Road Constraints Research Articles

Road Constrained Labeled Multi Bernoulli Filter based on PDF Truncation for Multi-Target Tracking

In this paper, road constrained filtering is applied to labeled multi Bernoulli (LMB) filter using PDF truncation in multi-road environment. In target tracking systems with road map information, road constraints can effectively improve the estimation performance. To apply multiple road constraints information to the tracking filter, all constraints should not be applied simultaneously and only one should be selected for each estimated trajectory. Then, probability density function (PDF) truncation is conducted which is a constrained filtering technique for inequality constraints. To verify the constrained filtering technique to LMB filter, simulations for multi-target tracking in cluttered environments are carried out. The simulation result shows that the proposed method bounded estimated trajectories on the road effectively and reduced OSPA error.

Key Factors Influencing Multimodal Transportation of Natural Block Rubber in Thailand

Thailand is the world’s largest natural rubbers exporter. The major type of natural rubbers exported is block rubber. Currently, the transportation of natural rubbers in Thailand depends mainly on truck mode because of the convenience and extensive network of roads. However, Thailand confronts road transportation limitations including maximum acceptable weight, traffic congestion and high fuel prices. Dealing with these problems, the multimodal connection from roads to container yards (CY) and ports is expected to decrease logistics cost, and increase various opportunities to trade with neighbouring countries. This paper, thus, aims at examining key factors influencing the use of multimodal transportation utilizing the exploratory factor analysis (EFA). The principal components analysis (PCA) method, together with the eigenvalue over 1, factor loading of 0.40, and varimax rotation method, extracted five key factors with a total of 20 associated attributes, which are the Multimodal Operation, Service Operation, Multimodal Service Provider, Market Consideration, and Road Constraints factors. The result also pinpoints the capacity of container yard (CY) or port, accessibility to container yard (CY) or port, documentation process, logistics cost, adequacy of a multimode service provider, and law enforcement on truck driving hours, as crucial attributes in planning for multimodal transportation. This paper can be used as a guideline for a feasibility study of multimodal transportation of natural block rubber in Thailand.

Road-Constrained Geometric Pose Estimation for Ground Vehicles

Pose estimation with state or measurement constraints has been frequent in autonomous vehicle navigation. Aiming at incorporating constraints inherently, this article proposes a dynamic potential field (DPF)-based formulation to represent states, measurements, and constraints on connected Riemannian manifolds. The state equation and the output equation are derived in the DPF forms, which imply the probabilistic inference with states and measurements. Constraints are incorporated by projecting points toward a constraint subset in the state space and the measurement space, where the DPF representing constraints is created. An information fusion scheme has been designed for DPFs, which are obtained from multisensor measurements and constraints. KITTI and self-collected sequences have been used in experiments, during which it is observed that the rotational drift is corrected and translational errors are reduced, thanks to the fusion of stereo visual odometry (SVO), heading measurements, and road maps. Note to Practitioners —Pose estimation aims to obtain ground vehicle’s position and heading in a certain coordinate system. The multisensor fusion has been a popular solution in reducing estimation errors, and constraints are helpful in improving the pose estimation performance by limiting possible solutions. This article proposes a framework to model multisensor pose estimation problems with constraints. The approximated estimation scheme inspired by the Monte Carlo method has been provided such that the framework can be applied to various systems without analytic modeling. Experiments in KITTI and self-collected sequences have been conducted to demonstrate the superiority of the proposed approach by fusing stereo visual odometry (SVO) and heading measurements under road constraints. The proposed approach can be ameliorated by adding an absolute reference in translation to correct the inherent translational drift in VO.

Route‐Aware Edge Bundling for Visualizing Origin‐Destination Trails in Urban Traffic

AbstractOrigin‐destination (OD) trails describe movements across space. Typical visualizations thereof use either straight lines or plot the actual trajectories. To reduce clutter inherent to visualizing large OD datasets, bundling methods can be used. Yet, bundling OD trails in urban traffic data remains challenging. Two specific reasons hereof are the constraints implied by the underlying road network and the difficulty of finding good bundling settings. To cope with these issues, we propose a new approach called Route Aware Edge Bundling (RAEB). To handle road constraints, we first generate a hierarchical model of the road‐and‐trajectory data. Next, we derive optimal bundling parameters, including kernel size and number of iterations, for a user‐selected level of detail of this model, thereby allowing users to explicitly trade off simplification vs accuracy. We demonstrate the added value of RAEB compared to state‐of‐the‐art trail bundling methods on both synthetic and real‐world traffic data for tasks that include the preservation of road network topology and the support of multiscale exploration.

Scalable Computing Architecture for Time-Dependent Transportation Optimization Problems Based on High-Performance Computing Techniques

Traditional formulations for transportation optimization problems mostly build complicating attributes into constraints while keeping the succinctness of objective functions. A popular solution is the Lagrangian decomposition by relaxing complicating constraints and then solving iteratively. Although this approach is effective for many problems, it generates intractability in other problems. To address this issue, this paper presents an alternative formulation for transportation optimization problems in which the complicating attributes of target problems are partially or entirely built into the objective function instead of into the constraints. Many mathematical complicating constraints in transportation problems can be efficiently modeled in dynamic network loading (DNL) models based on the demand–supply equilibrium, such as the various road or vehicle capacity constraints or “IF–THEN” type constraints. After “pre-building” complicating constraints into the objective functions, the objective function can be approximated well with customized high-fidelity DNL models. Three types of computing benefits can be achieved in the alternative formulation: ( a) the original problem will be kept the same; ( b) computing complexity of the new formulation may be significantly reduced because of the disappearance of hard constraints; ( c) efficiency loss on the objective function side can be mitigated via multiple high-performance computing techniques. Under this new framework, high-fidelity and problem-specific DNL models will be critical to maintain the attributes of original problems. Therefore, the authors’ recent efforts in enhancing the DNL’s fidelity and computing efficiency are also described in the second part of this paper. Finally, a demonstration case study is conducted to validate the new approach.

Research on distribution route with time window and on-board constraint based on tabu search algorithm

In order to solve the problem of the optimization of delivery and pickup cargoes’ routes with time windows under the constraints of load and road, the method and algorithm were designed and researched based on these factors. Firstly, the model was built with the constraints of truck number, load quality, load capacity, maximum distance, and customer service time in order to minimize the transportation cost and time cost. Then, the Floyd Algorithm was used to determine the moving path and the shortest distance between each point with the road constraint. 0-1 integer programming method was used to determine the number and type of vehicles. Tabu search algorithm was used to determine the order of delivery and pickup points and the distribution route based on the influence of job requirements of pickup cargoes, the time cost, and the transportation cost. Finally, the case was given to verify the effectiveness of the proposed method. This method can be used to improve the efficiency of delivery and pickup cargoes and the level of distribution service.

The Impact of Road Configuration in V2V-Based Cooperative Localization: Mathematical Analysis and Real-World Evaluation

Cooperative map matching (CMM) uses the global navigation satellite system (GNSS) position information of a group of vehicles to improve the standalone localization accuracy. While increasing accuracy is expected by increasing the number of participating vehicles, fundamental questions on how the vehicle membership within CMM affects the performance of the CMM results need to be addressed to provide guidelines for design and optimization of the vehicle network. This paper presents a theoretical study that establishes a framework for quantitative evaluation of the impact of the road constraints on the CMM accuracy. More specifically, a closed-form expression of the CMM error in terms of the road constraints and GNSS error is derived based on a simple CMM rule. The asymptotic decay of the CMM error as the number of vehicles increases is established and justified through numerical simulations. Moreover, it is proved that the CMM error can be minimized if the directions of the roads on which the connected vehicles travel obey a uniform distribution. Finally, the localization accuracy of CMM is evaluated based on the real traffic data. The contributions of this paper include establishing a theoretical foundation for CMM as well as providing insight and motivation for applications of CMM.

Tracking Ground Targets with a Road Constraint Using a GMPHD Filter.

The Gaussian mixture probability hypothesis density (GMPHD) filter is applied to the problem of tracking ground moving targets in clutter due to its excellent multitarget tracking performance, such as avoiding measurement-to-track association, and its easy implementation. For the existing GMPHD-based ground target tracking algorithm (the GMPHD filter incorporating map information using a coordinate transforming method, CT-GMPHD), the predicted probability density of its target state is given in road coordinates, while its target state update needs to be performed in Cartesian ground coordinates. Although the algorithm can improve the filtering performance to a certain extent, the coordinate transformation process increases the complexity of the algorithm and reduces its computational efficiency. To address this issue, this paper proposes two non-coordinate transformation roadmap fusion algorithms: directional process noise fusion (DNP-GMPHD) and state constraint fusion (SC-GMPHD). The simulation results show that, compared with the existing algorithms, the two proposed roadmap fusion algorithms are more accurate and efficient for target estimation performance on straight and curved roads in a cluttered environment. The proposed methods are additionally applied using a cardinalized PHD (CPHD) filter and a labeled multi-Bernoulli (LMB) filter. It is found that the PHD filter performs less well than the CPHD and LMB filters, but that it is also computationally cheaper.

Quadcopter for Medical Services

Lack of immediate medical attention post trauma is the major factor of the increased death rates in road accidents. In order to provide timely medical attention, a system is to be designed which is a means that does not have the road constraints of an emergency medical system. This is the Air Ambulance- Quadcopter. Attempting to bring about faster accessibility of medical services to the patient especially in rural areas is the motivating factor. The system will help in implementing this idea of faster site accessibility to a large extent, by providing timely first aid and serving the society through means of a Quadcopter. To design such an equipment, an elaborate survey has been made on the topics of Wi-Fi, Sensor. This paper is an outcome of the survey done which has enriched us with an in-depth knowledge of the various components and techniques required to design and build a Quadcopter.

Set-Membership Position Estimation With GNSS Pseudorange Error Mitigation Using Lane-Boundary Measurements

Model-based positioning methods involve nonlinear equations as is the case when using satellite pseudoranges on global navigation satellite systems (GNSSs) and local measurements on road features. As these are nonlinear models, classical estimation methods cannot provide guaranteed position estimation and can converge to local optima, sometimes far away from the global optimum or the true value. Based on interval analysis, set inversion, and constraints propagation on real values provide a framework that guarantees to find the true position with a characterized confidence domain. This paper describes an error bounded set membership algorithm that computes the absolute position of a road vehicle by using raw GNNS pseudoranges, lane boundary measurements, and a 2D road network map as geometric constraints. The algorithm is based on set inversion using interval analysis, and bounds are set on the measurements by taking into account a chosen risk. The GNSS pseudoranges errors are modeled carefully, and road constraints are formalized to provide additional information in the data fusion process. The proposed algorithm, named lane boundary augmented set-membership GNSS positioning (LB-ASGP), provides a novel and inexpensive approach to improve position estimation performance for road vehicles guaranteeing the enclosure of the computed solution with high confidence. Results from simulations and field experiments show that the LB-ASGP significantly reduces GNSS errors in the direction perpendicular to the lane thanks to the lane boundary measurements.

Road Constrained Monocular Visual Localization Using Gaussian-Gaussian Cloud Model

There are two main challenges, drift and scale ambiguity, restricting monocular visual odometry from an extensive application on real autonomous navigation. In this paper, an iterative localization framework is presented to globally localize a mobile vehicle equipped with a single camera and a freely available digital map. Inspired by the concept of cloud, a new Gaussian–Gaussian Cloud model is proposed to give a unified representation of the measurement randomness and scale ambiguity in monocular visual odometry. In this model, a collection of cloud drops is generated. Both the drift and scale ambiguity are considered and represented simultaneously in each cloud drop. To reduce the measurement uncertainties of any drop in Gaussian–Gaussian Cloud, road constraints from the open source map—OpenStreetMap—are utilized. The map is first converted to a template edge map and a shape matching step is then implemented to assign the probability of each cloud drop, indicating to what degree the drop accords with road constraints. A parameter estimation scheme is used to narrow down the scale ambiguity of monocular visual odometry while resampling cloud drops. Evaluations on the KITTI benchmark data set and our self-collected data set have demonstrated the stability and accuracy of the proposed approach.

Shimmy model for electric vehicle with independent suspensions

A 5-degrees-of-freedom shimmy model is established to analyse the dynamic responses of an electric vehicle with independent suspensions. Tyre elasticity is considered by means of Pacejka’s magic formula. Under the nonslip assumption for the leading contact point, tyre–road constraint equations are derived. Numerical simulation is conducted with different structural parameters and initial conditions to observe the shimmy phenomenon. Simulation results indicate that Hopf bifurcation occurs at a certain vehicle forward speed. Moreover, suspension structural parameters, such as caster angle, affect wheel shimmy. The linearized model of the system presents the stability boundaries, which agree with the simulation results. The results of this study not only provide a theoretical reference for shimmy attenuation, but also validate the effectiveness of the provided model, which can be used in further dynamic analysis of vehicle shimmy.

Robust Multirate On-Road Vehicle Localization for Autonomous Highway Driving Vehicles

An on-road vehicle localization scheme is proposed to keep a track of an ego vehicle with respect to the ego/target lane center, using the results of the camera-based lane recognition. A vehicle’s lateral motion model is first developed in terms of lane curve tracking error states, which is assumed to satisfy the clothoid road constraint. The proposed vehicle localization scheme computes vehicle’s state, such that advanced state space control can be applied at a constant fast rate. Utilizing multirate characteristics of each sensor, a multirate decentralized state estimator is developed, which exhibits robust convergence against uncertain slow update period of the camera-based lane recognition. Performance and robustness of the proposed on-road vehicle localization scheme is verified through the computational and experimental tests using camera-based lane recognition.

Paper-based Pneumatic Locomotive Robot with Sticky Actuator

Demands for small-scale and low-cost robots have witnessed a great increase in recent years [1–5]. This paper introduces the design and fabrication of a novel, simple, low-cost and designer-friendly locomotive robot. The materials and tools to build the robot originate from everyday life. The robot is pneumatically powered and manually controlled by simply pumping and vacuuming the syringe repeatedly, which realizes reliable locomotion by folding and opening of the planes. In order to realize this complicated motion, a “3D Sticky Actuator” is developed. The motion and force analysis of actuator are then modelled by the numerical method to develop the relations between design parameters. This suggests a systematic and user interactive way of manufacturing various shapes of the actuator, depending on user-defined road condition (e.g. obstacles and slopes) and other constraints. One key advantage of the paper-based robot is suggested by its high feasibility.

Optimized trajectory planning for Cybernetic Transportation Systems

This paper describes the development of an optimized path planning algorithm for automated vehicles in urban environments. This path planning is developed on the basis of urban environments, where Cybernetic Transportation Systems (CTS) will operate. Our approach is mainly affected by vehicle’s kinematics and physical road constraints. Based on this assumptions, computational time for path planning can be significantly reduced by creating an off-line database that already optimized all the potential trajectories in each curve the CTS can carry out. Therefore, this algorithm generates a database of smooth and continuous curves considering a big set of different intersection scenarios, taking into account the constraints of the infrastructure and the physical limitations of the vehicle. According to the real scenario, the local planner selects from the database the appropriate curves from searching for the ones that fit with the intersections defined on it. The path planning algorithm has been tested in simulation using the previous control architecture. The results obtained show path generation improvements in terms of smoothness and continuity. Finally, the proposed algorithm was compared with previous path planning algorithms for its assessment.

The Optimization Model for Electric System Emergency Facility Storage and Transportation Considering Information Credibility

When dealing with power system emergency caused by natural disaster, information survey is the important foundation of emergency power supply and repair. The information credibility would directly inflect the credibility and timeliness of electric system emergency work. Therefore, this paper starts the study from the point of information credibility and sets minimizing total cost and loss as the objective function, taking time constraint, transportation road constraint, information credibility constraint, and the parameters of different facilities into consideration and building a location and storage optimization model for electric system emergency facility. And a corresponding facility transportation model was built considering the power loss in demand points. The simulation result shows that the models proposed by this paper could satisfy the requirement of information credibility, improve the adaptability to demand change, and cut down total cost and loss.

A Novel Approach for Improved Vehicular Positioning Using Cooperative Map Matching and Dynamic Base Station DGPS Concept

In this paper, a novel approach for improving vehicular positioning is presented. This method is based on the cooperation of the vehicles by communicating their measured information about their position. This method consists of two steps. In the first step, we introduce our cooperative map matching method. This map matching method uses the V2V communication in a vehicular ad hoc network (VANET) to exchange global positioning system (GPS) information between vehicles. Having a precise road map, vehicles can apply the road constraints of other vehicles in their own map matching process and acquire a significant improvement in their positioning. After that, we have proposed the concept of a dynamic base station DGPS (DDGPS), which is used by vehicles in the second step to generate and broadcast the GPS pseudorange corrections that can be used by newly arrived vehicles to improve their positioning. The DDGPS is a decentralized cooperative method that aims to improve the GPS positioning by estimating and compensating the common error in GPS pseudorange measurements. It can be seen as an extension of DGPS where the base stations are not necessarily static with an exact known position. In the DDGPS method, the pseudorange corrections are estimated based on the receiver's belief on its positioning and its uncertainty and then broadcasted to other GPS receivers. The performance of the proposed algorithm has been verified with simulations in several realistic scenarios.

A Mobility Model for Connected Vehicles Induced by the Fish School

We proposed a mobility model of connected vehicles with wireless communications. The proposed mobility model is inspired by the fish school, which takes into account several significant attractive/repulsive potential fields induced by the expected mobility, the tendency to avoid collisions, and the road constraints. With this model, we present a comprehensive approach for designing and analyzing the potential fields acting on connected vehicles. Based on the Lyapunov stability theorem, a theoretical analysis is also provided to show that the cooperative collision avoidance and stable constrained flocking of connected vehicles can be performed by using this model. The numerical experiments prove the effectiveness of the wireless communication on the safety and efficiency of connected vehicles.

Compensatory neural fuzzy control for two wheels electric vehicle drive

This paper presents a novel speed control design of electric vehicles (EV) to improve behaviour and stability under different road constraints conditions. The proposed control is intended to increase the efficiency of a circuit using adaptive fuzzy reasoning method with compensatory fuzzy operators. The compensatory neural fuzzy (CNF) networks are made of both control-oriented fuzzy neurons and decision-oriented fuzzy neurons. The CNF networks are not only adaptively adjust fuzzy membership functions but also dynamically optimise the adaptive fuzzy reasoning by using a compensatory learning algorithm. The proposed traction system consists of two induction motors (IMs) that ensure the drive of the two rear wheels. The controller is designed based on a control structure that realises an independent speed control. Simulation results show that the CNF control method reduces the transient oscillations and ensures efficient behaviour in all types of road constraints.

Road-map–assisted standoff tracking of moving ground vehicle using nonlinear model predictive control

This paper presents road-map-assisted standoff tracking of a ground vehicle using nonlinear model predictive control. In model predictive control, since the prediction of target movement plays an important role in tracking performance, this paper focuses on utilizing road-map information to enhance the estimation accuracy. For this, a practical road approximation algorithm is first proposed using constant curvature segments, and then nonlinear road-constrained Kalman filtering is followed. To address nonlinearity from road constraints and provide good estimation performance, both an extended Kalman filter and unscented Kalman filter are implemented along with the state-vector fusion technique for cooperative unmanned aerial vehicles. Lastly, nonlinear model predictive control standoff tracking guidance is given. To verify the feasibility and benefits of the proposed approach, numerical simulations are performed using realistic car trajectory data in city traffic.