Conventional control, such as model-based control, is commonly utilized in autonomous driving due to its efficiency and reliability. However, real-world autonomous driving contends with a multitude of diverse traffic scenarios that are challenging for these planning algorithms. Model-free Deep Reinforcement Learning (DRL) presents a promising avenue in this direction, but learning DRL control policies that generalize to multiple traffic scenarios is still a challenge. To address this, we introduce Multi-residual Task Learning (MRTL), a generic learning framework based on multi-task learning that, for a set of task scenarios, decomposes the control into nominal components that are effectively solved by conventional control methods and residual terms which are solved using learning. We employ MRTL for fleet-level emission reduction in mixed traffic using autonomous vehicles as a means of system control. By analyzing the performance of MRTL across nearly 600 signalized intersections and 1200 traffic scenarios, we demonstrate that it emerges as a promising approach to synergize the strengths of DRL and conventional methods in generalizable control.
Advances in Single-vehicle intelligence of automated driving have encountered significant challenges because of limited capabilities in perception and interaction with complex traffic environments. Cooperative Driving Automation~(CDA) has been considered a pivotal solution to next-generation automated driving and intelligent transportation. Though CDA has attracted much attention from both academia and industry, exploration of its potential is still in its infancy. In industry, companies tend to build their in-house data collection pipeline and research tools to tailor their needs and protect …
Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration, with each other. Using sensors such as cameras, radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected, as well as the relative speed. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, 3 car following…
Universitat Pompeu Fabra（庞培法布拉大学）；Inria（法国国家信息与自动化研究所）；Université de Bordeaux（波尔多大学）；ENSTA ParisTech（巴黎高等先进技术学院）；Institució Catalana de Recerca i Estudis Avançats（加泰罗尼亚研究与高等研究院）；Santa Fe Institute（圣塔菲研究所）
Humans have been able to tackle biosphere complexities by acting as ecosystem engineers, profoundly changing the flows of matter, energy and information. This includes major innovations that allowed to reduce and control the impact of extreme events. Modelling the evolution of such adaptive dynamics can be challenging given the potentially large number of individual and environmental variables involved. This paper shows how to address this problem by using fire as the source of external, bursting and wide fluctuations. Fire propagates on a spatial landscape where a group of agents harvest and …
PerceptIn（感知创新公司）；South China University of Technology（华南理工大学）；Northwestern University（西北大学）
The infrastructure-vehicle cooperative autonomous driving approach depends on the cooperation between intelligent roads and intelligent vehicles. This approach is not only safer but also more economical compared to the traditional on-vehicle-only autonomous driving approach. In this paper, we introduce our real-world deployment experiences of cooperative autonomous driving, and delve into the details of new challenges and opportunities. Specifically, based on our progress towards commercial deployment, we follow a three-stage development roadmap of the cooperative autonomous driving approach:i…
University of Oxford（牛津大学）；University College London（伦敦大学学院）；Meta AI
Collective intelligence is a fundamental trait shared by several species of living organisms. It has allowed them to thrive in the diverse environmental conditions that exist on our planet. From simple organisations in an ant colony to complex systems in human groups, collective intelligence is vital for solving complex survival tasks. As is commonly observed, such natural systems are flexible to changes in their structure. Specifically, they exhibit a high degree of generalization when the abilities or the total number of agents changes within a system. We term this phenomenon as Combinatoria…
A Cascading Cooperative Multi-agent Framework for On-ramp Merging Control Integrating Large Language Models
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Department of Computer Science & Engineering, University of Minnesota（明尼苏达大学计算机科学与工程系）；Department of Mechanical Engineering & Materials Science, Yale University（耶鲁大学机械工程与材料科学系）；Computer Vision, BYD COMPANY LIMITED（计算机视觉，比亚迪有限公司）；School of Architecture, University of Texas at Austin（德克萨斯大学奥斯汀分校建筑学院）；Faculty of Applied Science & Engineering, University of Toronto（多伦多大学应用科学与工程学院）
From Grunts to Lexicons: Emergent Language from Cooperative Foraging
Institute for Infocomm Research (I 2 {}^{\text{2}} R), A ∗ STAR, Singapore（新加坡信息与通信研究院）；College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算机科学与数据科学学院）；Department of Computer Science, The University of Manchester, United Kingdom（曼彻斯特大学计算机科学系）；Centre for Frontier AI Research (CFAR), A ∗ STAR, Singapore（前沿人工智能研究中心）；Sakana AI, Japan（日本Sakana AI）
The demand for accurate localization has risen in recent years to enable the emerging of autonomous vehicles. To have these vehicles in the traffic ecosystem of smart cities, the need for an accurate positioning system is emphasized. To realize accurate positioning, collaborative localization plays an important role. This type of localization computes range measurements between vehicles and improves the accuracy of position by correcting the possibly faulty values of one of them by using the more accurate values of the other. 5G signals with the technology of Millimeter Wave (mmWave) support p…
Université Paris-Saclay（巴黎萨克雷大学）；Ubisoft（育碧）；University of Granada（格拉纳达大学）
While Explainable Artificial Intelligence (XAI) is increasingly expanding more areas of application, little has been applied to make deep Reinforcement Learning (RL) more comprehensible. As RL becomes ubiquitous and used in critical and general public applications, it is essential to develop methods that make it better understood and more interpretable. This study proposes a novel approach to explain cooperative strategies in multiagent RL using Shapley values, a game theory concept used in XAI that successfully explains the rationale behind decisions taken by Machine Learning algorithms. Thro…
Institute for Data Science & Analytics（数据科学与分析研究所）；Bocconi University（博科尼大学）；Toulouse School of Economics（图卢兹经济学院）；Artificial and Natural Intelligence Toulouse Institute（图卢兹人工智能与自然智能研究所）
We consider the problem of asynchronous online combinatorial optimization on a network of communicating agents. At each time step, some of the agents are stochastically activated, requested to make a prediction, and the system pays the corresponding loss. Then, neighbors of active agents receive semi-bandit feedback and exchange some succinct local information. The goal is to minimize the network regret, defined as the difference between the cumulative loss of the predictions of active agents and that of the best action in hindsight, selected from a combinatorial decision set. The main challen…
National Institute of Informatics（国立情报学研究所）；Carnegie Mellon University（卡内基梅隆大学）；General Motors Company（通用汽车公司）
Connected and Automated Vehicles (CAVs) have real-time information from the surrounding environment by using local on-board sensors, V2X (Vehicle-to-Everything) communications, pre-loaded vehicle-specific lookup tables, and map database. CAVs are capable of improving energy efficiency by incorporating these information. In particular, Eco-Cruise and Eco-Lane Selection on highways and/or motorways have immense potential to save energy, because there are generally fewer traffic controllers and the vehicles keep moving in general. In this paper, we present a cooperative and energy-efficient lane-…
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Shenzhen CyberAray Network Technology Co., Ltd, China（深圳 CyberAray 网络技术有限公司）；Information and Communication Branch of State Grid Hebei Electric Power Co., Ltd, China（国网河北电力信息通信分部）；Zhejiang Lab as well as Zhejiang University（浙江实验室以及浙江大学）
Federated learning (FL) in multi-service provider (SP) ecosystems is fundamentally hampered by non-cooperative dynamics, where privacy constraints and competing interests preclude the centralized optimization of multi-SP communication and computation resources. In this paper, we introduce PAC-MCoFL, a game-theoretic multi-agent reinforcement learning (MARL) framework where SPs act as agents to jointly optimize client assignment, adaptive quantization, and resource allocation. Within the framework, we integrate Pareto Actor-Critic (PAC) principles with expectile regression, enabling agents to c…
Highly automated, cooperative driving vehicles will allow for a more fluid flow of traffic, resulting in more efficient, eco-friendly and safe traffic situations. The automotive industry however, is safety critical and current safety standards were not designed to deal with cooperative driving. In this paper, we apply a modern safety analysis method, Systems-Theoretic Process Analysis, in the context of cooperative driving as part of the Grand Cooperative Driving Challenge (GCDC) and present our reflections on the method.
The problem of self-tuning control of cooperative manipulators forming a closed kinematic chain in the presence of an inaccurate kinematics model is addressed using adaptive machine learning. The kinematic parameters pertaining to the relative position/orientation uncertainties of the interconnected manipulators are updated online by two cascaded estimators in order to tune a cooperative controller for achieving accurate motion tracking with minimum-norm actuation force. This technique permits accurate calibration of the relative kinematics of the involved manipulators without needing high pre…
SynAgent: Generalizable Cooperative Humanoid Manipulation via Solo-to-Cooperative Agent Synergy
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Department of Automation, Tsinghua University（清华大学自动化系）；College of Artificial Intelligence, Nanjing Forestry University（南京林业大学人工智能学院）
DeReCo: Decoupling Representation and Coordination Learning for Object-Adaptive Decentralized Multi-Robot Cooperative Transport
Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST)（信息科学系，科学技术研究生院，科学与技术国立研究所（NAIST））；Department of Cognitive Robotics, Faculty of Mechanical Engineering, Delft University of Technology（认知机器人系，机械工程学院，代尔夫特理工大学）
