Trajectory Data Mining Zheng, Yu
ACM transactions on intelligent systems and technology,
05/2015, Letnik:
6, Številka:
3
Journal Article
Recenzirano
The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, ...vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into
trajectory data mining
, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of
trajectory data mining
, providing a quick understanding of this field to the community.
This paper presents a hierarchical trajectory planning based on the integration of a sampling and an optimization method for urban autonomous driving. To manage a complex driving environment, the ...upper behavioral trajectory planner searches the macro-scale trajectory to determine the behavior of an autonomous car by using environment models, such as traffic control device and objects. This planner infers reasonable behavior and provides it to the motion trajectory planner. For planning the behavioral trajectory, the sampling-based approach is used due to its advantage of a free-form cost function for discrete models of the driving environments and simplification of the searching area. The lower motion trajectory planner determines the micro-scale trajectory based on the results of the upper trajectory planning with the environment model. The lower planner strongly considers vehicle dynamics within the planned behavior of the behavioral trajectory. Therefore, the planning space of the lower planner can be limited, allowing for improvement of the efficiency of the numerical optimization of the lower planner to find the best trajectory. For the motion trajectory planning, the numerical optimization is applied due to its advantages of a mathematical model for the continuous elements of the driving environments and low computation to converge minima in the convex function. The proposed algorithms of the sampling-based behavioral and optimization-based motion trajectory were evaluated through experiments in various scenarios of an urban area.
For most atmospheric or exo-atmospheric spacecraft flight scenarios, a well-designed trajectory is usually a key for stable flight and for improved guidance and control of the vehicle. Although ...extensive research work has been carried out on the design of spacecraft trajectories for different mission profiles and many effective tools were successfully developed for optimizing the flight path, it is only in the recent five years that there has been a growing interest in planning the flight trajectories with the consideration of multiple mission objectives and various model errors/uncertainties. It is worth noting that in many practical spacecraft guidance, navigation and control systems, multiple performance indices and different types of uncertainties must frequently be considered during the path planning phase. As a result, these requirements bring the development of multi-objective spacecraft trajectory optimization methods as well as stochastic spacecraft trajectory optimization algorithms. This paper aims to broadly review the state-of-the-art development in numerical multi-objective trajectory optimization algorithms and stochastic trajectory planning techniques for spacecraft flight operations. A brief description of the mathematical formulation of the problem is firstly introduced. Following that, various optimization methods that can be effective for solving spacecraft trajectory planning problems are reviewed, including the gradient-based methods, the convexification-based methods, and the evolutionary/metaheuristic methods. The multi-objective spacecraft trajectory optimization formulation, together with different class of multi-objective optimization algorithms, is then overviewed. The key features such as the advantages and disadvantages of these recently-developed multi-objective techniques are summarised. Moreover, attentions are given to extend the original deterministic problem to a stochastic version. Some robust optimization strategies are also outlined to deal with the stochastic trajectory planning formulation. In addition, a special focus will be given on the recent applications of the optimized trajectory. Finally, some conclusions are drawn and future research on the development of multi-objective and stochastic trajectory optimization techniques is discussed.
Unmanned aerial vehicle (UAV) communication is anticipated to be widely applied in the forthcoming fifth-generation wireless networks, due to its many advantages such as low cost, high mobility, and ...on-demand deployment. However, the broadcast and line-of-sight nature of air-to-ground wireless channels give rise to a new challenge on how to realize secure UAV communications with the destined nodes on the ground. This paper aims to tackle this challenge by applying the physical layer security technique. We consider both the downlink and uplink UAV communications with a ground node, namely, UAV-to-ground (U2G) and ground-to-UAV (G2U) communications, respectively, subject to a potential eavesdropper on the ground. In contrast to the existing literature on the wireless physical layer security only with the ground nodes at fixed or quasi-static locations, we exploit the high mobility of the UAV to proactively establish favorable and degraded channels for the legitimate and eavesdropping links, through its trajectory design. We formulate new problems to maximize the average secrecy rates of the U2G and G2U transmissions, by jointly optimizing the UAV's trajectory, and the transmit power of the legitimate transmitter over a given flight period of the UAV. Although the formulated problems are non-convex, we propose iterative algorithms to solve them efficiently by applying the block coordinate descent and successive convex optimization methods. Specifically, both the transmit power and UAV trajectory are optimized, with the other being fixed in an alternating manner, until the algorithms converge. The simulation results show that the proposed algorithms can improve the secrecy rates for both U2G and G2U communications, as compared to other benchmark schemes without power control and/or trajectory optimization.
Optimization-Based Collision Avoidance Zhang, Xiaojing; Liniger, Alexander; Borrelli, Francesco
IEEE transactions on control systems technology
29, Številka:
3
Journal Article
Recenzirano
Odprti dostop
This article presents a novel method for exactly reformulating nondifferentiable collision avoidance constraints into smooth, differentiable constraints using strong duality of convex optimization. ...We focus on a controlled object whose goal is to avoid obstacles while moving in an <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula>-dimensional space. The proposed reformulation is exact, does not introduce any approximations, and applies to general obstacles and controlled objects that can be represented as the union of convex sets. We connect our results with the notion of signed distance, which is widely used in traditional trajectory generation algorithms. Our method can be applied to generic navigation and trajectory planning tasks, and the smoothness property allows the use of general-purpose gradient- and Hessian-based optimization algorithms. Finally, in case a collision cannot be avoided, our framework allows us to find "least-intrusive" trajectories, measured in terms of penetration. We demonstrate the efficacy of our framework on an automated parking problem, where our numerical experiments suggest that the proposed method is robust and enables real-time optimization-based trajectory planning in tight environments. Sample code of our example is provided at https://github.com/XiaojingGeorgeZhang/OBCA .
Trajectory tracking control in the cut-in scenarios is challenging, since the autonomous vehicles have to follow the reference trajectory and cooperate with the cut-in vehicles. This paper proposes a ...human-centered trajectory tracking control strategy integrating driver behavior prediction for the cut-in scenarios and their transient processes. A recurrent neural network (RNN) with long short-term memory (LSTM) cells is used to predict the driver behaviors of the cut-in vehicle. Then, a model predictive control (MPC) approach considering the driver behaviors of the cut-in vehicle is designed to track the reference trajectory. The transient processes of the cut-in scenarios are considered for different cut-in behaviors. Moreover, the moving horizon estimator (MHE) is used to estimate the vehicle lateral velocity that is used in the controller. Human driver tests on a driving simulator show that the drivers' intention of the cut-in vehicle can be predicted by the RNN with LSTM cells. CarSim® simulation studies show the human-centered trajectory tracking controller can track the reference trajectory using the estimated vehicle lateral velocity. The autonomous vehicle can cooperate with the cut-in vehicle in different driving situations and obtain smooth transient processes of the cut-in scenarios.
With the development of GPS-enabled devices, wireless communication and storage technologies, trajectories representing the mobility of moving objects are accumulated at an unprecedented pace. They ...contain a large amount of temporal and spatial semantic information. A great deal of valuable information can be obtained by mining and analyzing the trajectory dataset. Trajectory clustering is one of the simplest and most powerful methods to obtain knowledge from trajectory data, which is based on the similarity measure between trajectories. The existing similarity measurement methods cannot fully utilize the specific features of trajectory itself when measuring the distance between trajectories. In this paper, an enhanced trajectory model is proposed and a new trajectory clustering algorithm is presented based on multi-feature trajectory similarity measure, which can maximize the similarity of trajectories in the same cluster, and can be used to better serve for applications including traffic monitoring and road congestion prediction. Both the intuitive visualization presentation and the experimental results on synthetic and real trajectory datasets show that, compared to existing methods, the proposed approach improves the accuracy and efficiency of trajectory clustering.
With the aims of safe, smart and sustainable future mobility, a personalized approach of trajectory planning and control based on user preferences is developed for lane-change of autonomous vehicles ...in this paper. First, a safe area during the lane change process is identified by using constraint Delaunay triangulation. Then, an improved rapidly-exploring Random Trees ( i -RRT) is developed with B-spline to generate the feasible trajectory cluster, which is subject to the safe area boundaries and the vehicle dynamics. To extract a personalized trajectory from this cluster, we firstly adopt the fuzzy linguistic preference relation (FLPR) method to identify users' preferences on driving, which can be reflected by their subjective objectives including driving safety, ride comfort and vehicle stability. Then, the technique for order preference by similarity to ideal situation (TOPSIS) is utilized to solve the multi-objective optimisation problem formulated by considering the user preferences. The algorithms proposed above are integrated, and both simulation and experimental validation are conducted under lane-change scenarios of autonomous driving. Simulation and experiment results show that proposed approach is able to successfully realize personalized trajectory planning and lane-change control, satisfying users' various preferences and simultaneously ensure vehicle safety, demonstrating its feasibility and effectiveness.
Solar polar detection can greatly enrich human understanding of the solar magnetic field. However, accurate polar flybys are rarely involved in previous trajectory designs due to the requirement of ...an enormous amount of velocity. This paper identifies gravity assist trajectories for solar polar detection missions in patched-conic models. An analysis model is proposed to judge whether a gravity assist sequence can achieve a required excess velocity (v∞) at the planet encounter for the solar polar flyby. In addition to the classical Jupiter gravity assist trajectory, some less conventional paths, such as Jupiter–Earth (JEnGA), Jupiter–Venus (JVnGA), and Venus–Earth–Venus (Vn1En2Vn3GA), are examined. These unconventional trajectories can reach final short-period orbits. For JEnGA and JVnGA trajectories, two different cases are found: in the transfer process, case 1 has a shorter perihelion, and case 2 has a higher inclination. In Vn1En2Vn3GA trajectory, the aphelion distance can be reduced from 5.2 AU to 3.7 AU compared with those trajectories mainly using Jupiter gravity assist. The final orbital periods vary from 83 days to 4.1 years in the trajectories provided, and the corresponding transfer times are from 46 years to 1.2 years. In the future, designers of practical missions can choose the appropriate trajectory according to their specific needs.
•Flybys sequence analytic method for solar polar detection missions is studied.•Venus and Earth flybys can shorten the final period of solar polar orbit.•Only using inner planets gravity assists can achieve solar polar flyby.•Relationship between flight time and final period of solar polar orbit is discussed.
A two-step strategy is developed for real-time trajectory planning of a hypersonic vehicle (HV) in the reentry phase. The first step generates the optimal trajectory for the HV using a recently ...proposed fuzzy multiobjective transcription method. In the second step, the optimally generated trajectories are utilized to train a deep neural network (DNN), which is then acted as the optimal command generator in real time. A detailed simulation study is carried out to verify the effectiveness and real-time applicability of the proposed integrated design. The DNN-driven controller is further compared against other optimization-based techniques existing in relative works. Moreover, extension works on the real-time trajectory planning of a six-degree-of-freedom HV model are performed. The results confirm the feasibility and reliability of applying the proposed method for the planning of the HV entry flight path in real time.
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