Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions
School of Mechanical and Aerospace Engineering, Queen’s University Belfast（女王大学贝尔法斯特机械与航空航天工程学院）；Department of Mechanical and Industrial Engineering, Concordia University（Concordia大学机械与工业工程系）；State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, College of Mechanical and Vehicle Engineering, Hunan University（湖南大学车辆先进设计与制造技术国家重点实验室，机械与车辆工程学院）；James Watt School of Engineering, University of Glasgow（格拉斯哥大学詹姆斯·瓦特工程学院）
Jing Wang, Yan Jin, Hamid Taghavifar, Fei Ding, Chongfeng Wei
Since the emergence of autonomous driving technology, it has advanced rapidly over the past decade. It is becoming increasingly likely that autonomous vehicles (AVs) would soon coexist with human-driven vehicles (HVs) on the roads. Currently, safety and reliable decision-making remain significant challenges, particularly when AVs are navigating lane changes and interacting with surrounding HVs. Therefore, precise estimation of the intentions of surrounding HVs can assist AVs in making more reliable and safe lane change decision-making. This involves not only understanding their current behaviors but also predicting their future motions without any direct communication. However, distinguishing between the passing and yielding intentions of surrounding HVs still remains ambiguous. To address the challenge, we propose a social intention estimation algorithm rooted in Directed Acyclic Graph (DAG), coupled with a decision-making framework employing Deep Reinforcement Learning (DRL) algorithms. To evaluate the method's performance, the proposed framework can be tested and applied in a lane-changing scenario within a simulated environment. Furthermore, the experiment results demonstrate how our approach enhances the ability of AVs to navigate lane changes safely and efficiently on roads.
Autonomous cars can perform poorly for many reasons. They may have perception issues, incorrect dynamics models, be unaware of obscure rules of human traffic systems, or follow certain rules too conservatively. Regardless of the exact failure mode of the car, often human drivers around the car are behaving correctly. For example, even if the car does not know that it should pull over when an ambulance races by, other humans on the road will know and will pull over. We propose to make socially cohesive cars that leverage the behavior of nearby human drivers to act in ways that are safer and mor…
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）
Australian Centre for Field Robotics（澳大利亚野外机器人学中心）；University of Sydney（悉尼大学）
Seamlessly operating an autonomous vehicle in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict in such environments. Recent work has demonstrated that reinforcement learning-based methods have the ability to learn to drive in crowds. However, these methods can have very poor performance due to inaccurate predictions of the pedestrians' future state as human motion prediction has a large variance. To overcome this problem, we propose a new method, SARL-SGAN-KCE, that combines a deep socially aware attentive val…
VIT-Ped: Visionary Intention Transformer for Pedestrian Behavior Analysis
German University in Cairo（埃及德国大学）；Faculty of Media Engineering and Technology（媒体工程与技术学院）；Computer Science and Engineering Department（计算机科学与工程系）；C-DRiVeS Lab（C-DRiVeS实验室）；Cognitive Driving Research in Vehicular Systems（车载系统认知驾驶研究）
University of Surrey（萨里大学）；University of Warwick（华威大学）；Warwick Manufacturing Group（华威制造集团）
Autonomous driving decision-making is a challenging task due to the inherent complexity and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake at any time to pass a slow vehicle or to help traffic flow. Anticipating the intention of surrounding vehicles, estimating their future states and integrating them into the decision-making process of an automated vehicle can enhance the reliability of autonomous driving in complex driving scenarios. This paper proposes a Prediction-based Deep Reinforcement Learning (PDRL) decision-making model that considers the man…
University of Science and Technology of China（中国科学技术大学）
Existing navigation policies for autonomous robots tend to focus on collision avoidance while ignoring human-robot interactions in social life. For instance, robots can pass along the corridor safer and easier if pedestrians notice them. Sounds have been considered as an efficient way to attract the attention of pedestrians, which can alleviate the freezing robot problem. In this work, we present a new deep reinforcement learning (DRL) based social navigation approach for autonomous robots to move in pedestrian-rich environments with interaction capacity. Most existing DRL based methods intend…
Perspective-Shifted Neuro-Symbolic World Models: A Framework for Socially-Aware Robot Navigation
Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa（系统机器人研究所，理工学院，里斯本大学）；LAAS-CNRS, Artificial and Natural Intelligence Toulouse Institute (ANITI)（LAAS-CNRS，图卢兹人工智能研究所（ANITI））
Reframing Human-Robot Interaction Through Extended Reality: Unlocking Safer, Smarter, and More Empathic Interactions with Virtual Robots and Foundation Models
Division of Robotics, Perception, and Learning, KTH Royal Institute of Technology（KTH皇家理工学院机器人、感知与学习部）；Department of Clinical Medicine, University of Bergen（卑尔根大学临床医学系）
An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment
Intelligent Transportation Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）智能交通研究所，系统中心）；Guangdong Provincial Key Lab of Integrated Communication, Sensing and Computation for Ubiquitous Internet of Things（广东省集成通信、感知与计算 ubiquitous internet of things 关键实验室）；Robotics and Autonomous Systems Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）机器人与自主系统研究所）
Cognitive Neurorobotics Research Unit（认知神经机器人研究单元）；Okinawa Institute of Science and Technology Graduate University（冲绳科学技术大学院大学）
When agents interact socially with different intentions, conflicts are difficult to avoid. Although how agents can resolve such problems autonomously has not been determined, dynamic characteristics of agency may shed light on underlying mechanisms. The current study focused on the sense of agency (SoA), a specific aspect of agency referring to congruence between the agent's intention in acting and the outcome. Employing predictive coding and active inference as theoretical frameworks of perception and action generation, we hypothesize that regulation of complexity in the evidence lower bound …
SAP-CoPE: Social-Aware Planning using Cooperative Pose Estimation with Infrastructure Sensor Nodes
Mechanical and Mechatronics Eng. Department, University of Waterloo（机械与机电工程系，滑铁库大学）；Department of Data and Systems Engineering, University of Hong Kong（数据与系统工程系，香港大学）；Mechanical Engineering Department, University of Alberta（机械工程系，阿尔伯塔大学）
