For autonomous vehicles, effective behavior planning is crucial to ensure safety of the ego car. In many urban scenarios, it is hard to create sufficiently general heuristic rules, especially for challenging scenarios that some new human drivers find difficult. In this work, we propose a behavior planning structure based on reinforcement learning (RL) which is capable of performing autonomous vehicle behavior planning with a hierarchical structure in simulated urban environments. Application of the hierarchical structure allows the various layers of the behavior planning system to be satisfied. Our algorithms can perform better than heuristic-rule-based methods for elective decisions such as when to turn left between vehicles approaching from the opposite direction or possible lane-change when approaching an intersection due to lane blockage or delay in front of the ego car. Such behavior is hard to evaluate as correct or incorrect, but for some aggressive expert human drivers handle such scenarios effectively and quickly. On the other hand, compared to traditional RL methods, our algorithm is more sample-efficient, due to the use of a hybrid reward mechanism and heuristic exploration during the training process. The results also show that the proposed method converges to an optimal policy faster than traditional RL methods.
Real-Time Communication-Aware Ride-Sharing Route Planning for Urban Air Mobility: A Multi-Source Hybrid Attention Reinforcement Learning Approach
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（科学与工程学院，香港中文大学（深圳））；Xidian University State Key Laboratory of Integrated Services Network（西安电子科技大学集成服务网络国家重点实验室）；Department of Computer Science and Engineering, Kyung Hee University（庆北大学计算机科学与工程系）
GeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning
University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）；Concordia University（康考迪亚大学）；McGill University（麦吉尔大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；California Institute of Technology（加州理工学院）
Towards Urban Planing AI Agent in the Age of Agentic AI
University of Kansas（堪萨斯大学）；University of Florida（佛罗里达大学）；University of Central Florida（中央佛罗里达大学）；University of Alabama（阿拉巴马大学）；Arizona State University（亚利桑那州立大学）
CityPlanner: A Sandbox Agent for Executable Urban Planning
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；MIIT Key Laboratory of Data and Decision Intelligence, Beihang University（北京航空航天大学工业和信息化部数据与决策智能重点实验室）
FLUID: A Fine-Grained Lightweight Urban Signalized-Intersection Dataset of Dense Conflict Trajectories
School of Intelligent Systems Engineering, Sun Yat-sen University（智能系统工程学院，中山大学）；Guangdong Provincial Key Laboratory of Intelligent Transportation Systems（广东省智能交通系统重点实验室）；Pengcheng Laboratory（鹏城实验室）
OverFlowLight: Real-Time Gridlock Prevention and Traffic Signal Optimization for Urban Intersections
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Electronic Science and Technology of China（电子科技大学）；South China University of Technology（华南理工大学）；Beijing Xiaocheng Intelligent Computing（北京小城智能计算）；Tsinghua University（清华大学）；Lanzhou University（兰州大学）
University of Central Florida（中佛罗里达大学）；Virginia Tech（弗吉尼亚理工大学）；University of Macau（澳门大学）
Urban planning refers to the efforts of designing land-use configurations given a region. However, to obtain effective urban plans, urban experts have to spend much time and effort analyzing sophisticated planning constraints based on domain knowledge and personal experiences. To alleviate the heavy burden of them and produce consistent urban plans, we want to ask that can AI accelerate the urban planning process, so that human planners only adjust generated configurations for specific needs? The recent advance of deep generative models provides a possible answer, which inspires us to automate…
University of Central Florida（中佛罗里达大学）；Pinterest；Rutgers University（罗格斯大学）；Baidu Research（百度研究院）；Beihang University（北京航空航天大学）
The essential task of urban planning is to generate the optimal land-use configuration of a target area. However, traditional urban planning is time-consuming and labor-intensive. Deep generative learning gives us hope that we can automate this planning process and come up with the ideal urban plans. While remarkable achievements have been obtained, they have exhibited limitations in lacking awareness of: 1) the hierarchical dependencies between functional zones and spatial grids; 2) the peer dependencies among functional zones; and 3) human regulations to ensure the usability of generated con…
Stanford University（斯坦福大学）；Honda Research Institute（本田研究所）
Urban intersections represent a complex environment for autonomous vehicles with many sources of uncertainty. The vehicle must plan in a stochastic environment with potentially rapid changes in driver behavior. Providing an efficient strategy to navigate through urban intersections is a difficult task. This paper frames the problem of navigating unsignalized intersections as a partially observable Markov decision process (POMDP) and solves it using a Monte Carlo sampling method. Empirical results in simulation show that the resulting policy outperforms a threshold-based heuristic strategy on s…
Low-altitude urban flight planning for small Unmanned Aircraft Systems (UAS) requires accurate vehicle, environment maps, and risk models to assure flight plans consider the urban landscape as well as airspace constraints. This paper presents a suite of motion planning metrics designed for small UAS urban flight. We define map-based and path-based metrics to holistically characterize motion plan quality. Proposed metrics are examined in the context of representative geometric, graph-based, and sampling-based motion planners applied to a multicopter small UAS. A novel multi-objective heuristic …
Autonomous vehicles need to plan at the task level to compute a sequence of symbolic actions, such as merging left and turning right, to fulfill people's service requests, where efficiency is the main concern. At the same time, the vehicles must compute continuous trajectories to perform actions at the motion level, where safety is the most important. Task-motion planning in autonomous driving faces the problem of maximizing task-level efficiency while ensuring motion-level safety. To this end, we develop algorithm Task-Motion Planning for Urban Driving (TMPUD) that, for the first time, enable…
University of Michigan（密歇根大学）；Ford Center for Autonomous Vehicles Laboratory（福特自动驾驶汽车实验室）
This paper presents a novel dataset titled PedX, a large-scale multimodal collection of pedestrians at complex urban intersections. PedX consists of more than 5,000 pairs of high-resolution (12MP) stereo images and LiDAR data along with providing 2D and 3D labels of pedestrians. We also present a novel 3D model fitting algorithm for automatic 3D labeling harnessing constraints across different modalities and novel shape and temporal priors. All annotated 3D pedestrians are localized into the real-world metric space, and the generated 3D models are validated using a mocap system configured in a…
Honda Research Institute (HRI) Europe（本田欧洲研究院（HRI Europe））；Eindhoven University of Technology (TU/e)（埃因霍温理工大学（TU/e））
We consider the problem of intelligently navigating through complex traffic. Urban situations are defined by the underlying map structure and special regulatory objects of e.g. a stop line or crosswalk. Thereon dynamic vehicles (cars, bicycles, etc.) move forward, while trying to keep accident risks low. Especially at intersections, the combination and interaction of traffic elements is diverse and human drivers need to focus on specific elements which are critical for their behavior. To support the analysis, we present in this paper the so-called Risk Navigation System (RNS). RNS leverages a …
OPPO US Research Center（OPPO美国研究中心）；Cornell University（康奈尔大学）
This paper presents a novel framework for planning in unknown and occluded urban spaces. We specifically focus on turns and intersections where occlusions significantly impact navigability. Our approach uses an inpainting model to fill in a sparse, occluded, semantic lidar point cloud and plans dynamically feasible paths for a vehicle to traverse through the open and inpainted spaces. We demonstrate our approach using a car's lidar data with real-time occlusions, and show that by inpainting occluded areas, we can plan longer paths, with more turn options compared to without inpainting; in addi…
Massachusetts Institute of Technology（麻省理工学院）；CTU in Prague（布拉格捷克理工大学）
Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is revie…
FZI Research Center for Information Technology（FZI信息技术研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Motion planners take uncertain information about the environment as an input. The environment information is often quite noisy and has a tendency to contain false positive object detection. State-of-the-art motion planners consider all objects alike, thus producing overcautious behavior. In this paper we present a planning approach that considers alternative maneuvers in a combined fashion and plans a motion that is formed by the probabilities of those alternatives. The proposed planner can smoothly react to objects with low existence probability while remaining collision-free in case their ex…
In unstructured urban canals, regulation-aware interactions with other vessels are essential for collision avoidance and social compliance. In this paper, we propose a regulations aware motion planning framework for Autonomous Surface Vessels (ASVs) that accounts for dynamic and static obstacles. Our method builds upon local model predictive contouring control (LMPCC) to generate motion plans satisfying kino-dynamic and collision constraints in real-time while including regulation awareness. To incorporate regulations in the planning stage, we propose a cost function encouraging compliance wit…
This paper presents a novel context-based approach for pedestrian motion prediction in crowded, urban intersections, with the additional flexibility of prediction in similar, but new, environments. Previously, Chen et. al. combined Markovian-based and clustering-based approaches to learn motion primitives in a grid-based world and subsequently predict pedestrian trajectories by modeling the transition between learned primitives as a Gaussian Process (GP). This work extends that prior approach by incorporating semantic features from the environment (relative distance to curbside and status of p…
This paper studies the problem of fast and safe aerial payload transport by a single quadcopter in urban areas. The quadcopter payload system (QPS) is considered as a rigid body and modeled with a nonlinear dynamics. The urban area is modeled as an obstacle-laden environment with obstacle geometries obtained by incorporating realistic LIDAR data. Our approach for payload transport is decomposed into high-level motion planning and low-level trajectory control. For the low-level trajectory tracking, a feedback linearization control is applied to stably track the desired trajectory of the quadcop…
SRM Institute of Science and Technology（SRM科学技术研究所）
Autonomous Vehicles are currently being tested in a variety of scenarios. As we move towards Autonomous Vehicles, how should intersections look? To answer that question, we break down an intersection management into the different conundrums and scenarios involved in the trajectory planning and current approaches to solve them. Then, a brief analysis of current works in autonomous intersection is conducted. With a critical eye, we try to delve into the discrepancies of existing solutions while presenting some critical and important factors that have been addressed. Furthermore, open issues that…
Institute of Robotics and Machine Intelligence（机器人与机器智能研究所）；Poznan University of Technology（波兹南理工大学）
An efficient path planner for autonomous car-like vehicles should handle the strong kinematic constraints, particularly in confined spaces commonly encountered while maneuvering in city traffic, and should enable rapid planning, as the city traffic scenarios are highly dynamic. State-of-the-art planning algorithms handle such difficult cases at high computational cost, often yielding non-deterministic results. However, feasible local paths can be quickly generated leveraging the past planning experience gained in the same or similar environment. While learning through supervised training is pr…
RiskMap: A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment
The Hong Kong University of Science and Technology（香港科技大学）；Division of Emerging Interdisciplinary Areas（新兴跨学科领域）；Robotics and Autonomous Systems Thrust（机器人与自主系统方向）；Department of Electronic and Computer Engineering（电子与计算机工程系）
University of California San Diego（加州大学圣地亚哥分校）；San Diego State University（圣地亚哥州立大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Safety is a critical concern for the success of urban air mobility, especially in dynamic and uncertain environments. This paper proposes a path planning algorithm based on RRT in conjunction with chance constraints in the presence of uncertain obstacles. The chance-constrained formulation for Gaussian distributed obstacles is developed by converting the probabilistic constraints to deterministic constraints in terms of distribution parameters. The probabilistic feasible region at every time step can be established through the simulation of the system state and the evaluation of convex constra…
Loughborough University（拉夫堡大学）；Defence Science and Technology Laboratory（国防科学技术实验室）
The use of autonomous vehicles for chemical source localisation is a key enabling tool for disaster response teams to safely and efficiently deal with chemical emergencies. Whilst much work has been performed on source localisation using autonomous systems, most previous works have assumed an open environment or employed simplistic obstacle avoidance, separate to the estimation procedure. In this paper, we explore the coupling of the path planning task for both source term estimation and obstacle avoidance in a holistic framework. The proposed system intelligently produces potential gas sampli…
Efficient trajectory planning for urban intersections is currently one of the most challenging tasks for an Autonomous Vehicle (AV). Courteous behavior towards other traffic participants, the AV's comfort and its progression in the environment are the key aspects that determine the performance of trajectory planning algorithms. To capture these aspects, we propose a novel trajectory planning framework that ensures social compliance and simultaneously optimizes the AV's comfort subject to kinematic constraints. The framework combines a local continuous optimization approach and an efficient dri…
Toyota Research Institute（丰田研究院）；Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）
Predicting the motion of a driver's vehicle is crucial for advanced driving systems, enabling detection of potential risks towards shared control between the driver and automation systems. In this paper, we propose a variational neural network approach that predicts future driver trajectory distributions for the vehicle based on multiple sensors. Our predictor generates both a conditional variational distribution of future trajectories, as well as a confidence estimate for different time horizons. Our approach allows us to handle inherently uncertain situations, and reason about information ga…
Trajectory planning and control have historically been separated into two modules in automated driving stacks. Trajectory planning focuses on higher-level tasks like avoiding obstacles and staying on the road surface, whereas the controller tries its best to follow an ever changing reference trajectory. We argue that this separation is (1) flawed due to the mismatch between planned trajectories and what the controller can feasibly execute, and (2) unnecessary due to the flexibility of the model predictive control (MPC) paradigm. Instead, in this paper, we present a unified MPC-based trajectory…
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
This paper investigates the problem of planning a minimum-length tour for a three-dimensional Dubins airplane model to visually inspect a series of targets located on the ground or exterior surface of objects in an urban environment. Objects are 2.5D extruded polygons representing buildings or other structures. A visibility volume defines the set of admissible (occlusion-free) viewing locations for each target that satisfy feasible airspace and imaging constraints. The Dubins traveling salesperson problem with neighborhoods (DTSPN) is extended to three dimensions with visibility volumes that a…
We propose GameOpt: a novel hybrid approach to cooperative intersection control for dynamic, multi-lane, unsignalized intersections. Safely navigating these complex and accident prone intersections requires simultaneous trajectory planning and negotiation among drivers. GameOpt is a hybrid formulation that first uses an auction mechanism to generate a priority entrance sequence for every agent, followed by an optimization-based trajectory planner that computes velocity controls that satisfy the priority sequence. This coupling operates at real-time speeds of less than 10 milliseconds in high d…
City-Wide Low-Altitude Urban Air Mobility: A Scalable Global Path Planning Approach via Risk-Aware Multi-Scale Cell Decomposition
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；School of Aeronautics and Astronautics, Purdue University（普渡大学航空航天学院）
Enhanced Trust Region Sequential Convex Optimization for Multi-Drone Thermal Screening Trajectory Planning in Urban Environments
Institute of Automation, Chinese Academy of Science, China（中国科学院自动化研究所）；Beijing Institute of Technology, China（北京理工大学）；Institute of Automation, Chinese Academy of Sciences, China（中国科学院自动化研究所）；Tsinghua University, China（清华大学）
