Autonomous navigation in dense traffic scenarios remains challenging for autonomous vehicles (AVs) because the intentions of other drivers are not directly observable and AVs have to deal with a wide range of driving behaviors. To maneuver through dense traffic, AVs must be able to reason how their actions affect others (interaction model) and exploit this reasoning to navigate through dense traffic safely. This paper presents a novel framework for interaction-aware motion planning in dense traffic scenarios. We explore the connection between human driving behavior and their velocity changes when interacting. Hence, we propose to learn, via deep Reinforcement Learning (RL), an interaction-aware policy providing global guidance about the cooperativeness of other vehicles to an optimization-based planner ensuring safety and kinematic feasibility through constraint satisfaction. The learned policy can reason and guide the local optimization-based planner with interactive behavior to pro-actively merge in dense traffic while remaining safe in case the other vehicles do not yield. We present qualitative and quantitative results in highly interactive simulation environments (highway merging and unprotected left turns) against two baseline approaches, a learning-based and an optimization-based method. The presented results demonstrate that our method significantly reduces the number of collisions and increases the success rate with respect to both learning-based and optimization-based baselines.
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents
FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning
Machine Learning Research Lab, Volkswagen Group（大众集团机器学习研究实验室）；Technical University of Munich（慕尼黑技术大学）；Foundation Robotics Labs, Munich（慕尼黑基础机器人实验室）；Eötvös Loránd University（劳尔·艾尔多迪大学）
Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Tsinghua University（清华大学）；Tencent AI Lab（腾讯AI实验室）
Learning Diverse Policies with Soft Self-Generated Guidance
School of Mathematical Sciences, Beihang University（北京航空航天大学数学科学学院）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Zhongguancun Laboratory（中关村实验室）
GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents
SenseTime（商汤科技）；Baidu Inc.（百度公司）；SenseTime Research（商汤科技研究院）；Chinese Academy of Sciences（中国科学院）；Chinese University of Hong Kong（香港中文大学）；University of Cologne（科隆大学）；Arizona State University（亚利桑那州立大学）
CARLA Real Traffic Scenarios -- novel training ground and benchmark for autonomous driving
University of Warsaw（华沙大学）；Institute of Mathematics, Polish Academy of Sciences（波兰科学院数学研究所）；Jagiellonian University（雅盖隆大学）；Volkswagen AG（大众汽车集团）；Google（谷歌）
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology（麻省理工学院土木与环境工程系）；Laboratory for Information & Decision Systems, Massachusetts Institute of Technology（麻省理工学院信息与决策系统实验室）；Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology（首尔科学技术大学机械与汽车工程系）
EmbodiSteer: Steering Embodiment-Agnostic Visuomotor Policies with Joint-Space Guidance for Zero-Shot Cross-Embodiment Deployment
Department of Automation, Tsinghua University（清华大学自动化系）；Beijing Key Laboratory of Embodied Intelligence Systems（北京具身智能系统重点实验室）；Institute for Embodied Intelligence and Robotics, Tsinghua University（清华大学具身智能与机器人研究所）
TouchGuide: Inference-Time Steering of Visuomotor Policies via Touch Guidance
Shanghai Jiao Tong University（上海交通大学）；Xense Robotics（Xense机器人公司）；Sun Yat-sen University（中山大学）；Oxford（牛津大学）；Shanghai AI Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；UCSD（加州大学圣地亚哥分校）
RedLight-VLA: Models for traffic-rule grounding and behavioral emphasis in driving policies
Qualcomm Technologies, Inc.（高通技术公司）；Qualcomm Auto Ltd Sweden（瑞典高通汽车有限公司）；Qualcomm India Private Limited（高通印度私人有限公司）；Arriver System Software S.r.l.（Arriver系统软件有限公司）
Loiter UAV Reinsertion Guidance for Fixed-wing UAV Corridors
American Institute of Aeronautics and Astronautics, Inc.（美国航空航天学会）；Department of Mechatronics Engineering（机电工程系）；Department of Aerospace Engineering（航空航天工程系）
