Vehicular Motion Research Articles

Harnessing piezoelectric sensors for electricity generation from speed breaker

Abstract The population of India is increasing drastically. In 2024, it has reached 140 crores, which leads to more energy consumption. The energy generated from conventional sources is limited and non-renewable. Also, due to urbanization, the energy demand is increased tremendously. So, there is a need to generate energy through renewable sources. One of the effective ways to generate renewable energy is by using piezoelectric cells. Piezoelectric cells are the cells that can generate electric charge through mechanical stress. The mechanical stress is generated on the roads, especially on the road breakers. This mechanical stress is created by vibrations of the vehicles on the speed breaker which will induce electrical energy. By installing piezoelectric cells beneath the speed breaker, the generated mechanical energy can be converted into electrical energy. The generated electricity can be utilized for street lighting, traffic signals, etc. It helps in generating clean and renewable energy from vehicular motion, leading to sustainable development.

POWER GENERATION USING SPEED BREAKER

This research paper investigates the feasibility and efficacy of generating electricity from speed breakers, utilizing the kinetic energy dissipated by vehicular motion. With the escalating global demand for sustainable energy sources, the potential of speed breakers as a renewable energy solution gains significance. The study explores various methodologies and technologies employed worldwide to capture and convert kinetic energy into electrical power. Key aspects such as energy conversion efficiency, environmental impact, and economic viability are analyzed to assess the practicality of speed breaker power generation systems. The paper also examines case studies and real-world implementations, highlighting their successes, challenges, and future prospects. Through this comprehensive examination, the research aims to contribute to the advancement of renewable energy infrastructure, emphasizing the role of speed breakers in fostering sustainable development. Key Words: Power generation, speed breakers, kinetic energy, renewable energy, sustainability, energy conversion efficiency, environmental impact, economic viability, case studies, infrastructure, sustainable development.

Vehicular Motions Dominate the Ion Transport in Concentrated LiTFSI Aqueous Solutions?

Water-in-salt electrolytes (WiSEs) show great promise for applications in grid-scale energy storage. The design of high-performance WiSEs requires a comprehensive understanding of their microstructures and ion transport properties. In the present work, based on the CL&Pol force field, we have developed a polarizable force field (PFF) tailored for high-concentration LiTFSI aqueous solutions, which accurately reproduces the structural and dynamical properties. Unlike the literature, we do not observe the presence of bulk-like water in LiTFSI solutions exceeding 19 mol/kg. Furthermore, we find that the vast majority of Li(H20)n+ are short-lived, and thus, the structural motion rather than the vehicular motion is the main mode of ion transport. Our results have significant implications for understanding the ion dynamics in WiSEs. Additionally, further in-depth experimental analyses are imperative.

Developmental differences in children’s adaptation to vehicle distance and speed in street-crossing decision-making

Introduction: Young children cannot effectively adapt their behaviors to vehicles at varied distances and speeds, which is a critical cause of road accidents. However, the impact of this crucial ability on children’s street-crossing decision-making and the age at which they acquire it remain unclear. Method: This study examined the crossing decision-making behavior of children at 6, 8, and 11 years of age in facing 51 different videotaped traffic scenarios with varying vehicle distances and speeds. Sixty Chinese elementary school students, with 20 children evenly distributed into each of the three age groups (6 years, 8 years, and 11 years old), participated in a simulated street-crossing task using video projections. Hierarchical logistic regression models were used to analyze how age moderated the effects of vehicular motion factors (vehicle–pedestrian distance, vehicle speed) on children’s crossing safety, including dangerous crossing and crossing decision-making. Results: The results showed that when either vehicle–pedestrian distance decreased or vehicle speed increased all age groups tended to cross less frequently but probability of dangerous crossing increased. Compared to 8-year-old and 11-year-old children, 6-year-old children showed a less pronounced tendency toward both of these crossing decision-making behaviors, and had more dangerous crossing outcomes. Conclusions: These findings suggest that inadequate adaptation to vehicle–pedestrian distance and vehicle speed may partly contribute to the inferior safety of street-crossing behavior in 6-year-olds compared to 8-year-olds. No significant differences were observed between 8- and 11-year-old children, suggesting the turning point for this ability might occur between 6 and 8 years of age. Practical Applications: Preventive measures aimed to reduce crossing risks for children should consider children’s developmental stages.

Large-scale closed and generalized networks of ribosome flow model with different site sizes

Dynamical systems play a key role in understanding the different biophysical aspects of many complex transport phenomena. The ribosome flow model with different site sizes (RFMD) is a dynamical system relevant to model various biological and physical processes such as phosphorelay, motion of vehicles along an isolated road, and more. However, many transport processes take place concurrently which give rise to networks comprising multiple tracks e.g., vehicular motion, cargo transport along the cytoskeletal highway, etc. Here, we present two large-scale network models: RFMDs network with a pool (RFMDNP), and the generalized network of RFMDs (RFMDN). The RFMDNP is a closed system that can model the simultaneous movement of particles along tracks having sites of different capacities in a resource-limited environment. We prove that the RFMDNP admits a continuum of linearly ordered steady-state points and it always phase-locks with the periodic excitations in the parameters. Furthermore, we also show that increasing any of the parameters in an RFMD leads to an increase in the output rate of this RFMD, and the output rate of other RFMDs all increase or decrease. Next, we analyze a generalized network of RFMDs that models static connections between the RFMDs and prove the important mathematical properties: (a) the network admits a unique steady-state, and (b) the problem of determining the connecting weights to maximize the steady-state network output rate is a convex optimization problem. We believe that these networks can have wide applicability to model various non-linear phenomena since their behavior is predictable and ordered.

A Review of Dynamic Vehicular Motion Simulators: Systems and Algorithms

Vehicular motion simulators have evolved to become an important contributor to major industries, including defense, aerospace, and vehicle manufacturing. During the past few decades, many efforts have been made towards developing robust, adaptive motion simulators with the highest level of fidelity and realism. Thus, it is important to recollect, in order to evaluate the current state of complex robotic systems, encouraging careful planning of further improvements in the future. This article focuses on the current motion simulators’ structural designs and working principles alongside the currently developed motion control algorithms to achieve the highest fidelity. Furthermore, within the era of industry 4.0 and the fast-paced merging of technologies into key industries, some suggestions are made for future works which it is believed are worth investigating to provide robustness and adaptivity to the control of simulation systems, improving their fidelity and realism alongside reducing motion sickness experienced by the simulator operator.

Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest

Accurate crash frequency prediction is critical for proactive safety management. The emerging connected vehicles technology provides us with a wealth of vehicular motion data, which enables a better connection between crash frequency and driving behaviors. However, appropriately dealing with the spatial dependence of crash frequency and multitudinous driving features has been a difficult but critical challenge in the prediction process. To this end, this study aims to investigate a new Artificial Intelligence technique called Geographical Random Forest (GRF) that can address spatial heterogeneity and retain all potential predictors. By harnessing more than 2.2 billion high-resolution connected vehicle Basic Safety Message (BSM) observations from the Safety Pilot Model Deployment in Ann Arbor, MI, 30 indicators of driving volatility are extracted, including speed, longitudinal and lateral acceleration, and yaw rate. The developed GRF was implemented to predict rear-end crash frequency at intersections. The results show that: 1) rear-end crashes are more likely to happen at intersections connecting minor roads compared to major roads; 2) a higher number of hard acceleration and deceleration events beyond two standard deviations in the longitudinal direction is a leading indicator of rear-end crashes; 3) the optimal GRF significantly outperforms Global Random Forest, with a 9% lower test error and a substantially better fit; and 4) geographical visualization of variable importance highlights the presence of spatial non-stationarity. The proposed framework can proactively identify at-risk intersections and alert drivers when leading indicators of driving volatility tend to worsen.

A Lightweight Long-Term Vehicular Motion Prediction Method Leveraging Spatial Database and Kinematic Trajectory Data

Long-term vehicular motion prediction is a crucial function for both autonomous driving and advanced driver-assistant systems. However, due to the uncertainties of vehicle dynamics and complexities of surroundings, long-term motion prediction is never trivial work. As they combine effects of humans, vehicles and environments, kinematic trajectory data reflect several aspects of vehicles’ spatial behaviors. In this paper, we propose a novel method that leverages spatial database and kinematic trajectory data to achieve long-term vehicular motion prediction in a lightweight way. In our system, a spatial database system is initially embedded in an extended Kalman filter (EKF) framework. The spatial kinematic trajectory data are managed through the database and directly used in motion prediction; namely, weighted means are derived from the spatially retrieved kinematic data and used to update EKF predictions. The proposed method is validated in the real world. The experiments indicate that different weighting methods make a slight accuracy difference. Our method is not data-and-computation-consumed; its performance is acceptable in the limited data conditions and its prediction accuracy is improved as the size of used data sets increases; our method can predict in real time. The efficiency of an unscented Kalman filter (UKF) is compared with that of the EKF. The results show that the UKF can hardly meet real-time requirements.

Super-Twisting Sliding Mode Control for Differential Steering Systems in Vehicular Yaw Tracking Motion

This paper deals with the modelling and control of differential steering for vehicular motions with the help of Sliding Mode Control (SMC) with a Super-Twisting Algorithm (STA). A single-track mechanical model of the vehicular dynamics is proposed and validated by simulations. A control strategy based on the STA is proposed for the yaw dynamics to improve travel comfort and the stability of the vehicular motion. The desired yaw trajectory is given and the equivalent and corrective control parts of the STA are calculated by the controller. Simulation results show the effectiveness of the proposed control strategy.

Cooperative car-following control with electronic throttle and perceived headway errors on gyroidal roads

Gyroidal roads are not uncommon on mountainous freeways. In the provision of complex road information such as visual field and slope, some errors between the perceived headway and the real headway information may exist subject to the accuracy of sensors. With the penetration of automated and autonomous vehicles, there is an increasing need for developing a systematic controlling approach to ensure efficient and safe vehicular motion on gyroidal roads. This study contributes to devising a vehicle-to-vehicle communication-based feedback control method with such considerations. A modified car-following model considering perceived headway errors on gyroidal roads is presented. The neutral stability curves of the new model are derived according to the classical control method, which verifies the significant impact of the error coefficient, slope information, and perception time on the robustness of the model, and that the effect of the error coefficient and slope information is positive while that of the perception time is negative. Given the importance of electronic throttle angle information for vehicle-to-vehicle communication technology, a cooperative control signal is designed for the scenario when the above stability condition is unsatisfied. The numerical simulation example verifies the effectiveness of the controller in alleviating traffic congestion and improving road traffic efficiency.

Decentralized route-planning for multi-vehicle teams to satisfy a subclass of linear temporal logic specifications

Linear temporal logic (LTL) formulae are widely used to provide high level behavioral specifications on mobile robots. We propose a decentralized route-planning method for a networked team of mobile robots to satisfy a common (global) LTL specification. The global specification is assumed to be a conjunction of multiple formulae, each of which is treated as a task to be assigned to one or more vehicles. The vehicle kinematic model consists of two modes: a hover mode and a constant-speed forward motion mode with bounded steering rate. The proposed method leverages the consensus-based bundle algorithm for decentralized task assignment, thereby decomposing the global specification into local specifications for each vehicle. We develop a new algorithm to assign collaborative tasks: those simultaneously assigned to multiple vehicles. Rewards in the proposed task assignment algorithm are computed using the so-called lifted graph, which ensures the satisfaction of minimum turn radius constraints on vehicular motion. This algorithm synchronizes the vehicles’ routes with minimum waiting durations. The proposed method is compared against a multi-vehicle task assignment algorithm from the literature, which we extend to apply for satisfying LTL specifications. The proposed route-planning method is demonstrated via numerical simulation examples and a hardware implementation on networked single-board computers.

Detection of Road Accidents Using Synthetically Generated Multi-Perspective Accident Videos

Road accidents are often caused by short abnormal events, including traffic violations, abrupt change in vehicular motion, driver fatigue, etc. Observing an accident event from the right camera perspective plays a crucial role while detecting accidents. However, it may not be possible to capture such abnormal events from a limited camera perspective. We present a deep learning framework to analyze the accident events recorded from multiple perspectives. First, we estimate feature similarity in videos recorded from multiple perspectives. We then divided the video samples into high and low feature similarity groups. Next, we extract spatio-temporal features from each group using two-branch DCNNs and fuse them using a rank-based weighted average pooling strategy followed by classification. We present a new road accident video dataset (MP-RAD), where each accident event is synthetically generated and captured from five independent camera perspectives using a computer gaming platform. Most of the existing road accident datasets use egocentric views or they are captured in fixed camera setups. However, our dataset is large and multi-perspective that can be used to validate ITS-related tasks such as accident detection, accident localization, traffic monitoring, etc. The dataset contains 400 accident events with a total of 2000 videos. We provide temporal annotations of all videos. The proposed framework and the dataset have been cross-validated with latest accident detection baselines trained on real-world road accident videos and vice-versa. The sub-optimal detection accuracy obtained using the baselines indicates that the proposed framework and the dataset can be useful for ITS related research. Code and dataset is available at: https://github.com/draxler1/MP-RAD-Dataset-ITS-

AUKF based unified estimation scheme for non-linear vehicle dynamics

The paper presents an additive unscented Kalman filter (AUKF) based modular approach to estimate lateral vehicle dynamics and tyre forces. In this approach, a simplified single-track vehicular model and a dynamics-oriented tyre model are adopted to represent the vehicular motion. Subsequently, multiple observer modules for each dynamical state are designed and integrated into a unified estimation scheme (UES-AUKF). Two additional non-modular observers using AUKF and extended Kalman filter (EKF) are designed for comparative analysis of estimation accuracy and computational efficiency of the designed scheme. The simplified model and the designed estimators are simulated using double lane change (DLC) and sinusoidal (Sine) manoeuvres for high and low µ surfaces, respectively, and the results are analysed. Thereby the scheme is further validated using real vehicle dataset for estimation accuracy. Simulation results for the simplified vehicle and the tyre model conform with the standard results with acceptable deviations. Also, the proposed scheme exhibits improved accuracy with reduced computational time as compared to the non-modular observers.

Three Layered Architecture for Driver Behavior Analysis and Personalized Assistance with Alert Message Dissemination in 5G Envisioned Fog-IoCV

The Internet of connected vehicles (IoCV) has made people more comfortable and safer while driving vehicles. This technology has made it possible to reduce road casualties; however, increased traffic and uncertainties in environments seem to be limitations to improving the safety of environments. In this paper, driver behavior is analyzed to provide personalized assistance and to alert surrounding vehicles in case of emergencies. The processes involved in this research are as follows. (i) Initially, the vehicles in an environment are clustered to reduce the complexity in analyzing a large number of vehicles. Multi-criterion-based hierarchical correlation clustering (MCB-HCC) is performed to dynamically cluster vehicles. Vehicular motion is detected by edge-assisted road side units (E-RSUs) by using an attention-based residual neural network (AttResNet). (ii) Driver behavior is analyzed based on the physiological parameters of drivers, vehicle on-board parameters, and environmental parameters, and driver behavior is classified into different classes by implementing a refined asynchronous advantage actor critic (RA3C) algorithm for assistance generation. (iii) If the driver’s current state is found to be an emergency state, an alert message is disseminated to the surrounding vehicles in that area and to the neighboring areas based on traffic flow by using jelly fish search optimization (JSO). If a neighboring area does not have a fog node, a virtual fog node is deployed by executing a constraint-based quantum entropy function to disseminate alert messages at ultra-low latency. (iv) Personalized assistance is provided to the driver based on behavior analysis to assist the driver by using a multi-attribute utility model, thereby preventing road accidents. The proposed driver behavior analysis and personalized assistance model are experimented on with the Network Simulator 3.26 tool, and performance was evaluated in terms of prediction error, number of alerts, number of risk maneuvers, accuracy, latency, energy consumption, false alarm rate, safety score, and alert-message dissemination efficiency.

The charge transport mechanism in Brønsted-acidic protic ionic liquid/water systems – An NMR and QENS study

In this study, a protic ionic liquid (PIL), 2-Sulfoethylmethylammonium triflate [2-Sema][TfO] is considered as a potential new proton conducting electrolyte for future polymer membrane fuel cells capable of ambient air operation above 100 °C. The proton dynamics of the PIL with residual water are examined as a function of the hydration level on different time scales using pulsed field gradient nuclear magnetic resonance (PFG-NMR) and quasi-elastic neutron scattering (QENS). The separation of the different contributing relaxation processes enables a quantification of the proton fractions for the underlying hopping or vehicular motions. The hopping motion of the water in the time scale of picosecond and the vehicular motion in the time scale of nanosecond are detected by means of QENS. Such dynamic processes can be well described by the Chudley-Elliot jump model. This emphasised the presence of fixed jump lenghts. In the timescale of millisecond, the cooperative transport of the active protons of the acidic SO3H group and of the H2O molecules, as well as the vehicular transport of the PIL cations are detected by NMR. The different diffusion coefficients obtained by the NMR and QENS techniques are discussed in detail.

Environmental monitoring through Vehicular Ad Hoc Network: A productive application for smart cities

SummaryIndustrial growth, technological development, and increased human activities for comfort and luxurious life increase different environmental parameters like temperature, humidity, carbon monoxide, methane, and smoke. The value of these parameters are increasing steadily which has adverse effects on human life and human health and is also the cause of various disease like cancers and lung disease. It is now essential to monitor the environmental parameters of a region frequently. For the automatic continuous monitoring of environmental parameters of any location, this paper proposes to use vehicular ad hoc network (VANET), which is one of the promising subclasses of mobile ad hoc network (MANET). Automatic monitoring of environmental parameters of any location includes measurement of environmental parameters of a location, transmission of environmental parameters from the measured location to the observation center, and analysis of the parameters at the observation center. Two aspects are discussed for the transmission of parameters from the location of measurement to the observation center: first by vehicular communication and second by vehicular cloud. The analysis of the environmental parameters at the observation center is done using probabilistic neural network (PNN). Apart from this, different generic network parameters like end‐to‐end delay, number of hops, and network gaps for the proposed work are computed for the transmission of different environmental parameters and is compared with different VANET routing protocols like geographic source routing (GSR), anchor‐based street and traffic‐aware routing (A‐STAR), and peripheral node‐based geographic distance routing (P‐GEDIR). Vehicular motion is created using Simulation of Urban MObility (SUMO), and the network parameter values, like end‐to‐end delay and number of hops, are obtained using OMNET++. A testbed experiment is conducted for the classification of sensor data which is adoptable in practice.

Micro-Doppler signature based target recognition using covariance based S-transform

Micro-Doppler signature analysis play an important role for target recognition such as fixed-target, vibrating target, moving human, vehicular motion, etc. Therefore, various signal processing based methods are reported for target recognition. Recently, Short time Fourier transform (STFT), has been applied for target recognition. In this work, we have applied Covariance based S-transform for analysis of fixed target, vibrating target. Also, the performance of Covariance based S-transform method has been compared with STFT.

Collision Risk Assessment Service for Connected Vehicles: Leveraging Vehicular State and Motion Uncertainties

The Internet of Things plays an indispensable role in the development of connected vehicles, which will pave the way for road safety applications. In recent years, the concept of a cooperative collision warning system (CCWS) has been introduced and developed to enhance road safety, and it has been seen as a typical Internet-of-Vehicles application. In most CCWSs, it is vital to have a detection mechanism based on trajectory predictions where the uncertainties associated with vehicular state and motion are complex. However, most available approaches in this regard did not consider these uncertainties. Hence, this article proposes a new collision risk assessment (CRA) method where sigma trajectories that include multiple possible trajectories considering multiple aspects of vehicular motion are designed to cope with vehicular uncertainties. Our method is implemented in a novel server-based architecture, which is different from the commonly used vehicle-based controlled CCWSs. The CRA is provided as a service by a cloud server. The proposed method and architecture are validated and evaluated through extensive real-world experiments. Experimental results show that our method outperforms a referenced method in terms of CRA and achieves better robustness in tolerating communication delays and dropouts. Latencies in CRA service were analyzed, and it was found that powerful computing resources provided by cloud servers can significantly decrease computational cost, which will indirectly compensate for communication costs in the future. Based on our high-performance CRA method, the proposed architecture can be regarded as a novel option for CCWS design.

A hierarchical estimation scheme of tire-force based on random-walk SCKF for vehicle dynamics control

The estimation of vehicle dynamics states is crucial for vehicle stability control and autonomous driving, especially tire-force which governs vehicular motion. However, measuring tire-force directly needs expensive measurement instruments, moreover, vehicle non-linear characteristics, vehicle parameter uncertainties, unknown key variables, and sensor noise could cause great challenges in its observation. Therefore, this paper aims to develop an accurate, affordable tire-force estimator to tackle the above-mentioned issues. A novel hierarchical estimation scheme based on random-walk square-root cubature Kalman filter (SCKF) is proposed and it works without a complex tire model and considers vehicle parameter uncertainties. The estimator scheme contains three blocks. The first block estimates related key variables of tire force observation. The second block estimates both vertical tire-force and longitudinal tire-force. The longitudinal tire-force is observed based on effective tire radius identification, and a proportional integral differential (PID) method is derived based on the Lyapunov principle. Then, a random-walk SCKF algorithm is presented to estimates lateral tire-force. To validate the effectiveness of the proposed estimation scheme, CarSim & Matlab/Simulink joint simulation and real car tests are carried out. Results show that the proposed estimation scheme's accuracy performance and its potential as an affordable solution for the tire-force observation.

Solvation and transport of lithium ions in deep eutectic solvents.

Lithium based deep eutectic solvents (DESs) are excellent candidates as eco-friendly electrolytes for lithium ion batteries. While some of these DESs have shown promising results, a clear mechanism of lithium ion transport in DESs is not yet established. This work reports the study on the solvation and transport of lithium in a DES made from lithium perchlorate and acetamide using Molecular Dynamics (MD) simulation and inelastic neutron scattering. Based on hydrogen bonding (H-bonding) of acetamide with neighboring molecules/ions, two states are largely prevalent: (1) acetamide molecules that are H-bonded to lithium ions (∼36%) and (2) acetamide molecules that are entirely free (∼58%). Analyzing their stochastic dynamics independently, it is observed that the long-range diffusion of the former is significantly slower than that of the latter. This is also validated from the neutron scattering experiment on the same DES system. Furthermore, the analysis of the lithium dynamics shows that the diffusion of acetamide molecules in the first category is strongly coupled to that of lithium ions. On an average, the lithium ions are H-bonded to ∼3.2 acetamide molecules in their first solvation. These observations are further bolstered through the analysis of the H-bond correlation function between acetamide and lithium ions, which shows that ∼90% of lithium ionic transport is achieved by vehicular motion where the ions diffuse along with their first solvation shell. It is also observed that the ionic motions are largely uncorrelated and the conductivity of lithium ions in the DES is found to be 11 mS/cm. The findings of this work are an important advancement in understanding solvation and transport of lithium in the DES.