This paper presents an experimental study of a path-tracking framework for autonomous vehicles in which the lateral control command is applied to a dynamic control point along the wheelbase. Instead of enforcing a fixed reference at either the front or rear axle, the proposed method continuously interpolates between both, enabling smooth adaptation across driving contexts, including low-speed maneuvers and reverse motion. The lateral steering command is obtained by barycentric blending of two complementary controllers: a front-axle Stanley formulation and a rear-axle curvature-based geometric controller, yielding continuous transitions in steering behavior and improved tracking stability. In addition, we introduce a curvature-aware longitudinal control strategy based on virtual track borders and ray-tracing, which converts upcoming geometric constraints into a virtual obstacle distance and regulates speed accordingly. The complete approach is implemented in a unified control stack and validated in simulation and on a real autonomous vehicle equipped with GPS-RTK, radar, odometry, and IMU. The results in closed-loop tracking and backward maneuvers show improved trajectory accuracy, smoother steering profiles, and increased adaptability compared to fixed control-point baselines.
This paper presents a control method and trajectory planner for vehicles with first-order nonholonomic constraints that guarantee asymptotic convergence to a time-indexed trajectory. To overcome the nonholonomic constraint, a fixed point in front of the vehicle can be controlled to track a desired trajectory, albeit with a steady-state error. To eliminate steady state error, a sufficiently smooth trajectory is reformulated for the new reference point such that, when tracking the new trajectory, the vehicle asymptotically converges to the original trajectory. The resulting zero-error tracking l…
National Key Lab of Vehicular Transmission, School of Mechanical Engineering, Beijing Institute of Technology（北京理工大学机械工程学院车辆传动国家重点实验室）
Model predictive control (MPC) is widely used for path tracking of autonomous vehicles due to its ability to handle various types of constraints. However, a considerable predictive error exists because of the error of mathematics model or the model linearization. In this paper, we propose a framework combining the MPC with a learning-based error estimator and a feedforward compensator to improve the path tracking accuracy. An extreme learning machine is implemented to estimate the model based predictive error from vehicle state feedback information. Offline training data is collected from a ve…
Beijing Institute of Technology（北京理工大学）；Delft University of Technology（代尔夫特理工大学）
This paper proposes a life-long adaptive path tracking policy learning method for autonomous vehicles that can self-evolve and self-adapt with multi-task knowledge. Firstly, the proposed method can learn a model-free control policy for path tracking directly from the historical driving experience, where the property of vehicle dynamics and corresponding control strategy can be learned simultaneously. Secondly, by utilizing the life-long learning method, the proposed method can learn the policy with task-incremental knowledge without encountering catastrophic forgetting. Thus, with continual mu…
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；University of Saarland（萨尔大学）；Technical University of Berlin（柏林工业大学）
The aim of this work is to control the longitudinal position of an autonomous vehicle with an internal combustion engine. The powertrain has an inherent dead-time characteristic and constraints on physical states apply since the vehicle is neither able to accelerate arbitrarily strong, nor to drive arbitrarily fast. A model predictive controller (MPC) is able to cope with both of the aforementioned system properties. MPC heavily relies on a model and therefore a strategy on how to obtain multiple linear state space prediction models of the nonlinear system via input/output data system identifi…
The vision of unmanned aerial vehicles is very significant for UAV-related applications such as search and rescue, landing on a moving platform, etc. In this work, we have developed an integrated system for the UAV landing on the moving platform, and the UAV object detection with tracking in the complicated environment. Firstly, we have proposed a robust LoG-based deep neural network for object detection and tracking, which has great advantages in robustness to object scale and illuminations compared with typical deep network-based approaches. Then, we have also improved based on the original …
We present a scalable distributed target tracking algorithm based on the alternating direction method of multipliers that is well-suited for a fleet of autonomous cars communicating over a vehicle-to-vehicle network. Each sensing vehicle communicates with its neighbors to execute iterations of a Kalman filter-like update such that each agent's estimate approximates the centralized maximum a posteriori estimate without requiring the communication of measurements. We show that our method outperforms the Consensus Kalman Filter in recovering the centralized estimate given a fixed communication ba…
This paper presents a visual tracking system that is capable or running real time on-board a small UAV (Unmanned Aerial Vehicle). The tracking system is computationally efficient and invariant to lighting changes and rotation of the object or the camera. Detection and tracking is autonomously carried out on the payload computer and there are two different methods for creation of the image patches. The first method starts detecting and tracking using a stored image patch created prior to flight with previous flight data. The second method allows the operator on the ground to select the interest…
Iterative learning control has been successfully used for several decades to improve the performance of control systems that perform a single repeated task. Using information from prior control executions, learning controllers gradually determine open-loop control inputs whose reference tracking performance can exceed that of traditional feedback-feedforward control algorithms. This paper considers iterative learning control for a previously unexplored field: autonomous racing. Racecars are driven multiple laps around the same sequence of turns while operating near the physical limits of tire-…
Instituto de Telecomunicações（电信研究所）；ISCTE-Instituto Universitário de Lisboa（里斯本大学学院）；CTS-UNINOVA
This paper presents a monocular vision system susceptible of being installed in unmanned small and medium-sized aerial vehicles built to perform missions in forest environments (e.g., search and rescue). The proposed system extends a previous monocular-based technique for trail detection and tracking so as to take into account volumetric data acquired from a Visual SLAM algorithm and, as a result, to increase its sturdiness upon challenging trails. The experimental results, obtained via a set of 12 videos recorded with a camera installed in a tele-operated, unmanned small-sized aerial vehicle,…
University of Nevada（内华达大学）；NASA Ames Research Center（NASA艾姆斯研究中心）
Wildland fire fighting is a very dangerous job, and the lack of information of the fire front is one of main reasons that causes many accidents. Using unmanned aerial vehicle (UAV) to cover wildfire is promising because it can replace human in hazardous fire tracking and save operation costs significantly. In this paper we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collision as well as cooperate well wit…
Accurate and robust tracking of surrounding road participants plays an important role in autonomous driving. However, there is usually no prior knowledge of the number of tracking targets due to object emergence, object disappearance and false alarms. To overcome this challenge, we propose a generic vehicle tracking framework based on modified mixture particle filter, which can make the number of tracking targets adaptive to real-time observations and track all the vehicles within sensor range simultaneously in a uniform architecture without explicit data association. Each object corresponds t…
LADARs mounted on mobile platforms produce a wealth of precise range data on the surrounding objects and vehicles. The challenge we address is to infer from these raw LADAR data the location and orientation of nearby vehicles. We propose a novel view-dependent adaptive matched filter for obtaining fast and precise measurements of target vehicle pose. We derive an analytic expression for the matching function which we optimize to obtain target pose and size. Our algorithm is fast, robust and simple to implement compared to other methods. When used as the measurement component of a tracker on an…
Assessing collision risk is a critical challenge to effective traffic safety management. The deployment of unmanned aerial vehicles (UAVs) to address this issue has shown much promise, given their wide visual field and movement flexibility. This research demonstrates the application of UAVs and V2X connectivity to track the movement of road users and assess potential collisions at intersections. The study uses videos captured by UAVs. The proposed method combines deep-learning based tracking algorithms and time-to-collision tasks. The results not only provide beneficial information for vehicle…
New York University, Tandon School of Engineering（纽约大学坦登工程学院）；Technology Innovation Institute（技术创新研究院）
In this paper, we address the vision-based detection and tracking problems of multiple aerial vehicles using a single camera and Inertial Measurement Unit (IMU) as well as the corresponding perception consensus problem (i.e., uniqueness and identical IDs across all observing agents). We design several vision-based decentralized Bayesian multi-tracking filtering strategies to resolve the association between the incoming unsorted measurements obtained by a visual detector algorithm and the tracked agents. We compare their accuracy in different operating conditions as well as their scalability ac…
Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin（德克萨斯大学奥斯汀分校土木、建筑与环境工程系）；Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology（巴布里·纳希里万理工大学电气与计算机工程学院）；Transportation Research Institute (IMOB), UHasselt-Hasselt University（乌赫斯泰特大学交通研究所）；Faculty of Mechanical Engineering, Babol Noshirvani University of Technology（巴布里·纳希里万理工大学机械工程学院）；Department of Computer Engineering, Amirkabir University of Technology（阿姆irkabir技术大学计算机工程系）
This paper presents an end-to-end approach for tracking static and dynamic objects for an autonomous vehicle driving through crowded urban environments. Unlike traditional approaches to tracking, this method is learned end-to-end, and is able to directly predict a full unoccluded occupancy grid map from raw laser input data. Inspired by the recently presented DeepTracking approach [Ondruska, 2016], we employ a recurrent neural network (RNN) to capture the temporal evolution of the state of the environment, and propose to use Spatial Transformer modules to exploit estimates of the egomotion of …
Tongji University（同济大学）；Hong Kong Polytechnic University（香港理工大学）
Object tracking has been broadly applied in unmanned aerial vehicle (UAV) tasks in recent years. However, existing algorithms still face difficulties such as partial occlusion, clutter background, and other challenging visual factors. Inspired by the cutting-edge attention mechanisms, a novel object tracking framework is proposed to leverage multi-level visual attention. Three primary attention, i.e., contextual attention, dimensional attention, and spatiotemporal attention, are integrated into the training and detection stages of correlation filter-based tracking pipeline. Therefore, the prop…
University of Nevada, Reno（内华达大学里诺分校）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）
Wild-land fire fighting is a hazardous job. A key task for firefighters is to observe the "fire front" to chart the progress of the fire and areas that will likely spread next. Lack of information of the fire front causes many accidents. Using Unmanned Aerial Vehicles (UAVs) to cover wildfire is promising because it can replace humans in hazardous fire tracking and significantly reduce operation costs. In this paper we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designe…
New Jersey Institute of Technology（新泽西理工学院）；University of Connecticut（康涅狄格大学）
Unmanned Aerial Vehicles(UAVs) are attaining more and more maneuverability and sensory ability as a promising teleoperation platform for intelligent interaction with the environments. This work presents a novel 5-degree-of-freedom (DoF) unmanned aerial vehicle (UAV) cyber-physical system for aerial manipulation. This UAV's body is capable of exerting powerful propulsion force in the longitudinal direction, decoupling the translational dynamics and the rotational dynamics on the longitudinal plane. A high-level impedance control law is proposed to drive the vehicle for trajectory tracking and i…
This work presents an online learning-based control method for improved trajectory tracking of unmanned aerial vehicles using both deep learning and expert knowledge. The proposed method does not require the exact model of the system to be controlled, and it is robust against variations in system dynamics as well as operational uncertainties. The learning is divided into two phases: offline (pre-)training and online (post-)training. In the former, a conventional controller performs a set of trajectories and, based on the input-output dataset, the deep neural network (DNN)-based controller is t…
The ability of an autonomous vehicle to perform 3D tracking is essential for safe planing and navigation in cluttered environments. The main challenges for multi-object tracking (MOT) in autonomous driving applications reside in the inherent uncertainties regarding the number of objects, when and where the objects may appear and disappear, and uncertainties regarding objects' states. Random finite set (RFS) based approaches can naturally model these uncertainties accurately and elegantly, and they have been widely used in radar-based tracking applications. In this work, we developed an RFS-bas…
Micro unmanned aerial vehicles (mUAV) became very common in recent years. As a result of their widespread usage, when they are flown by hobbyists illegally, crucial risks are imposed and such mUAVs need to be sensed by security systems. Furthermore, the sensing of mUAVs are essential for also swarm robotics research where the individuals in a flock of robots require systems to sense and localize each other for coordinated operation. In order to obtain such systems, there are studies to detect mUAVs utilizing different sensing mediums, such as vision, infrared and sound signals, and small-scale…
Huawei Turkey R&D Center（华为土耳其研发中心）；Istanbul University – Cerrahpasa（伊斯坦布尔大学-塞拉赫帕萨分校）
In this paper, a combat Unmanned Air Vehicle (UAV) is modeled in the simulation environment. The rotary wing UAV is successfully performed various tasks such as locking on the targets, tracking, and sharing the relevant data with surrounding vehicles. Different software technologies such as API communication, ground control station configuration, autonomous movement algorithms, computer vision, and deep learning are employed.
Aerial tracking, which has exhibited its omnipresent dedication and splendid performance, is one of the most active applications in the remote sensing field. Especially, unmanned aerial vehicle (UAV)-based remote sensing system, equipped with a visual tracking approach, has been widely used in aviation, navigation, agriculture,transportation, and public security, etc. As is mentioned above, the UAV-based aerial tracking platform has been gradually developed from research to practical application stage, reaching one of the main aerial remote sensing technologies in the future. However, due to t…
Estimating the states of surrounding traffic participants stays at the core of autonomous driving. In this paper, we study a novel setting of this problem: model-free single-object tracking (SOT), which takes the object state in the first frame as input, and jointly solves state estimation and tracking in subsequent frames. The main purpose for this new setting is to break the strong limitation of the popular "detection and tracking" scheme in multi-object tracking. Moreover, we notice that shape completion by overlaying the point clouds, which is a by-product of our proposed task, not only im…
Argo AI（Argo AI公司）；Carnegie Mellon University（卡内基梅隆大学）；Georgia Institute of Technology（佐治亚理工学院）
We present Argoverse -- two datasets designed to support autonomous vehicle machine learning tasks such as 3D tracking and motion forecasting. Argoverse was collected by a fleet of autonomous vehicles in Pittsburgh and Miami. The Argoverse 3D Tracking dataset includes 360 degree images from 7 cameras with overlapping fields of view, 3D point clouds from long range LiDAR, 6-DOF pose, and 3D track annotations. Notably, it is the only modern AV dataset that provides forward-facing stereo imagery. The Argoverse Motion Forecasting dataset includes more than 300,000 5-second tracked scenarios with a…
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles
KEMLG Research Group, Universitat Politècnica de Catalunya Barcelona, Spain（凯姆尔格研究组，巴塞罗那理工大学，西班牙）；Instituto de Ciencias del Mar, Consejo Superior de Investigaciones Científicas, Barcelona, Spain（海洋科学研究所，西班牙国家科学研究委员会，巴塞罗那，西班牙）；KEMLG Research Group, Universitat Politècnica de Catalunya (UPC), and with the HPAI group at Barcelona Supercomputing Center (BSC), Barcelona, Spain（凯姆尔格研究组，巴塞罗那理工大学（UPC），以及巴塞罗那超级计算中心（BSC）的HPAI组，巴塞罗那，西班牙）
National Institute of Technology, Rourkela（鲁尔克拉国立技术学院）；University of Alberta（阿尔伯塔大学）
Deep learning has recently started being applied to visual tracking of generic objects in video streams. For the purposes of robotics applications, it is very important for a target tracker to recover its track if it is lost due to heavy or prolonged occlusions or motion blur of the target. We present a real-time approach which fuses a generic target tracker and object detection module with a target re-identification module. Our work focuses on improving the performance of Convolutional Recurrent Neural Network-based object trackers in cases where the object of interest belongs to the category…
Delft University of Technology（代尔夫特理工大学）；University of Leuven (KU Leuven)（鲁汶大学）
This paper proposes a new robust trajectory tracking error-based control approach for unmanned ground vehicles. A trajectory tracking error-based model is used to design a linear model predictive controller and its control action is combined with feedforward and robust control actions. The experimental results show that the proposed control structure is capable to let a tractor-trailer system track both linear and curvilinear target trajectories with low tracking error.
The purpose of this project is to build a system that will quickly track the location of a stolen vehicle, thereby reducing the cost and effort of police. Moreover, the vehicle's computer system can be controlled remotely by the owners of the vehicle or police. More precisely, the goal of this work is to design a, develop remote control of the vehicle, and find the locations with Latitude (LAT) and Longitude (LONG).
The motion planning and tracking control techniques of unmanned underwater vehicles (UUV) are fundamentally significant for efficient and robust UUV navigation, which is crucial for underwater rescue, facility maintenance, marine resource exploration, aquatic recreation, etc. Studies on UUV motion planning and tracking control have been growing rapidly worldwide, which are usually sorted into the following topics: task assignment of the multi-UUV system, UUV path planning and UUV trajectory tracking. This paper provides a comprehensive review of conventional and intelligent technologies for mo…
This paper presents a novel model-reference reinforcement learning algorithm for the intelligent tracking control of uncertain autonomous surface vehicles with collision avoidance. The proposed control algorithm combines a conventional control method with reinforcement learning to enhance control accuracy and intelligence. In the proposed control design, a nominal system is considered for the design of a baseline tracking controller using a conventional control approach. The nominal system also defines the desired behaviour of uncertain autonomous surface vehicles in an obstacle-free environme…
University of Guelph（圭尔夫大学）；McMaster University（麦克马斯特大学）
Tracking control has been a vital research topic in robotics. This paper presents a novel hybrid control strategy for an unmanned underwater vehicle (UUV) based on a bioinspired neural dynamics model. An enhanced backstepping kinematic control strategy is first developed to avoid sharp velocity jumps and provides smooth velocity commands relative to conventional methods. Then, a novel sliding mode control is proposed, which is capable of providing smooth and continuous torque commands free from chattering. In comparative studies, the proposed combined hybrid control strategy has ensured contro…
An intelligent control strategy is proposed to eliminate the actuator saturation problem that exists in the trajectory tracking process of unmanned underwater vehicles (UUV). The control strategy consists of two parts: for the kinematic modeling part, a fuzzy logic-refined backstepping control is developed to achieve control velocities within acceptable ranges and errors of small fluctuations; on the basis of the velocities deducted by the improved kinematic control, the sliding mode control (SMC) is introduced in the dynamic modeling to obtain corresponding torques and forces that should be a…
University of Guelph（圭尔夫大学）；China Ship Scientific Research Center（中国船舶科学研究中心）
This paper proposes an intelligent fault-tolerant control (FTC) strategy to tackle the trajectory tracking problem of an underwater vehicle (UV) under thruster damage (power loss) cases and meanwhile resolve the actuator saturation brought by the vehicle's physical constraints. In the proposed control strategy, the trajectory tracking component is formed by a refined backstepping algorithm that controls the velocity variation and a sliding mode control deducts the torque/force outputs; the fault-tolerant component is established based on a Grasshopper Optimization Algorithm (GOA), which provid…
This article is an overview of the various literature on path tracking methods and their implementation in simulation and realistic operating environments.The scope of this study includes analysis, implementation,tuning, and comparison of some selected path tracking methods commonly used in practice for trajectory tracking in autonomous vehicles. Many of these methods are applicable at low speed due to the linear assumption for the system model, and hence, some methods are also included that consider nonlinearities present in lateral vehicle dynamics during high-speed navigation. The performan…
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles
Systems and Control Laboratory, HUN-REN Inst. for Computer Science and Control（系统与控制实验室，HUN-REN计算机科学与控制研究所）；Control Systems Group, Eindhoven University of Technology（控制系统组，埃因霍温理工大学）；Vehicle Research Center, Széchenyi István University（车辆研究中心，塞梅尔维斯大学）；ETH Zürich（苏黎世联邦理工学院）
Vehicle teleoperation is an interesting feature in many fields. A typical problem of teleoperation is communication time delay which, together with actuator saturation and environmental disturbance, can cause a vehicle deviation from the target trajectory imposed by the human operator who imposes to the vehicle a steering wheel angle reference and a speed/acceleration reference. With predictive techniques, time-delay can be accounted at sufficient extent. But, in presence of disturbances, due to the absence of instantaneous haptic and visual feedback, human-operator steering command transmitte…
Safe mobility for unmanned ground vehicles requires reliable detection of other vehicles, along with precise estimates of their locations and trajectories. Here we describe the algorithms and system we have developed for accurate trajectory estimation of nearby vehicles using an onboard scanning LADAR. We introduce a variable-axis Ackerman steering model and compare this to an independent steering model. Then for robust tracking we propose a multi-hypothesis tracker that combines these kinematic models to leverage the strengths of each. When trajectories estimated with our techniques are input…
