Vehicle Mobility Prediction Research Articles

Modeling of Vehicle Mobility in Shallow Water With Data-Driven Hydrodynamics Model

Abstract In this study, a data-driven hydrodynamics model is proposed to enable quick prediction of vehicle mobility in shallow water, considering the effect of tire–soil interaction. To this end, a high-fidelity coupled vehicle–water interaction model using computational fluid dynamics (CFD) and multibody dynamics (MBD) solvers is developed to characterize the hydrodynamic loads exerted on a vehicle operated in shallow water, and it is used to generate training data for the data-driven hydrodynamics model. To account for the history-dependent hydrodynamic behavior, a long short-term memory (LSTM) neural network is introduced to incorporate effects of the historical variation of vehicle motion states as the input to the data-driven model, and it is used to predict hydrodynamic loads online exerted on vehicle components in the MBD mobility simulation. The impacts of hydrodynamic loads on the vehicle mobility capability in shallow water are examined for different water depths and incoming flow speeds using the high-fidelity coupled CFD-MBD model. Furthermore, it is demonstrated that the vehicle–water interaction behavior in scenarios not considered in the training data can be predicted using the proposed LSTM data-driven hydrodynamics model. However, the use of non-LSTM layers, which do not account for the sequential variation of vehicle motion states as the input, leads to an inaccurate prediction. A substantial computational speedup is achieved with the proposed LSTM-MBD vehicle–water interaction model while ensuring accuracy, compared to the computationally expensive high-fidelity coupled CFD-MBD model.

Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph

Predicting vehicle mobility is crucial in domains such as ride-hailing, where the balance between offer and demand is paramount. Since city road networks can be easily represented as graphs, recent works have exploited graph neural networks (GNNs) to produce more accurate predictions on real traffic data. However, a better understanding of the characteristics and limitations of this approach is needed. In this work, we compare several GNN aggregated mobility prediction schemes to a selection of other approaches in a very restricted and controlled simulation scenario. The city graph employed represents roads as directed edges and road intersections as nodes. Individual vehicle mobility is modeled as transitions between nodes in the graph. A time series of aggregated mobility is computed by counting vehicles in each node at any given time. Three main approaches are employed to construct the aggregated mobility predictors. First, the behavior of the moving individuals is assumed to follow a Markov chain (MC) model whose transition matrix is inferred via a least squares estimation procedure; the recurrent application of this MC provides the aggregated mobility prediction values. Second, a multilayer perceptron (MLP) is trained so that—given the node occupation at a given time—it can recursively provide predictions for the next values of the time series. Third, we train a GNN (according to the city graph) with the time series data via a supervised learning formulation that computes—through an embedding construction for each node in the graph—the aggregated mobility predictions. Some mobility patterns are simulated in the city to generate different time series for testing purposes. The proposed schemes are comparatively assessed compared to different baseline prediction procedures. The comparison illustrates several limitations of the GNN approaches in the selected scenario and uncovers future lines of investigation.

AI-Enabled Spatial-Temporal Mobility Awareness Service Migration for Connected Vehicles

In the future 6 G intelligent transportation system, the edge server will bring great convenience to the timely computing service for connected vehicles. To guarantee the quality of service, the time-critical services need to be migrated according to the future location of the vehicle. However, predicting vehicle mobility is challenging due to the time-varying of road traffic and the complex mobility patterns of vehicles. To address this issue, a spatial-temporal awareness proactive service migration strategy is proposed in this paper. First, a spatial-temporal neural network is designed to obtain accurate mobility by using gated recurrent units and graph convolutional layers extracting features from spatial road traffic and multi-time scales driving data. Then a proactive migration method is proposed to guarantee the reliability of services and reduce energy consumption. Considering the reliability of services and the real-time workload of servers, the migration problem is modeled as a multi-objective optimization problem, and the Lyapunov optimization method is utilized to obtain utility-optimal migration decisions. Extensive simulations based on real-world datasets are performed to validate the performance of the proposed method. The results show that the proposed method achieved 6% higher prediction accuracy, 10% lower dropping rate, and 10% lower energy consumption compared to state-of-the-art methods.

Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction

Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy consumption, and traffic efficiency can be significantly improved. An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of the V2X network. Recurrent neural networks and their variants have been reported in recent research to predict vehicle mobility. However, the spatial attribute of vehicle movement behavior has been overlooked, resulting in incomplete information utilization. To bridge this gap, we put forward for the first time a hierarchical trajectory prediction structure using the capsule neural network (CapsNet) with three sequential components. First, the geographic information is transformed into a grid map presentation, describing vehicle mobility distribution spatially and temporally. Second, CapsNet serves as the core model to embed local temporal and global spatial correlation through hierarchical capsules. Finally, extensive experiments conducted on actual taxi mobility data collected in Porto city (Portugal) and Singapore show that the proposed method outperforms the state-of-the-art methods.

Reliability-Based Multivehicle Path Planning Under Uncertainty Using a Bio-Inspired Approach

Abstract Identifying a reliable path in uncertain environments is essential for designing reliable off-road autonomous ground vehicles (AGVs) considering postdesign operations. This article presents a novel bio-inspired approach for model-based multivehicle mission planning under uncertainty for off-road AGVs subjected to mobility reliability constraints in dynamic environments. A physics-based vehicle dynamics simulation model is first employed to predict vehicle mobility (i.e., maximum attainable speed) for any given terrain and soil conditions. Based on physics-based simulations, the vehicle state mobility reliability in operation is then analyzed using an adaptive surrogate modeling method to overcome the computational challenges in mobility reliability analysis by adaptively constructing a surrogate. Subsequently, a bio-inspired approach called Physarum-based algorithm is used in conjunction with a navigation mesh to identify an optimal path satisfying a specific mobility reliability requirement. The developed Physarum-based framework is applied to reliability-based path planning for both a single-vehicle and multiple-vehicle scenarios. A case study is used to demonstrate the efficacy of the proposed methods and algorithms. The results show that the proposed framework can effectively identify optimal paths for both scenarios of single and multiple vehicles. The required computational time is less than the widely used Dijkstra-based method.

An Adaptive Learning-Based Approach for Vehicle Mobility Prediction

This work presents an innovative methodology to predict the future trajectories of vehicles when its current and previous locations are known. We propose an algorithm to adapt the vehicles trajectories’ data based on consecutive GPS locations and to construct a statistical inference module that can be used online for mobility prediction. The inference module is based on a hidden Markov model (HMM), where each trajectory is modeled as a subset of consecutive locations. The prediction stage uses the statistical information inferred so far and is based on the Viterbi algorithm, which identifies the subset of consecutive locations (hidden information) with the maximum likelihood when a prior subset of locations are known (observations). By analyzing the disadvantages of using the Viterbi algorithm (TDVIT) when the number of hidden states increases, we propose an enhanced algorithm (OPTVIT), which decreases the prediction computation time. Offline analysis of vehicle mobility is conducted through the evaluation of a dataset containing real traces of 442 taxis running in the city of Porto, Portugal, during a full year. Experimental results obtained with the dataset show that the prediction process is improved when more information about prior vehicle mobility is available. Moreover, the computation time of the prediction process is significantly improved when OPTVIT is adopted and approximately 90% of prediction performance can be achieved, showing the effectiveness of the proposed method for vehicle trajectory prediction.

A Deep Learning-Based Mobile Crowdsensing Scheme by Predicting Vehicle Mobility

Mobile crowdsensing is an emerging paradigm that selects users to complete sensing tasks. Recently, mobile vehicles are adopted to perform sensing data collection tasks in the urban city due to their ubiquity and mobility. In this article, we study how mobile vehicles can be optimally selected in order to collect maximum data from the urban environment in a future period of tens of minutes. We formulate the recruitment of vehicles as a maximum data limited budget problem. The application scenario is generalized to a realistic online setting where vehicles are continuously moving in real-time and the data center decides to recruit a set of vehicles immediately. A deep learning-based scheme through mobile vehicles (DLMV) is proposed to collect sensing data in the urban environment. We first propose a deep learning-based offline algorithm to predict vehicle mobility in a future time period. Furthermore, we propose a greedy online algorithm to recruit a subset of vehicles with a limited budget for the NP-Complete problem. Extensive experimental evaluations are conducted on the real mobility dataset in Rome. The results have not only verified the efficiency of our proposed solution but also validated that DLMV can improve the quantity of collected sensing data compared with other algorithms.

Collision Avoidance Method Using Vector-Based Mobility Model in TDMA-Based Vehicular Ad Hoc Networks

The rapid development of wireless technology has accelerated the development of Vehicular Ad Hoc Networks (VANETs) to support accident prevention and the safety of a vehicle driver. VANET is a form of Mobile Ad Hoc Network (MANET), and it differs from MANET in that the network topology of a VANET changes dynamically in response to the high mobility of a vehicle and the unstable link quality due to various types of road patterns. Since most access and merging conflicts occur due to vehicle movement patterns and traffic conditions, the collision rate can be reduced if each vehicle can predict the location, movement direction, and resource occupancy information of other vehicles. In this paper, we propose a collision avoidance method based on the vehicle mobility prediction model in TDMA-based VANET. The proposed algorithm allocates time-slots of TDMA to avoid access and merging collisions by predicting the mobility of nearby vehicles using control time-slot occupancy information, vehicle ID, hop information, vehicle movement direction, and longitude and latitude of a vehicle. Simulation results show that the proposed algorithm can reduce access and merging collision rates compared with other legacy algorithms, and the performance gain of the proposed algorithm is enhanced in road environments when traffic density is high and where vehicles have high mobility and change their travel directions frequently.

The interactions between an off-road tire and granular terrain: GPU-based DEM-FEM simulation and experimental validation

Numerical methods have become useful tools for predicting vehicle mobility performance on granular terrain. However, due to the large number of soil particles and complex contact search in tire-granular terrain simulation, time-consuming calculation has become the most critical problem restricting the application of these methods. In this study, an efficient GPU-based DEM-FEM for simulation of the interactions between an off-road tire and granular terrain is implemented to improve computational efficiency. In this method, the main body of calculation is executed on GPU and programmed into an in-house developed code CDFP. The new GPU-based computing framework consists of 14 kernels, including efficient contact calculation between large-scale particles, novel contact calculation between particles and complex tread, contact calculation between particles and boundary wall, internal force calculation of finite elements, and information update. As a result, the efficient contact search and the complex interactions between an off-road tire and granular terrain are accomplished. Based on the self-developed single-wheel test device, an accurate simulation model consistent with the experiment is established. The validity of the proposed method is verified by comparing the simulation results with the experimental results. Finally, the discussion of computational efficiency shows that the proposed GPU-based DEM-FEM can be a powerful tool to simulate the interactions between an off-road tire and granular terrain.

Efficient generation of accurate mobility maps using machine learning algorithms

U.S Army’s mission is to develop, integrate, and sustain the right technology solutions for all manned and unmanned ground vehicles, and mobility is a key requirement for all ground vehicles. Mobility focuses on ground vehicles’ capabilities that enable them to be deployable worldwide, operationally mobile in all environments, and protected from symmetrical and asymmetrical threats. In order for military ground vehicles to operate in any combat zone, the planners require a mobility map that gives the maximum predicted speeds on these off-road terrains. In the past, empirical and semi-empirical techniques (Ahlvin and Haley, 1992; Haley et al., 1979) were used to predict vehicle mobility on off-road terrains such as the NATO Reference Mobility Model (NRMM). Because of its empirical nature, the NRMM method cannot be extrapolated to new vehicle designs containing advanced technologies, nor can it be applied to lightweight robotic vehicles.The mobility map is a function of different parameters such as terrain topology and profile, soil type (mud, snow, sand, etc.), vegetation, obstacles, weather conditions, and vehicle type and characteristics.A physics-based method such as the discrete element method (DEM) (Dasch et al., 2016) was identified by the NATO Next Generation NRMM Team as a potential high fidelity method to model the soil. This method allows the capture of the soil deformation as well as its non-linear behavior. Hence it allows the simulation of the vehicle on any off-road terrain and have an accurate mobility map generated. The drawback of the DEM method is the required simulation time. It takes several weeks to generate the mobility map because of the large number of soil particles (millions) even while utilizing high performance computing.One approach to reduce the computational time is to use machine learning algorithms to predict the mobility map. Machine learning (Boutell et al., 2004; Burges, 1998; Barber et al., 1997) can lead to very accurate mobility predictions over a wide range of terrains. Machine learning is divided into two categories: the supervised and the unsupervised learning. Supervised learning requires the training data to be labeled into predetermined classes, while the unsupervised learning does not require the training data to be labeled. Machine learning can help generate mobility maps using trained models created from a minimum number of simulation runs. In this study different supervised machine learning algorithms such as the support vector machine (SVM), the nearest neighbor classifier (k-NN), decision trees, and boosting methods were used to create trained models labeled as 2 classes for the ‘go/no-go’ map, 5 classes for the 5-speed map, and 7 classes for the 7-speed map. The trained models were created from the physics-based simulation runs of a nominal wheeled vehicle traversing on a cohesive soil.

DeepVM: RNN-Based Vehicle Mobility Prediction to Support Intelligent Vehicle Applications

The recent advances in vehicle industry and vehicle-to-everything communications are creating a huge potential market of intelligent vehicle applications, and exploiting vehicle mobility is of great importance in this field. Hence, this article proposes a novel vehicle mobility prediction algorithm to support intelligent vehicle applications. First, a theoretical analysis is given to quantitatively reveal the predictability of vehicle mobility. Based on the knowledge earned from theoretical analysis, a deep recurrent neural network (RNN)-based algorithm called DeepVM is proposed to predict vehicle mobility in a future period of several or tens of minutes. Comprehensive evaluations have been carried out based on the real taxi mobility data in Tokyo, Japan. The results have not only proved the correctness of our theoretical analysis but also validated that DeepVM can significantly improve the quality of vehicle mobility prediction compared with other state-of-art algorithms.

VeMo: Enabling Transparent Vehicular Mobility Modeling at Individual Levels with Full Penetration

Understanding and predicting real-time vehicle mobility patterns on highways are essential to address traffic congestion and respond to the emergency. However, almost all existing works (e.g., based on cellphones, onboard devices, or traffic cameras) suffer from high costs, low penetration rates, or only aggregate results. To address these drawbacks, we utilize Electric Toll Collection systems (ETC) as a large-scale sensor network and design a system called VeMo to transparently model and predict vehicle mobility at the individual level with a full penetration rate. Our novelty is how we address uncertainty issues (i.e., unknown routes and speeds) due to sparse implicit ETC data based on a key data-driven insight, i.e., individual driving behaviors are strongly correlated with crowds of drivers under certain spatiotemporal contexts and can be predicted by combining both personal habits and context information. We evaluate VeMo with (i) a large-scale ETC system with tracking devices at 773 highway entrances and exits capturing more than 2 million vehicles every day; (ii) a fleet consisting of 114 thousand vehicles with GPS data as ground truth. Compared with state-of-the-art benchmark mobility models, the experimental results show that VeMo outperforms them by average 10% in terms of accuracy.

Testing Design Optimization for Uncertainty Reduction in Generating Off-Road Mobility Map Using a Bayesian Approach

Abstract The Next Generation NATO Reference Mobility Model (NG-NRMM) plays a vital role in vehicle mobility prediction and mission planning. The complicated vehicle–terrain interactions and the presence of heterogeneous uncertainty sources in the modeling and simulation (M&S) result in epistemic uncertainty/errors in the vehicle mobility prediction for given terrain and soil conditions. In this paper, the uncertainty sources that cause the uncertainty in mobility prediction are first partitioned into two levels, namely uncertainty in the M&S and uncertainty in terrain and soil maps. With a focus on the epistemic uncertainty in the M&S, this paper presents a testing design optimization framework to effectively reduce the uncertainty in the M&S and thus increase the confidence in generating off-road mobility maps. A Bayesian updating approach is developed to reduce the epistemic uncertainty/errors in the M&S using mobility testing data collected under controllable terrain and soil conditions. The updated models are then employed to generate the off-road mobility maps for any given terrain and soil maps. Two types of design strategies, namely testing design for model selection and testing design for uncertainty reduction, are investigated in the testing design framework to maximize the information gain subject to limited resources. Results of a numerical example demonstrate the effectiveness of the proposed mobility testing design optimization framework.

Edge-Assisted Vehicle Mobility Prediction to Support V2X Communications

Vehicle-to-everything (V2X) communications have a great potential of enabling future intelligent vehicle applications, and exploiting vehicle mobility is of great importance in designing efficient V2X protocols and applications. Thus, this paper proposes a novel edge-assisted algorithm that makes use of the resources in both cloud and edge sides of vehicular networks to predict vehicle mobility. The proposed algorithm adopts a hybrid architecture of convolutional and recurrent neural networks, and enables computationally efficient transfer learning in each vehicle to generate its customized mobility prediction model. Extensive evaluations have been conducted by using a real taxi mobility data set that is obtained from a testbed deployed in Tokyo, Japan. The results have validated that, compared with other state-of-art algorithms, our proposal improves the prediction F1 score of vehicle mobility by more than 30%, especially for those vehicles that own a strong individual mobility preference.

Understanding the Effects of a Discrete Element Soil Model's Parameters on Ground Vehicle Mobility

The Army's mission is to develop, integrate, and sustain the right technology solution for all manned and unmanned ground vehicles, and mobility is a key requirement for all ground vehicles. Mobility focuses on ground vehicles' capabilities that enable them to be deployable worldwide, operationally mobile in all environments, and protected from symmetrical and asymmetrical threats. In order for military ground vehicles to operate in any combat zone, mobility on off-road terrains should be extensively investigated. Mobility on off-road terrains is poorly understood because of the empirical and semi-empirical height-field based methods which are often used for predicting vehicle mobility, such as Bekker–Wong type models. Those methods do not capture the three-dimensional soil deformation/flow as well as the soil's nonlinear behavior. The discrete element method (DEM) in which soil is modeled using discrete particles was identified as a high-fidelity method that can capture the deformation of the soil and its nonlinear behavior. In this paper, a simulation study is undertaken to understand the influence of DEM soil model parameters on vehicle mobility. A typical wheeled vehicle model was built in ivress/dis software and simulated over different cohesive and noncohesive soils modeled using DEM, with a particular emphasis on weak soils (with both low friction angle and low cohesion). Some characteristics of these soils were varied, namely, the interparticle cohesion, the interparticle friction, the particle size, and the particle mass. The mobility measures, including vehicle speed, wheel sinkage, wheel slip, and tractive force were evaluated using the model and correlated to the DEM soil model parameters. This study shows that the vehicle speed increases with cohesion, friction, soil density, and particle size while wheel sinkage, wheel slip, and tractive force decrease with those parameters. The combined influence of those parameters is more complex. Extensive studies of those and other soil parameters need to be carried out in the future to understand their effect on vehicle mobility.

Predictgis 2018 workshop report held in conjunction with ACM SIGSPATIAL 2018

The prediction of human and vehicle mobility in a city is becoming an attracting field. This topic attracts researchers from broad fields including behavioral sciences, where understanding the complexity of the human mobility behavior is one of the hot topic, and also to the industrial partners, who apply such results to many beneficial applications. Recent progress to sensing human mobility via smartphones is boosting this trend. However, due to the complexity and context-dependence of human behavior and the incompleteness and noise of geospatial data collecting from various sensors, the prediction of human and vehicle mobility is still far from solved. This workshop aimed at collecting contributions on the cutting-edge studies in human mobility description, modeling, intelligent computational method which can advance the human and vehicle prediction research. Potential topics included, but were not limited to 1) The next location prediction of individual mobility, 2) The crowd or population mobility prediction, 3) Dynamics of pedestrians, 4) commute flow and migration flow, 5) Traffic congestion, road usage forecast and optimal vehicle routing, 6) Social event forecast using geospatial data, 7) Novel agent mobility simulators, and 8) Case studies of mobility estimation in academia as well as in industrial field.

Mobility Prediction-Assisted Over-the-Top Edge Prefetching for Hierarchical VANETs

Content prefetching brings contents close to end users before their explicit requests to reduce the content retrieval time, which is crucial for mobile scenarios, such as vehicular ad-hoc networks (VANETs). In order to make intelligent prefetching decisions, three questions have to be answered: which content should be prefetched, when and where it should be prefetched. This paper answers these questions by proposing a vehicle mobility prediction-based over-the-top (OTT) content prefetching solution. We proposed a vehicle mobility prediction module to estimate the future connected roadside units (RSUs) using data traces collected from a real-world VANET testbed deployed in the city of Porto, Portugal. We designed a multi-tier caching mechanism with an OTT content popularity estimation scheme to forecast the content request distribution. We implemented a learning-based algorithm to proactively prefetch the user content to VANET edge caching at RSUs. We implemented a prototype using Raspberry Pi emulating RSU nodes to prove the system functionality. We also performed large-scale OpenStack experiments to validate the system scalability. Extensive experiment results prove that the system can bring benefits for both end-users and OTT service providers, which help them to optimize network resource utilization and reduce bandwidth consumption.

Application of Terramechanics in Off-road Vehicle Performance Prediction

This paper is an introduction of terramechanics and its application in off-road vehicle mobility prediction. Firstly the concept of terramechanics is introduced. Terramechanics is an important branch of applied mechanics in which the interaction between the vehicle and its operating terrain is studied. Secondly, many well-known techniques in terramechanics, such as Cone Penetrometer technique, Bevameter technique are introduced. By comparing the RCI of soil with VCI of vehicle, trafficability of Off-road vehicle can be determined which predicts the “go/no go” property of vehicle. Furthermore, to predict the speed behavior of off-road vehicle over trafficable terrain, three types of analyzing methods, empirical, semi-empirical, computer-aided, are presented in detail. It is evident that computeraided method is much more efficient in simulating terrain-vehicle interaction and produces more realistic prediction in vehicle mobility. Therefore, it is meaningful to develop computer-aided method to provide engineers with powerful parametric analysis in vehicle performance and design.

PredictGIS 2017 workshop report held in conjunction with ACM SIGSPATIAL 2017

The prediction of human and vehicle mobility in a city is becoming attracting field. This topic attracts researchers in broad field from the behavioral science, where understanding the complexity of the human mobility behavior is one of the hot topic, to industrial field, which apply the result to many beneficial applications. Recent progress to sensing human mobility via smartphones is boosting this trend. However, due to the complexity and context-dependence of human behavior and the incompleteness and noise of geospatial data collecting from various sensors, the prediction of human and vehicle mobility is still far from solved. This workshop aimed at collecting contributions on the cutting-edge studies in human mobility description, modeling, intelligent computational method which can advance the human and vehicle prediction research. Potential topics included, but were not limited to 1) The next location prediction of individual mobility, 2) The crowd or population mobility prediction, 3) Dynamics of pedestrians, 4) commute flow and migration flow, 5) Traffic congestion, road usage forecast and optimal vehicle routing, 6) Social event forecast using geospatial data, 7) Novel agent mobility simulators, and 8) Case studies of mobility estimation in academia as well as in industrial field.

Prediction of vehicle mobility on large-scale soft-soil terrain maps using physics-based simulation

A high-fidelity physics-based approach for predicting vehicle mobility over large soft-soil terrain maps is presented. The approach is based on an HPC design-of-experiments (DOE) procedure, and the integration of multibody dynamics for modelling the vehicle and the discrete element method (DEM) for modelling the soil into one solver. A general cohesive soil DEM material model is used which includes the effects of cohesion, elasticity, plasticity/compressibility, damping, friction, and viscosity. To manage problem size, a novel moving soil patch technique is developed in which DEM particles which are behind the vehicle are continuously eliminated and then reemitted in front of the vehicle, levelled and compacted. The DEM inter-particle cohesion and friction are calibrated to the cone index using a simulation of a cone penetrometer. The DOE approach is demonstrated by predicting the speed-made-good distribution on 22 × 22 km terrain map for a 4 × 4 military vehicle. Two terrain parameters are considered in the DOE: terrain positive slope and soil strength.