Road Constraints Research Articles

A reliable optimal electric Vehicle charging stations allocation

This paper presents a new and effective approach for optimally allocating Electric Vehicle (EV) stations in a distribution network by integrating electrical and road constraints. The proposed methodology, a two-stage optimization technology, addresses the challenges posed by the increasing energy demand caused by the adoption of EVs. In the first stage, the stochastic demand load of EVs is simulated considering the probability and hierarchic clustering of EV load based on the travelling distance of users. This stage focuses on emulating the uncertainty of EV user travel patterns, time of arrival and departure, and their impact on the EV load profile. In the second stage, a multi-objective problem is formulated to minimize power losses, voltage deviation, and enhance system reliability. To achieve this, charging station zones are determined by clustering the transportation network into groups, and statistical simulations of EV user behaviour are conducted numerous times based on probability. To obtain the optimal solution for the investigated system, which consists of a 25-node transportation network interconnected with the IEEE 69 bus distribution network, the Galaxy Gravity Optimization technique is applied. The results obtained from this study illustrate how the placement of charging stations influences reliability indices, power loss, and voltage deviation.

RC-SLAM: Road Constrained Stereo Visual SLAM System Based on Graph Optimization.

Intelligent vehicles are constrained by road, resulting in a disparity between the assumed six degrees of freedom (DoF) motion within the Visual Simultaneous Localization and Mapping (SLAM) system and the approximate planar motion of vehicles in local areas, inevitably causing additional pose estimation errors. To address this problem, a stereo Visual SLAM system with road constraints based on graph optimization is proposed, called RC-SLAM. Addressing the challenge of representing roads parametrically, a novel method is proposed to approximate local roads as discrete planes and extract parameters of local road planes (LRPs) using homography. Unlike conventional methods, constraints between the vehicle and LRPs are established, effectively mitigating errors arising from assumed six DoF motion in the system. Furthermore, to avoid the impact of depth uncertainty in road features, epipolar constraints are employed to estimate rotation by minimizing the distance between road feature points and epipolar lines, robust rotation estimation is achieved despite depth uncertainties. Notably, a distinctive nonlinear optimization model based on graph optimization is presented, jointly optimizing the poses of vehicle trajectories, LPRs, and map points. The experiments on two datasets demonstrate that the proposed system achieved more accurate estimations of vehicle trajectories by introducing constraints between the vehicle and LRPs. The experiments on a real-world dataset further validate the effectiveness of the proposed system.

General Optimal Trajectory Planning: Enabling Autonomous Vehicles with the Principle of Least Action

This study presents a general optimal trajectory planning (GOTP) framework for autonomous vehicles (AVs) that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently. Firstly, we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline. Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve. Considering the road constraints and vehicle dynamics, limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system. Furthermore, in selecting the optimal trajectory, we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’ behavior and summarizing their manipulation characteristics of “seeking benefits and avoiding losses.” Finally, by integrating the idea of receding-horizon optimization, the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility, optimality, and adaptability. Extensive simulations and experiments are performed, and the results demonstrate the framework’s feasibility and effectiveness, which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants. Moreover, we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’ manipulation.

A Novel Ant Colony-inspired Coverage Path Planning for Internet of Drones

Coverage Path Planning (CPP) is a fundamental problem widely used in various applications such as monitoring, patrol, mapping, and sweeping. Several types of robots can perform CPP, which has become very popular with the use of multiple drones. The Internet of Drones (IoD) is an emerging technology that aims to facilitate a multi-drone environment for various applications. In this context, coordination and organization of drones are crucial to avoid collisions and congestion in the airspace. One way to achieve this is by using well-defined airways. CPP tasks are often performed using multiple drones to cover a given area, which can be integrated into the IoD system. However, existing CPP solutions for drones do not fully consider collaboration capabilities between them, specific energy constraints, and the dynamic IoD scenario. To address these challenges, this work proposes an Ant Colony Optimization Algorithm for CPP in IoD (AntIoD), considering energy consumption. This scenario is particularly challenging due to the unique characteristics of being a mix of CPP for terrestrial vehicles with road constraints and CPP for drone networks. We compared our approach, AntIoD, with three recent solutions from the literature. The results reveal that AntIoD outperformed them in all simulated scenarios by at least 10% in terms of energy efficiency when compared to the best scenarios of the other algorithms.

Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics

The complex backgrounds of satellite videos and serious interference from noise and pseudo-motion targets make it difficult to detect and track moving vehicles. Recently, researchers have proposed road-based constraints to remove background interference and achieve highly accurate detection and tracking. However, existing methods for constructing road constraints suffer from poor stability, low arithmetic performance, leakage, and error detection. In response, this study proposes a method for detecting and tracking moving vehicles in satellite videos based on the constraints from spatiotemporal characteristics (DTSTC), fusing road masks from the spatial domain with motion heat maps from the temporal domain. The detection precision is enhanced by increasing the contrast in the constrained area to accurately detect moving vehicles. Vehicle tracking is achieved by completing an inter-frame vehicle association using position and historical movement information. The method was tested at various stages, and the results show that the proposed method outperformed the traditional method in constructing constraints, correct detection rate, false detection rate, and missed detection rate. The tracking phase performed well in identity retention capability and tracking accuracy. Therefore, DTSTC is robust for detecting moving vehicles in satellite videos.

VRR-Net: Learning Vehicle–Road Relationships for Vehicle Trajectory Prediction on Highways

Vehicle trajectory prediction is an important decision-making and planning basis for autonomous driving systems that enables them to drive safely and efficiently. To accurately predict vehicle trajectories, the complex representations and dynamic interactions among the elements in a traffic scene are abstracted and modelled. This paper presents vehicle–road relationships net, a deep learning network that extracts features from vehicle–road relationships and models the effects of traffic environments on vehicles. The introduction of geographic highway information and the calculation of spatiotemporal distances with a reference not only unify heterogeneous vehicle–road relationship representations into a time series vector but also reduce the requirement for sensing transient changes in the surrounding area. A hierarchical long short-term memory network extracts environmental features from two perspectives—social interactions and road constraints—and predicts the future trajectories of vehicles by their manoeuvre categories. Accordingly, vehicle–road relationships net fully exploits the contributions of historical trajectories and integrates the effects of road constraints to achieve performance that is comparable to or better than that of state-of-the-art methods on the next-generation simulation dataset.

Gaussian mixture probability hypothesis density filter with dynamic probabilities: Application to road traffic surveillance

With the development of autonomous driving system, accurate tracking of the number of vehicles and their states is critical to ensure traffic safety. Since the numbers and states of the vehicles are unknown and time-varying, the road traffic surveillance can be considered as a multiple-target tracking (MTT) problem. The Gaussian mixture probability hypothesis density (GM-PHD) filter is a popular approach in solving MTT problem and has been successfully applied to a range of applications including ground vehicle tracking. However, due to the road constraint, the maneuverability of a ground vehicle is limited to the road lane. Thus, the target detection probability, survival probability, and birth rate, which are assumed to be constant in the original GM-PHD filter, vary according to the surveillance area as well as its own state. To address this, we propose an algorithm that dynamically updates the probability of detection and survival, and birth rate simultaneously in a mathematically rigorous way. This is done by integrating the semantic information inferred from the environment with the state-dependent GM-PHD filter. The performance and effectiveness of the proposed algorithm are demonstrated via illustrative road traffic scenarios.

Obstacle-Avoidance Path-Planning Algorithm for Autonomous Vehicles Based on B-Spline Algorithm

To solve the problem of the real-time path-planning of autonomous vehicles for obstacle avoidance on structured roads, a path-planning approach based on the B-spline algorithm is proposed in this paper. Firstly, the mechanism of driver path planning is analyzed, and a dynamic risk-identification model based on the support vector machine is proposed. It combines the driver’s risk perception characteristics and a risk model. Then, the B-spline algorithm model is improved based on the risk-identification model. Furthermore, road features, road constraints and dynamic constraints are considered to further optimize the planning algorithm. To verify the path-planning approach proposed in this paper, a co-simulation experiment based on CarSim/Simulink is conducted. Results show that the improved algorithm is effective in static and dynamic obstacles avoidance. The algorithm can generate collision-free obstacle avoidance paths, and the paths meet the real-time requirements and dynamic constraints of obstacle avoidance scenarios. In addition, the proposed algorithm optimizes the path according to the driver’s operating characteristics, which can further improve the safety and comfort of autonomous vehicles.

An Application-Oriented Method Based on Cooperative Map Matching for Improving Vehicular Positioning Accuracy

Accurate vehicular positioning is important for intelligent and connected vehicles (ICVs). However, in urban canyons, vehicles that rely solely on global navigation satellite system (GNSS) are susceptible to factors such as signal blocking and multi-path, reducing the positioning performance. In this paper, an application-oriented cooperative map matching (CMM) method is proposed, and a low-cost Global Positioning System (GPS)/BeiDou navigation satellite system (BDS) integrated positioning system is designed. The road constraints of a real traffic environment, which simplifies the computational complexity and facilitates practical applications, are modeled. The positioning system is designed to collect and store the positioning data for experimental analysis. Static and dynamic experiments are conducted to verify the effectiveness of the CMM method. From the experimental results, the mean absolute error (MAE) and root mean square error (RMSE) of the positioning with CMM correction in the static experiment are reduced by 9.0% and 4.9%, respectively. In the dynamic experiment, compared with the original positioning error, the MAE is reduced by 44.2% while the RMSE is reduced by 24.3%. The results show that the proposed method can improve vehicular positioning accuracy effectively in both static and dynamic environments.

Vehicle Motion Planning With Joint Cartesian-Frenét MPC

The Frenét frame is commonly used in motion planning for its superiority of reshaping nonconvex curving boundaries and decoupling lateral and longitudinal behaviors. Nevertheless, such frame is not friendly for describing vehicle-related characteristics such as body shape and classical dynamic models. By contrast, the Cartesian frame facilitates straightforward modeling of vehicle dynamics, but confronts nonconvex constraints on curving lanes. For purpose of combining the complementary superiority of both frames, this letter proposes a joint Cartesian-Frenét MPC planning method based on a locally linear intermediary connection which is updated along with solving iterations. The augmented Cartesian-Frenét state space is constrained by a Cartesian vehicle control model that stands for tracking feasibility, a set of Frenét road constraints that account for driving safety, and a Cartesian-Frenét transformation that connects both frames. The final optimization is solved iteratively, where each iteration formulates into a quadratic programming problem with help of the apriori trajectory solved by the previous iteration. A comparative simulation and a 5-month-long real-vehicle test prove that the proposed method can produce a feasible and smooth trajectory within a safe area efficiently in complex real-world scenarios.

Real-Time Metaheuristic Algorithm for Dynamic Fuzzification, De-Fuzzification and Fuzzy Reasoning Processes

This paper presents a systematic approach to designing a dynamic metaheuristic fuzzy logic controller (FLC) to control a piece of non-linear plant. The developed controller is a multiple-input–multiple-output (MIMO) system. However, with the proposed control mechanism is possible to adapt it to single-input–single-output (SISO) systems as well. During real-time operation, the dynamic behavior of the proposed fuzzy controller is influenced by a metaheuristic particle swarm optimization (PSO) mechanism. Nevertheless, to analyze the performance of the developed dynamic metaheuristic FLC as a piece of non-linear plant, a 1 kW four-wheel independent-drive electric rover is controlled under different road constraints. The test results show that the proposed dynamic metaheuristic FLC maintains the wheel slip ratio of all four wheels to less than 0.35 and a top recorded translational speed of 90 km/h is maintained for a fixed orientation.

A Vehicle Trajectory Privacy Preservation Method Based on Caching and Dummy Locations in the Internet of Vehicles.

In the internet of vehicles (IoVs), vehicle users should provide location information continuously when they want to acquire continuous location-based services (LBS), which may disclose the vehicle trajectory privacy. To solve the vehicle trajectory privacy leakage problem in the continuous LBS, we propose a vehicle trajectory privacy preservation method based on caching and dummy locations, abbreviated as TPPCD, in IoVs. In the proposed method, when a vehicle user wants to acquire a continuous LBS, the dummy locations-based location privacy preservation method under road constraint is used. Moreover, the cache is deployed at the roadside unit (RSU) to reduce the information interaction between vehicle users covered by the RSU and the LBS server. Two cache update mechanisms, the active cache update mechanism based on data popularity and the passive cache update mechanism based on dummy locations, are designed to protect location privacy and improve the cache hit rate. The performance analysis and simulation results show that the proposed vehicle trajectory privacy preservation method can resist the long-term statistical attack (LSA) and location correlation attack (LCA) from inferring the vehicle trajectory at the LBS server and protect vehicle trajectory privacy effectively. In addition, the proposed cache update mechanisms achieve a high cache hit rate.

Development of autonomous vehicle lateral control using time-varying asymmetric barrier Lyapunov function via ADRC approach

In a surface vehicle ride, the passenger’s comfort is desirable, ensuring safety under various tight constraints. To consider this requirement, this article has proposed a lateral control for autonomous vehicle considering the lateral offset error constraints. A reduced-order dynamics of the vehicle has been considered for improving and easy implementation of backstepping. A nonlinear control has been developed by utilizing the Time-varying Asymmetric Barrier Lyapunov Function via the Active Disturbance Rejection Control (ADRC) approach. The Extended State Observer, a component of applied ADRC, estimates all unknown system states as well as the unknown disturbance. The desired trajectories (smooth and non-smooth) based on the yaw rate profile have been synthesized to test the performance of the vehicle. Simulation results are presented to show the effectiveness of the proposed closed-loop control strategy. The vehicle performance has been compared using the proposed method, the conventional Quadratic Lyapunov Function and Symmetric Barrier Lyapunov Function based control. The proposed control provides a smoother control signal, small tracking error and robustness in presence of hard road constraints for smooth/non-smooth vehicle motion.

Online on-Road Motion Planning Based on Hybrid Potential Field Model for Car-Like Robot.

The application of Middle-sized Car-like Robots (MCRs) in indoor and outdoor road scenarios is becoming broader and broader. To achieve the goal of stable and efficient movement of the MCRs on the road, a motion planning algorithm based on the Hybrid Potential Field Model (HPFM) is proposed in this paper. Firstly, the artificial potential field model improved with the eye model is used to generate a safe and smooth initial path that meets the road constraints. Then, the path constraints such as curvatures and obstacle avoidance are converted into an unconstrained weighted objective function. The efficient least-squares & quasi-Newton fusion algorithm is used to optimize the initial path to obtain a smooth path curve suitable for the MCR. Finally, the speed constraints are converted into a weighted objective function based on the path curve to get the best speed profile. Numerical simulation and practical prototype experiments are carried out on different road scenes to verify the performance of the proposed algorithm. The results show that re-planned trajectories can satisfy the path constraints and speed constraints. The real-time re-planning period is 184 ms, which demonstrates the proposed approach’s effectiveness and feasibility.

Motion planning for efficient and safe module transportation in modular integrated construction

AbstractModular integrated construction (MiC) is the most advanced construction method that involves off‐site manufacturing, factory‐to‐site transportation, and on‐site assembly of free‐standing integrated modules. Despite the growing interest in the manufacturability of MiC, little research is available on the passing ability issues where efficient and safe transportation of modules is critical to the project's success. This paper proposes (1) a novel computational method for formulating and solving the module's horizontal passing ability with road constraints as a motion planning problem and (2) a new index to assess the path performance in critical scenarios. The newly developed Truck‐Parallelized Hybrid A Star (TP‐Hybrid A*) has novelties in collision checking, cost function formulation, and parallel computing on different vehicle dimensions. The effectiveness of the developed algorithm was experimentally verified to be superior to the rapid random tree in computation time and path performance and further demonstrated by applying it to a real‐life MiC project. The new index can serve as a useful tool for decision‐making in module dimension design and transportation planning of MiC projects when tackling the module's horizontal passing ability issues.

Tracking a Ground Target Utilizing Doppler-Only Measurements of a Single Passive Sonar Sensor Assisted by Straight Road Constraints

This paper handles tracking a ground target using a single passive sonar sensor measuring doppler shift of the target. We consider a scenario where the frequency of the emitting tone is not known in advance. We further consider a ground target which is constrained to move along a known straight road with a known course. It is assumed that the single sonar sensor is located close to the road, whose shape is known in advance. This article introduces tracking the ground target assisted by road constraints. We address a non-recursive estimation algorithm using a batch filter initialized with samples that are distributed considering the road constraints. As far as we know, our paper is unique in using a single sonar sensor for target localization with doppler-only measurements with unknown emitting frequency. Moreover, our article is unique in target tracking based on doppler-only measurements of a single sonar sensor, assisted by road constraints. The effectiveness of the proposed tracking filter is verified under MATLAB simulations.

Interrelationships Among Factors Influencing Multimodal Transportation Efficiency of Agricultural Products in Thailand

Thailand is the world’s leading agricultural products exporter. The transportation of agricultural products in Thailand relies mainly on roads because of its extensive network and convenience. However, with the limited capacity, traffic congestion, air pollution, and high fuel prices, this transportation mode becomes less effective. The government, then, attempts to develop and promote multimodal transportation, especially rail and water modes, to reduce the logistics costs and increase the transportation network efficiency. This study extracts five key factors influencing multimodal transportation efficiency of agricultural products in Thailand, namely Multimodal Transportation Infrastructure, Operation, Multimodal Service Provider, Market, and Road Constraints factors, as well as examines their interrelationships using the structural equation modeling method. The results show significant direct and indirect relationships among the five key multimodal transportation efficiency factors. It is found that with better multimodal transportation-related operations, multimodal transportation market is improved through less use of road transportation and more multimodal service providers in the market. Moreover, policymakers should focus on improving multimodal transportation-related infrastructures, such as container yards, access roads to train station and port, and transshipment equipment, in order to increase multimodal transportation efficiency and encourage the use of multimodal transportation. Engineering managers can use the study results in decision-making of transportation mode selection. Policymakers in government sector may apply the study results to establish effective policies to achieve a sustainable transportation system, which, in turn, gives rises to national industrial competitiveness on a global scale.

Online Local Path Planning on the Campus Environment for Autonomous Driving Considering Road Constraints and Multiple Obstacles

In this paper, an urban-based path planning algorithm that considered multiple obstacles and road constraints in a university campus environment with an autonomous micro electric vehicle (micro-EV) is studied. Typical path planning algorithms, such as A*, particle swarm optimization (PSO), and rapidly exploring random tree* (RRT*), take a single arrival point, resulting in a lane departure situation on the high curved roads. Further, these could not consider urban-constraints to set collision-free obstacles. These problems cause dangerous obstacle collisions. Additionally, for drive stability, real-time operation should be guaranteed. Therefore, an urban-based online path planning algorithm, which is robust in terms of a curved-path with multiple obstacles, is proposed. The algorithm is constructed using two methods, A* and an artificial potential field (APF). To validate and evaluate the performance in a campus environment, autonomous driving systems, such as vehicle localization, object recognition, vehicle control, are implemented in the micro-EV. Moreover, to confirm the algorithm stability in the complex campus environment, hazard scenarios that complex obstacles can cause are constructed. These are implemented in the form of a delivery service using an autonomous driving simulator, which mimics the Chungbuk National University (CBNU) campus.

Regenerative Braking Control Method Based on Predictive Optimization for Four-Wheel Drive Pure Electric Vehicle

Regenerative braking is the key to achieve efficient use of energy and extend the driving range in pure electric vehicles. This study proposes a new predictive control method integrating adaptive cubic exponential prediction and dynamic programming to address the problem of efficient energy recovery during the regular braking process of four-wheel pure electric vehicles. The method considers the dynamic characteristics of an electro-hydraulic combined braking system. The adaptive cubic exponential prediction is adopted to predict the vehicle velocity and braking intensity. The dynamic programming is employed to optimize the motor braking torques and wheel cylinder pressures under the condition of braking regulations, road constraints, and vehicle constraints. To verify the effectiveness of the new predictive control method, the ideal and multi-stage braking force distribution methods are employed for comparison. The results confirm that, under gradual braking conditions, the energy recovery efficiency achieved via the proposed method is improved by 1.55% and 6.40% considering the ideal and multi-stage braking force distribution methods, respectively.

Modelling geographical accessibility to support disaster response and rehabilitation of a healthcare system: an impact analysis of Cyclones Idai and Kenneth in Mozambique

ObjectivesModelling and assessing the loss of geographical accessibility is key to support disaster response and rehabilitation of the healthcare system. The aim of this study was therefore to estimate postdisaster...