在人类控制交通流中的自主平行式匝道合流，仍然是自动驾驶车辆控制中一个现存问题。现有的非学习型车辆控制解决方案主要依赖规则和优化方法，这些方法已被证明面临重大挑战。深度强化学习的最新进展展现出潜力并获得了显著的学术关注，然而现有的基于学习的方法对其他高速公路车辆关注不足，且常常依赖不准确的道路交通假设。此外，平行式场景很少被考虑。本文提出了一种新颖的基于学习的加速度和车道变换决策模型，该模型明确考虑了自车及其周围车辆（可能合作或不合作）的效用，以产生社会可接受的行为。新颖的奖励函数利用社会价值取向（Social Value Orientation）来加权车辆的社会合作水平，并分为自车效用和周围车辆效用，根据模型指定的社会价值取向进行加权。研究考虑了一条带有匝道的两车道高速公路，匝道分为锥形和并行式两段。仿真结果表明，在奖励函数设计中考虑周围车辆的重要性，并显示所提出的模型在碰撞方面与文献中的模型相当或更优，同时通过直接考虑合流对周围车辆的影响，引入了社会礼貌行为，避免了近距离事故和反社会行为。
英文摘要：
Autonomous parallel-style on-ramp merging in human controlled traffic continues to be an existing issue for autonomous vehicle control. Existing non-learning based solutions for vehicle control rely on rules and optimization primarily. These methods have been seen to present significant challenges. Recent advancements in Deep Reinforcement Learning have shown promise and have received significant academic interest however the available learning based approaches show inadequate attention to other highway vehicles and often rely on inaccurate road traffic assumptions. In addition, the parallel-style case is rarely considered. A novel learning based model for acceleration and lane change decision making that explicitly considers the utility to both the ego vehicle and its surrounding vehicles which may be cooperative or uncooperative to produce behaviour that is socially acceptable is proposed. The novel reward function makes use of Social Value Orientation to weight the vehicle's level of social cooperation and is divided into ego vehicle and surrounding vehicle utility which are weighted according to the model's designated Social Value Orientation. A two-lane highway with an on-ramp divided into a taper-style and parallel-style section is considered. Simulation results indicated the importance of considering surrounding vehicles in reward function design and show that the proposed model matches or surpasses those in literature in terms of collisions while also introducing socially courteous behaviour avoiding near misses and anti-social behaviour through direct consideration of the effect of merging on surrounding vehicles.
Michigan Technological University（密歇根理工大学）；University of Science and Technology of China（中国科学技术大学）
Despite the success of AI-enabled onboard perception, on-ramp merging has been one of the main challenges for autonomous driving. Due to limited sensing range of onboard sensors, a merging vehicle can hardly observe main road conditions and merge properly. By leveraging the wireless communications between connected and automated vehicles (CAVs), a merging CAV has potential to proactively obtain the intentions of nearby vehicles. However, CAVs can be prone to inaccurate observations, such as the noisy basic safety messages (BSM) and poor quality surveillance images. In this paper, we present a …
Prior work has looked at applying reinforcement learning and imitation learning approaches to autonomous driving scenarios, but either the safety or the efficiency of the algorithm is compromised. With the use of control barrier functions embedded into the reinforcement learning policy, we arrive at safe policies to optimize the performance of the autonomous driving vehicle. However, control barrier functions need a good approximation of the model of the car. We use probabilistic control barrier functions as an estimate of the model uncertainty. The algorithm is implemented as an online versio…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Texas at Austin（得克萨斯大学奥斯汀分校）
We consider the problem of designing an algorithm to allow a car to autonomously merge on to a highway from an on-ramp. Two broad classes of techniques have been proposed to solve motion planning problems in autonomous driving: Model Predictive Control (MPC) and Reinforcement Learning (RL). In this paper, we first establish the strengths and weaknesses of state-of-the-art MPC and RL-based techniques through simulations. We show that the performance of the RL agent is worse than that of the MPC solution from the perspective of safety and robustness to out-of-distribution traffic patterns, i.e.,…
Ramp merging is a critical maneuver for road safety and traffic efficiency. Most of the current automated driving systems developed by multiple automobile manufacturers and suppliers are typically limited to restricted access freeways only. Extending the automated mode to ramp merging zones presents substantial challenges. One is that the automated vehicle needs to incorporate a future objective (e.g. a successful and smooth merge) and optimize a long-term reward that is impacted by subsequent actions when executing the current action. Furthermore, the merging process involves interaction betw…
School of Mechanical Engineering, Beijing Institute of Technology（北京理工大学机械工程学院）；University of California at Berkeley（加州大学伯克利分校）
Humans make daily routine decisions based on their internal states in intricate interaction scenarios. This paper presents a probabilistically reconstructive learning approach to identify the internal states of multi-vehicle sequential interactions when merging at highway on-ramps. We treated the merging task's sequential decision as a dynamic, stochastic process and then integrated the internal states into an HMM-GMR model, a probabilistic combination of an extended Gaussian mixture regression (GMR) and hidden Markov models (HMM). We also developed a variant expectation-maximum (EM) algorithm…
Beijing Institute of Technology（北京理工大学）；University of California at Berkeley（加州大学伯克利分校）；McGill University（麦吉尔大学）
Merging at highway on-ramps while interacting with other human-driven vehicles is challenging for autonomous vehicles (AVs). An efficient route to this challenge requires exploring and exploiting knowledge of the interaction process from demonstrations by humans. However, it is unclear what information (or environmental states) is utilized by the human driver to guide their behavior throughout the whole merging process. This paper provides quantitative analysis and evaluation of the merging behavior at highway on-ramps with congested traffic in a volume of time and space. Two types of social i…
Delay-Aware Reinforcement Learning for Highway On-Ramp Merging under Stochastic Communication Latency
Department of Computer Science, George Washington University, Washington, D.C.（计算机科学系，乔治华盛顿大学，华盛顿特区）；Connected and Automated Vehicle Program Manager, Traffic Operations Division, Virginia Department of Transportation（连接与自动化车辆计划主任，交通运营处，弗吉尼亚州交通部）；Department of Mechanical & Aerospace Engineering, George Washington University, Washington, D.C.（机械与航空航天工程系，乔治华盛顿大学，华盛顿特区）
California PATH, University of California, Berkeley（加州大学伯克利分校加州PATH）
Multiple automakers have in development or in production automated driving systems (ADS) that offer freeway-pilot functions. This type of ADS is typically limited to restricted-access freeways only, that is, the transition from manual to automated modes takes place only after the ramp merging process is completed manually. One major challenge to extend the automation to ramp merging is that the automated vehicle needs to incorporate and optimize long-term objectives (e.g. successful and smooth merge) when near-term actions must be safely executed. Moreover, the merging process involves interac…
Fault-Tolerant MARL for CAVs under Observation Perturbations for Highway On-Ramp Merging
Department of Automation, Tsinghua University（自动化系，清华大学）；Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University（北京信息科学与技术国家研究中心（BNRist），清华大学）
PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X
School of Internet of Things Engineering, Jiangnan University（物联网工程学院，江南大学）；Department of Electronic Engineering, State Key laboratory of Space Network and Communications, Beijing National Research Center for Information Science and Technology, Tsinghua University（电子工程系，空间网络与通信国家重点实验室，北京信息科学与技术国家研究中心，清华大学）；Department of Computer Science, Brunel University（计算机科学系，布鲁内尔大学）；State Key Lab. of ISN and School of Telecommunications Engineering, Xidian University（信息国家实验室和电信工程学院，西安电子科技大学）；Department of Electronic Engineering, Shanghai Jiao Tong University（电子工程系，上海交通大学）；Department of Electrical and Computer Engineering, the Hong Kong University of Science and Technology (HKUST)（电气与计算机工程系，香港科技大学）
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）
Modeling the Lane-Change Reactions to Merging Vehicles for Highway On-Ramp Simulations
Global Center for Automotive Performance Simulation（全球汽车性能模拟中心）；Honda Research Institute, USA Inc.（美国本田研究所）；Virginia Tech Transportation Institute（弗吉尼亚理工大学交通研究所）
Coordinated Lane-Level Variable Speed Limits and Ramp Metering for Successive Weaving Segments Considering Merging/Diverging Risks: A Hybrid Model Predictive Control and Multi-Agent Reinforcement Learning Approach
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与学院）
Merging into the highway from the on-ramp is an essential scenario for automated driving. The decision-making under the scenario needs to balance the safety and efficiency performance to optimize a long-term objective, which is challenging due to the dynamic, stochastic, and adversarial characteristics. The Rule-based methods often lead to conservative driving on this task while the learning-based methods have difficulties meeting the safety requirements. In this paper, we propose an RL-based end-to-end decision-making method under a framework of offline training and online correction, called …
Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University（同济大学道路与交通工程教育部重点实验室）；China Railway Siyuan Survey and Design Group Co., Ltd.（中铁第四勘察设计院集团有限公司）；Institute of Logistics Science and Engineering, Shanghai Maritime University（上海海事大学物流科学与工程研究院）
The freeway on-ramp merging section is often identified as a crash-prone spot due to the high frequency of traffic conflicts. Very few traffic conflict analysis studies comprehensively consider different vehicle types at freeway merging section. Thus, the main objective of this study is to analyse conflicts between different vehicle types at freeway merging section. Field data are collected by Unmanned Aerial Vehicle (UAV) at merging areas in Shanghai, China. Vehicle extraction method is utilized to obtain vehicle trajectories. Time-to-collision (TTC) is utilized as the surrogate safety measur…
Modern driver assistance systems as well as autonomous vehicles take their decisions based on local maps of the environment. These maps include, for example, surrounding moving objects perceived by sensors as well as routes and navigation information. Current research in the field of environment mapping is concerned with two major challenges. The first one is the integration of information from different sources e.g. on-board sensors like radar, camera, ultrasound and lidar, offline map data or backend information. The second challenge comprises in finding an abstract representation of this ag…
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…
Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway. Previous studies generally update signal timings in real-time based on predefined traffic measures collected by point detectors, such as traffic volumes and occupancies. Comparing with point detectors, traffic cameras-which have been increasingly deployed on road networks-could cover larger areas and provide more detailed traffic information. In this work, we propose a deep reinforcement learning (DRL) method to explore the potential of tra…
Global Center for Automotive Performance Simulation（全球汽车性能模拟中心）；Honda Research Institute, USA Inc.（美国本田研究院）；Virginia Tech Transportation Institute（弗吉尼亚理工交通研究所）
Autonomous vehicles face tremendous challenges while interacting with human drivers in different kinds of scenarios. Developing control methods with safety guarantees while performing interactions with uncertainty is an ongoing research goal. In this paper, we present a real-time safe control framework using bi-level optimization with Control Barrier Function (CBF) that enables an autonomous ego vehicle to interact with human-driven cars in ramp merging scenarios with a consistent safety guarantee. In order to explicitly address motion uncertainty, we propose a novel extension of control barri…
German Aerospace Center (DLR)（德国航空航天中心）；Hochschule Emden/Leer, University of Applied Sciences（埃姆登/莱尔应用科学大学）
Connected and Automated Vehicles (CAVs) are envisioned to transform the future industrial and private transportation sectors. However, due to the system's enormous complexity, functional verification and validation of safety aspects are essential before the technology merges into the public domain. Therefore, in recent years, a scenario-driven approach has gained acceptance, emphasizing the requirement of a solid data basis of scenarios. The large-scale research facility Test Bed Lower Saxony (TFNDS) enables the provision of ample information for a database of scenarios on highways. For that p…
MR-LDM -- The Merge-Reactive Longitudinal Decision Model: Game Theoretic Human Decision Modeling for Interactive Sim Agents
Global Center for Automotive Performance Simulation（全球汽车性能模拟中心）；Honda Research Institute, USA Inc.（本田研究院（美国公司））；Division of Data and Analytics, Virginia Tech Transportation Institute（弗吉尼亚理工运输研究所数据分析部门）
Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road participants, AVs have to accurately predict the behavior of surrounding vehicles and plan accordingly. Such prediction should be probabilistic, to address the uncertainties in human behavior. Such prediction should also be interactive, since the distribution over all possible trajectories of the predicted vehicle depends not only on historical information, but also on future plans of other vehicles that interact with it. To achieve such interaction-aware predictions, we propose a probabilistic predic…
Safe Reinforcement Learning of Autonomous Highway Driving: A Unified Framework for Safety and Efficiency
School of Physics, Northeast Normal University（东北师范大学物理学院）；Clean Energy Automotive Engineering Center, School of Automotive Studies, Tongji University（同济大学汽车学院清洁能源汽车工程中心）；Mengshi Automobile Technology Company, Dongfeng Motor Corporation（东风汽车公司猛士汽车技术公司）
