ADS in terms of operational safety status maintenance. In this paper, we introduce a Model Predictive Instantaneous Safety Metric (MPrISM), which determines the safety status of the SV, considering the worst-case safety scenario for a given traffic snapshot. The method then analyzes the SV's closeness to a potential collision within a certain evaluation time period. The described metric induces theoretical guarantees of safety in terms of the time to collision under standard assumptions. Through formulating the solution as a series of minimax quadratic optimization problems of a specific structure, the method is tractable for real-time safety evaluation applications. Its capabilities are demonstrated with synthesized examples and cases derived from real-world tests.
Shanghai University（上海大学）；Michigan State University（密歇根州立大学）
Deep reinforcement learning (DRL) has a great potential for solving complex decision-making problems in autonomous driving, especially in mixed-traffic scenarios where autonomous vehicles and human-driven vehicles (HDVs) drive together. Safety is a key during both the learning and deploying reinforcement learning (RL) algorithms process. In this paper, we formulate the on-ramp merging as a Markov Decision Process (MDP) problem and solve it with an off-policy RL algorithm, i.e., Soft Actor-Critic for Discrete Action Settings (SAC-Discrete). In addition, a motion predictive safety controller inc…
College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）
In this paper, we propose an efficient, receding horizon, local adaptive low-level planner as the middle layer between our original planner and controller. Our method is named as corridor-based model predictive contouring control (CMPCC) since it builds upon on MPCC and utilizes the flight corridor as hard safety constraints. It optimizes the flight aggressiveness and tracking accuracy simultaneously, thus improving our system's robustness by overcoming unmeasured disturbances. Our method features its online flight speed optimization, strict safety and feasibility, and real-time performance, a…
Ain Shams University（艾因夏姆斯大学）；Jacobs University（雅各布大学）；KTH Royal Institute of Technology（KTH皇家理工学院）
Reinforcement learning (RL) algorithms can achieve state-of-the-art performance in decision-making and continuous control tasks. However, applying RL algorithms on safety-critical systems still needs to be well justified due to the exploration nature of many RL algorithms, especially when the model of the robot and the environment are unknown. To address this challenge, we propose a data-driven safety layer that acts as a filter for unsafe actions. The safety layer uses a data-driven predictive controller to enforce safety guarantees for RL policies during training and after deployment. The RL…
Interaction-Aware Model Predictive Decision-Making for Socially-Compliant Autonomous Driving in Mixed Urban Traffic Scenarios
Institute of Control Systems, Karlsruhe Institute for Technology（控制系统研究所，卡尔斯鲁厄技术学院）；Wuerzburg Institute for Traffic Sciences GmbH（魏玛交通科学研究所有限公司）；Faculty of Engineering - Mechanical Engineering Dept, University of Alberta（工程学院-机械工程系，阿尔伯塔大学）
University of Alcalá（阿尔卡拉大学）；Joint Research Centre, European Commission（欧盟委员会联合研究中心）
Conventional vehicles are certified through classical approaches, where different physical certification tests are set up on test tracks to assess required safety levels. These approaches are well suited for vehicles with limited complexity and limited interactions with other entities as last-second resources. However, these approaches do not allow to evaluate safety with real behaviors for critical and edge cases, nor to evaluate the ability to anticipate them in the mid or long term. This is particularly relevant for automated and autonomous driving functions that make use of advanced predic…
ETH Zürich（苏黎世联邦理工学院）；California Institute of Technology（加州理工学院）
The problem of dynamic locomotion over rough terrain requires both accurate foot placement together with an emphasis on dynamic stability. Existing approaches to this problem prioritize immediate safe foot placement over longer term dynamic stability considerations, or relegate the coordination of foot placement and dynamic stability to heuristic methods. We propose a multi-layered locomotion framework that unifies Control Barrier Functions (CBFs) with Model Predictive Control (MPC) to simultaneously achieve safe foot placement and dynamic stability. Our approach incorporates CBF based safety …
Shield-Loco: Shielding Locomotion Policies with Predictive Safety Filtering
Robert Bosch Center for Cyber Physical Systems（罗伯特·博世网络物理系统中心）；Indian Institute of Science（印度科学研究院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所（MIRMI））；Technical University of Munich（慕尼黑技术大学）；Department of Computer Science & Automation（计算机科学与自动化部门）；Department of Mechanical Engineering（机械工程系）；Carnegie Mellon University（卡内基梅隆大学）；Institute for Advanced Study（高级研究 institute）
Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
State Key Laboratory of Industrial Control Technology, Zhejiang University（浙江大学工业控制技术国家重点实验室）；Professorship of Autonomous Vehicle Systems, Technical University of Munich（慕尼黑工业大学自主车辆系统教授职位）
Tsinghua University（清华大学）；ETH Zurich（苏黎世联邦理工学院）；Harbin Institute of Technology（哈尔滨工业大学）
This paper presents an efficient and safe method to avoid static and dynamic obstacles based on LiDAR. First, point cloud is used to generate a real-time local grid map for obstacle detection. Then, obstacles are clustered by DBSCAN algorithm and enclosed with minimum bounding ellipses (MBEs). In addition, data association is conducted to match each MBE with the obstacle in the current frame. Considering MBE as an observation, Kalman filter (KF) is used to estimate and predict the motion state of the obstacle. In this way, the trajectory of each obstacle in the forward time domain can be param…
The optimal performance of robotic systems is usually achieved near the limit of state and input bounds. Model predictive control (MPC) is a prevalent strategy to handle these operational constraints, however, safety still remains an open challenge for MPC as it needs to guarantee that the system stays within an invariant set. In order to obtain safe optimal performance in the context of set invariance, we present a safety-critical model predictive control strategy utilizing discrete-time control barrier functions (CBFs), which guarantees system safety and accomplishes optimal performance via …
Point Cloud-Based Control Barrier Functions for Model Predictive Control in Safety-Critical Navigation of Autonomous Mobile Robots
School of Automation Science and Engineering, South China University of Technology（自动化科学与工程学院，华南理工大学）；Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（南方海洋科学与工程 Guangdong 实验室（珠海））；Guangdong Academy of Safety Production and Emergency Management Science and Technology（广东安全生产与应急管理部门科学与技术研究院）
A Predictive Cooperative Collision Avoidance for Multi-Robot Systems Using Control Barrier Function
Harbin Institute of Technology Shenzhen（哈尔滨工业大学深圳研究院）；Southern University of Science and Technology（南方科技大学）；University of Oulu（奥卢大学）；VTT-Technical Research Centre of Finland（芬兰技术研究中心）
Cooptimizing Safety and Performance Using Safety Value-Constrained Model Predictive Control
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California（南加州大学明希德电气与计算机工程系）；Department of Aeronautics and Astronautics, Stanford University（斯坦福大学航空与航天系）；Electrical and Computer Engineering Department, New York University（纽约大学电气与计算机工程系）
Safety is one of the fundamental problems in robotics. Recently, one-step or multi-step optimal control problems for discrete-time nonlinear dynamical system were formulated to offer tracking stability using control Lyapunov functions (CLFs) while subject to input constraints as well as safety-critical constraints using control barrier functions (CBFs). The limitations of these existing approaches are mainly about feasibility and safety. In the existing approaches, the feasibility of the optimization and the system safety cannot be enhanced at the same time theoretically. In this paper, we pro…
Predictive Safety Shield for Dyna-Q Reinforcement Learning
Departement U2IS, ENSTA Paris, Institut Polytechnique de Paris（ENSTA巴黎大学U2IS部门，巴黎理工学院）；Department of Electrical Engineering and Computer Science, University of California, Berkeley, USA（加州大学伯克利分校电气工程与计算机科学系）
Reinforcement learning is a promising approach to learning control policies for performing complex multi-agent robotics tasks. However, a policy learned in simulation often fails to guarantee even simple safety properties such as obstacle avoidance. To ensure safety, we propose multi-agent model predictive shielding (MAMPS), an algorithm that provably guarantees safety for an arbitrary learned policy. In particular, it operates by using the learned policy as often as possible, but instead uses a backup policy in cases where it cannot guarantee the safety of the learned policy. Using a multi-ag…
Predictive Semantic Safety: From Visual Physical Reasoning to Safety-Critical Control
University of Michigan（密歇根大学）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Dalle Molle Institute for Artificial Intelligence Research (IDSIA)（达勒莫尔人工智能研究所）；Università della Svizzera italiana (USI)（瑞士意大利语区大学）；Scuola universitaria professionale della Svizzera italiana (SUPSI)（瑞士意大利语区应用科学与艺术大学）
VisionSafeEnhanced VPC: Cautious Predictive Control with Visibility Constraints under Uncertainty for Autonomous Robotic Surgery
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；School of Advanced Manufacturing and Robotics, Peking University（北京大学先进制造与机器人学院）；MicroPort MedBot (Group) Company Ltd.（微宏医疗机器人（集团）有限公司）；Department of Biomedical Engineering, City University of Hong Kong（香港城市大学生物医学工程系）
Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizon
University of Toronto（多伦多大学）；University of Toronto Robotics Institute（多伦多大学机器人研究所）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Technical University of Munich（慕尼黑工业大学）；Munich Institute for Robotics and Machine Intelligence（慕尼黑机器人与机器智能研究所）
Quantifying the safety of the human body orientation is an important issue in human-robot interaction. Knowing the changing physical constraints on human motion can improve inspection of safe human motions and bring essential information about stability and normality of human body orientations with real-time risk assessment. Also, this information can be used in cooperative robots and monitoring systems to evaluate and interact in the environment more freely. Furthermore, the workspace area can be more deterministic with the known physical characteristics of safety. Based on this motivation, w…
