Empirical Analysis of Vehicle Tracking Algorithms for Extracting Integral Trajectories from Consecutive Videos

This study introduces a novel methodological frame-work for extracting integral vehicle trajectories from several consecutive pictures automatically. The frame-work contains camera observation, eliminating image distortions, video stabilising, stitching images, identify-ing vehicles and tracking vehicles. Observation videos of four sections in South Fengtai Road, Nanjing, Jiangsu Province, China are taken as a case study to validate the framework. As key points, six typical tracking algorithms, including boosting, CSRT, KCF, median flow, MIL and MOSSE, are compared in terms of tracking reliability, operational time, random access memory (RAM) usage and data accuracy. Main impact factors taken into con-sideration involve vehicle colours, zebra lines, lane lines, lamps, guide boards and image stitching seams. Based on empirical analysis, it is found that MOSSE requires the least operational time and RAM usage, whereas CSRT presents the best tracking reliability. In addition, all tracking algorithms produce reliable vehicle trajecto-ry and speed data if vehicles are tracked steadily

Recurrent Aligned Network for Generalized Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is a crucial component in computer vision and robotics, but remains challenging due to the domain shift problem. Previous studies have tried to tackle this problem by leveraging a portion of the trajectory data from the target domain to adapt the model. However, such domain adaptation methods are impractical in real-world scenarios, as it is infeasible to collect trajectory data from all potential target domains. In this paper, we study a task named generalized pedestrian trajectory prediction, with the aim of generalizing the model to unseen domains without accessing their trajectories. To tackle this task, we introduce a Recurrent Aligned Network~(RAN) to minimize the domain gap through domain alignment. Specifically, we devise a recurrent alignment module to effectively align the trajectory feature spaces at both time-state and time-sequence levels by the recurrent alignment strategy.Furthermore, we introduce a pre-aligned representation module to combine social interactions with the recurrent alignment strategy, which aims to consider social interactions during the alignment process instead of just target trajectories. We extensively evaluate our method and compare it with state-of-the-art methods on three widely used benchmarks. The experimental results demonstrate the superior generalization capability of our method. Our work not only fills the gap in the generalization setting for practical pedestrian trajectory prediction but also sets strong baselines in this field

Simulation-Based Assessment of Multilane Separate Freeways at Toll Station Area: A Case Study from Huludao Toll Station on Shenshan Freeway

To support the rapid growth of demand in passengers and freight, separating trucks and passenger-cars is a potential solution to improve traffic efficiency and safety. The primary purpose of this paper is to comprehensively assess the multilane separate freeway at Huludao Toll Station in Liaoning Province, China. Based on the configuration and segmentation of the freeway near a toll station, a six-step guidance strategy is designed to adapt to the separate organization mode. Five conventional traffic scenarios are designed in the Vissim platform for comparative analysis between different guidance strategies. To investigate the vehicle-to-infrastructure (V2I) environment, a microscopic testbed is established with cooperative car-following and lane-changing models using the MATLAB platform. The numerical simulation results show that the guidance strategy significantly improves efficiency and safety, and also reduces emissions and fuel consumption. Meanwhile, pre-guidance before toll channels outperforms the scenario only applied with guidance measures after toll plaza. Compared to conventional conditions, the assessment of pollutant emissions and fuel consumption also embodies the superiority of the other five scenarios, especially in the sections of toll plaza and channels with the lowest efficiency and safety level. Generally, all indexes indicate that the cooperative V2I technology is the best alternative for multilane separate freeways

Sparse Pedestrian Character Learning for Trajectory Prediction

Pedestrian trajectory prediction in a first-person view has recently attracted much attention due to its importance in autonomous driving. Recent work utilizes pedestrian character information, \textit{i.e.}, action and appearance, to improve the learned trajectory embedding and achieves state-of-the-art performance. However, it neglects the invalid and negative pedestrian character information, which is harmful to trajectory representation and thus leads to performance degradation. To address this issue, we present a two-stream sparse-character-based network~(TSNet) for pedestrian trajectory prediction. Specifically, TSNet learns the negative-removed characters in the sparse character representation stream to improve the trajectory embedding obtained in the trajectory representation stream. Moreover, to model the negative-removed characters, we propose a novel sparse character graph, including the sparse category and sparse temporal character graphs, to learn the different effects of various characters in category and temporal dimensions, respectively. Extensive experiments on two first-person view datasets, PIE and JAAD, show that our method outperforms existing state-of-the-art methods. In addition, ablation studies demonstrate different effects of various characters and prove that TSNet outperforms approaches without eliminating negative characters

Robust Navigation with Cross-Modal Fusion and Knowledge Transfer

Recently, learning-based approaches show promising results in navigation tasks. However, the poor generalization capability and the simulation-reality gap prevent a wide range of applications. We consider the problem of improving the generalization of mobile robots and achieving sim-to-real transfer for navigation skills. To that end, we propose a cross-modal fusion method and a knowledge transfer framework for better generalization. This is realized by a teacher-student distillation architecture. The teacher learns a discriminative representation and the near-perfect policy in an ideal environment. By imitating the behavior and representation of the teacher, the student is able to align the features from noisy multi-modal input and reduce the influence of variations on navigation policy. We evaluate our method in simulated and real-world environments. Experiments show that our method outperforms the baselines by a large margin and achieves robust navigation performance with varying working conditions.Comment: Accepted by ICRA 202

The Effect of Nonlinear Charging Function and Line Change Constraints on Electric Bus Scheduling

The recharging plans are a key component of the electric bus schedule. Since the real-world charging function of electric vehicles follows a nonlinear relationship with the charging duration, it is challenging to accurately estimate the charging time. To provide a feasible bus schedule given the nonlinear charging function, this paper proposes a mixed integer programming model with a piecewise linear charging approximation and multi-depot and multi-vehicle type scheduling. The objective of the model is to minimise the total cost of the schedule, which includes the vehicle purchasing cost and operation cost. From a practical point of view, the number of line changes of each bus is also taken as one of the constraints in the optimisation. An improved heuristic algorithm is then proposed to find high-quality solutions of the problem with an efficient computation. Finally, a real-world dataset is used for the case study. The results of using different charging functions indicate a large deviation between the linear charging function and the piecewise linear approximation, which can effectively avoid the infeasible bus schedules. Moreover, the experiments show that the proposed line change constraints can be an effective control method for transit operators

Research on Fine Scheduling and Assembly Planning of Modular Integrated Building: A Case Study of the Baguang International Hotel Project

There exist various challenges in constructing a large in-city project, such as narrow construction sites, limited surrounding roads, heavy construction periods and tasks, various types of vehicles, and affected cargo transport. Considering construction needs, transportation characteristics, and site conditions, this paper puts forward the overall planning for modular integrated construction (MiC) transportation and on-site assembly. Meanwhile, the traffic organization and transportation scheduling method are designed for smart construction sites and different engineering materials are coordinated in the space-time dimension during the overall period from construction delivery. Meanwhile, an integer programming model is developed to solve the truck scheduling matching problem between the supply side and the construction side. The weighted loss time of the truck is set as the optimization objective function, and time, space, and material type are the constraints. For this model, this paper proposes an operations scheduling solution method by combining operations research and actual field construction scheduling experience. The traditional empirical scheduling method and the proposed operations research scheduling model are compared through a case study of actual engineering scheduling data. The experimental results show that the operations research scheduling model is better than the traditional empirical scheduling method at different traffic levels. In addition, the implementation of the scheme is guaranteed through measures such as pre-data analysis, management framework, and information technology equipment. The planning and scheduling cover the whole process of MiC module transportation and on-site assembly, which have practical guiding significance for the project and ensure the timely success and acceptance of the project

Large-Scale Growth of Tubular Aragonite Whiskers through a MgCl2-Assisted Hydrothermal Process

In this paper, we have developed a facile MgCl2-assissted hydrothermal synthesis route to grow tubular aragonite whiskers on a large scale. The products have been characterized by powder X-ray diffraction (XRD), optical microscopy, and scanning electronic microscopy (SEM). The results show the as-grown product is pure tubular aragonite crystalline whiskers with a diameter of 5–10 mm and a length of 100–200 mm, respectively. The concentration of Mg2+ plays an important role in determining the quality and purity of the products. Furthermore, the method can be extended to fabricate CaSO4 fibers. The high quality of the product and the mild conditions used mean that the present route has good prospects for the growth of inorganic crystalline whiskers

Dual heuristic dynamic programming based event-triggered control for nonlinear continuous-time systems

A novel event-triggered approach for a class of nonlinear continuous-time system is proposed in this paper to reduce the computation cost of the dual heuristic dynamic programming (DHP) algorithm. Two neural networks are included in our design. A critic network is used to estimate the partial derivatives of the cost function with respect to its inputs, and an action network is used to approximate the optimal control law. Instead of periodical sampling in the traditional DHP approach, under the event-triggered mechanism, both of the neural networks are only updated at the jump instants, and kept constant during the inter-event time. With the designed trigger threshold, the proposed DHP-based event-triggered approach can save computation time significantly while obtaining competitive control performance when comparing with those of the traditional DHP approach. Two simulation tests are presented to verify the theoretical results

Adaptive event-triggered control based on heuristic dynamic programming for nonlinear discrete-time systems

This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included