Vehicle Distance Research Articles

Human-like speed modeling for autonomous vehicles during car-following at intersections

This study aims to model drivers’ speed in car-following during braking situations at intersections to estimate a safe comfortable human-like speed at the minimum car-following distance for autonomous vehicles (AV). Several car-following behavioral measures were extracted at different times before reaching the minimum following distance and the intersection control type (signalized or unsignalized) was recorded to train the model using three machine-learning techniques. The results showed that the XGBoost model is superior to other techniques with R2 values of 0.99 and 0.97 for training and testing datasets, respectively. The results also indicated that the control type impacts driver speed at the minimum following distance. The modeled speed will provide a more comfortable experience for AV riders and will not violate the expectations of the surrounding traditional vehicle drivers. Also, the proposed model can be adopted to enhance current car-following models by considering the effect of intersections and their control type.

Effects of full windshield head-up display on visual attention allocation

This study explores the impact of full windshield head-up display (FHUD) cues on the visual attention allocation of drivers under different scenarios. Forty-eight participants with driving experience were randomly divided into two groups and asked to drive on a preset route in various virtual driving scenarios created in advance. The full windshield highlights situational cues related to the driving task, such as lane lines, safe vehicle distance warnings, navigation guidance and pedestrian cues. Regarding the perception of situational cues, the number of fixations and mean fixation duration with FHUD were lower than those without FHUD. Furthermore, the dwell time percentage of the driver’s forward view with FHUD was larger than that without FHUD, and the dwell time percentage on both sides was smaller than that without FHUD. In conclusion, FHUD may help drivers more effectively perceive cues and improve drivers’ visual attention allocation. Practitioner summary: FHUD may affect drivers’ attention while driving. We examined the effect of FHUD on number of fixations, fixation duration and dwell time percentage in the area of interest under different weather scenarios. Experimental results indicated that FHUD could improve drivers’ visual attention allocation and help drivers more effectively perceive cues. Abbreviations: FHUD: full windshield head-up display; HUD: head-up display; HDD: head-down display; AOI: area of interests; AR: augmented reality

Subjective experiences of bicyclists being passed by motor vehicles: The relationship to motor vehicle passing distance

BackgroundFeeling unsafe when riding a bicycle is a key barrier to cycling participation. To better understand the experiences of cycling on-road, this study aimed to explore the relationship between cyclists’ subjective experiences and the lateral passing distance of motor vehicles. MethodsAn on-road observational study was conducted in Victoria, Australia. Participants had a custom device installed on their bicycle that measured the lateral passing distance of motor vehicles and included a handlebar mounted “panic button” that participants could press when they felt that a passing event was too close or unsafe. A random effects logistic regression model was used to investigate the relationship between cyclist sex, motor vehicle type and infrastructure characteristics, and button press events. ResultsA total of 217 button press events were recorded (1.2 % of all passing events) from 60 participants. For events with a passing distance closer than 100 cm, 10.4 % of these events had a recorded button press. The adjusted odds of a button press event were over three-fold higher when the rider was passed by a truck, compared to when the rider was passed by a sedan. The predicted probability of a button press event was higher in events occurring in road environments with no bike lane and no parked cars (1.9 %) compared to no bike lane with parked cars (1.2 %), a bike lane with no parked cars (0.9 %) and a bike lane with parked cars (0.7 %). ConclusionsThe study identified important links between cyclists’ subjective experience of unsafe events, motor vehicle types and infrastructure characteristics. Greater emphasis needs to be placed on capturing subjective experiences to inform and advance the development and implementation of safe and comfortable cycling infrastructure.

Human–machine cooperative scheme for car-following control of the connected and automated vehicles

To address the out-of-the-loop problem of the automated driving, a human–machine cooperative scheme for car-following control of the connected and automated vehicles (CAVs) is proposed. The proposed scheme can keep the drivers always in the loop and improve the car-following performance. To be specific, the driving automation system (artificial driver) is assigned to the task of velocity tracking and the human driver is responsible for headway adjustment. For the velocity tracking task, a feedforward–feedback control strategy was designed firstly by considering the advantages of the accurate perception and communication of CAVs, then an H∞ suboptimal control method was developed to optimize the controller parameters according to the desired performance index, further the controller was fine-tuned based on the idea of human-simulated intelligent control (HSIC) to improve the dynamic performance of the velocity tracking. For the operator’s headway adjusting task, the stability analysis based on the Lyapunov function proved that the simple proportional feedback control can be assumed by the driver to ensure the system stability under the cooperation of automated velocity tracking. The experiments based on the driving simulator demonstrated that human–machine cooperative scheme for car-following can reduce the tracking error of vehicle distance effectively, and the human driver can be kept in the control loop with a smaller operating load.

A Hierarchical Game Model for OFDM Integrated Radar and Communication Systems

This paper studies the spectrum allocation problem between spectrum service providers (SSPs) and terminals equipped with orthogonal frequency division multiplexing (OFDM) integrated radar and communication (IRC) systems. In particular, IRC-equipped terminals such as autonomous vehicles need to buy spectrum for their radar functions, e.g., sensing and detecting distant vehicles, and communication functions, e.g., transmitting sensing data to road-side units. The terminals determine their spectrum demands from the SSPs subject to their IRC performance requirements, while the SSPs compete with each other on the service prices to attract terminals. Taking into account the complicated interactions, a hierarchical Stackelberg game is proposed to reconcile the spectrum demand and service price, where the SSPs are the leaders and the terminals are the followers. Due to the spectrum constraints of the SSPs, we model the lower-layer subgame among the terminals as a generalized Nash equilibrium problem. An iterative searching algorithm is then developed that guarantees the convergence to the Stackelberg equilibrium. Numerical results demonstrate the effectiveness of our proposed scheme in terms of social welfare compared to baseline schemes.

Multi-depot vehicle routing problem for large scale disaster relief in drought scenarios: The case of the Brazilian northeast region

Emergency water trucking (EWT) is the reactive response to address droughts most adopted by developing countries. Consequently, as droughts are becoming more frequent, severe, and longer due to climate change, there has been an increasing demand for EWT. However, when applied on a large scale to geographically dispersed rural populations, EWT requires the use of a significant vehicle fleet, leading to higher operational costs. It thus becomes crucial to propose solutions that guarantee EWT's financial and technical viability. Therefore, this paper proposes a two-step procedure to address the problem of large-scale water distribution to drought-affected populations. In step one, we use the transportation problem to assign a set of demand points to water sources, and, in step two, we model the problem as Capacitated Vehicle Routing Problems. Moreover, we also explore in this paper possibilities of using a new hybridizing Ant Colony Optimization metaheuristic with Random Variable Neighborhood Descent (MACS-RVND) to search for efficient solutions, in terms of the total distance and number of vehicles used, for large scale EWT. Hence, in the second step of the developed procedure, results from the MACS-RVND are compared with those obtained by Clarke and Wright heuristics with 2-opt, and with results from an Adaptive Large Neighborhood Search approach proposed by Erdogan. The procedure was then applied to a real water distribution case in the Brazilian semi-arid region. Results are compared with the actual procedures adopted in Brazilian water distribution, showing that the developed procedure can promote more efficient, economic, and equitable water distribution.

Data dissemination model using Epidemic and Transmission-Segment-based Geographic Routing protocol: a new perspective in vehicular ad hoc networks

PurposeThe purpose of this paper is to introduce a new data dissemination model in order to improve the performance of transmission in VANET. It proposes a protocol named Epidemic and Transmission-Segment-based Geographic Routing (ETSGR) and outlining the issues due to high mobility of nodes and uncertain physical topologies in the network. The proposed ETSGR is mainly used to analyze the vehicle state, direction, distance, traffic density and link quality of the network.Design/methodology/approachThis research work based on ETSGR protocol mainly uses epidemic algorithm in order to find the vehicle state based on susceptible, infected and recovered (SIR) model. Furthermore, the vehicle position and finding the head node in the network is utilized using the transmission segment protocol based on geographic routing and analyses each node to form the segments and find the destination to transmit the data in timely manner.FindingsThe paper provides the enhancement of the performance based on some metrics such as end-to-end delay that obtained 0.62%, data throughput as 32.3%, packet delivery ratio as 67% and one-hop communication as 13%. The proposed ETSGR protocol analyzes the state of the vehicle correctly and each node segmented to transmit the data with the timely manner and obtaining reliable performance even with high mobility of nodes in the network.Research limitations/implicationsThe proposed ETSGR protocol may have some limitation when considering the timing which should improve even in increasing many number of vehicles and different road segments.Practical implicationsThis paper includes some suggestions for the practical deployment of the approach in which a real-time traffic analysis can be evaluated for taking prior actions during an emergency situation and proper dissemination of data in timely manner can help utilize the guidance of proper planning of roads.Originality/valueThis research fulfills an enhanced protocol to improve the performance of data dissemination.

RETRACTED ARTICLE: IOV-a novel mechanism for initiating secure vehicle data transmission using PSO with two phase authentication

It has been significantly evolved and it also includes its own vehicular communication. The main communications are said to be V2V, V2R, V21 named as Vehicular Ad-hoc Networks (VANET). VANET is said to be an important part of Intelligent Transport System. In VANET is nothing but it is said to be a special class of Mobile Ad-hoc Network (MANET). It consists of both technical and business-related limitations. VANET still does not consider as a very large scale deployment in the world. At present IoV and its significant extension of the VANET Capabilities are discussed with the vehicles present here by using a type of global network was enabled with the help of Wireless Access Technologies (WAT). In additionally, it involves the internet and also it includes heterogeneous access networks. Here along with IoV, the Internet of Things (IoT) is to be considered with RWP to monitor vehicle distance. Additionally, the vehicles present here need to be driven safely especially in the urban traffic management by using the automobile production. This work highlights the impact of IoT for improving the efficacy of ITS towards IoV.

Numerical modeling and dynamic analysis of a floating bridge subjected to wave, current and moving loads

The dynamic analysis of the pontoon-type floating bridge is very important in the design phase. In this paper, a numerical simulation method for calculating the dynamic properties of a floating bridge under the wave, current, and moving loads was presented. In order to verify this method, the calculated vertical displacements of the floating bridge under vehicle loads were compared with the experimental results. The effects of vehicle speeds and distances between neighboring vehicles on the vertical displacements of pontoons were analyzed. The influences of environmental parameters on dynamic characteristics were also investigated. The results show that the vertical displacements of pontoons caused by heavy moving loads are significantly bigger than those caused by environmental loads. In addition, some useful conclusions have been drawn, which aim to provide references for the analysis and design of the pontoon-type floating bridges.

Adaptive Computing Scheduling for Edge-Assisted Autonomous Driving

This paper investigates computing resource scheduling for real-time applications in autonomous driving, such as localization and obstacle avoidance. In our considered scenario, autonomous vehicles periodically sense the environment, offload sensor data to an edge server for processing, and receive computing results from the server. Due to mobility and computing latency, a vehicle travels some distance in the duration between the instant of offloading its sensor data and the instant of receiving the computing result. Our objective is finding a scheduling scheme for the edge sever to minimize the above traveled distance of vehicles. The approach is to determine the processing order according to individual vehicle mobility and computing capability of the edge server. We formulate a restless multi-arm bandit (RMAB) problem, design a Whittle index based stochastic scheduling scheme, and determine the index using a deep reinforcement learning (DRL) method. The proposed scheduling scheme avoids the time-consuming policy exploration common in DRL scheduling approaches and makes effectual decisions with low complexity. Extensive simulation results demonstrate that the proposed indexed-based scheme can deliver computing results to the vehicles promptly while adapting to time-variant vehicle mobility.

Auction based Energy-Efficient Cooperative Relay Scheduling in Bidirectional Highway Scenarios for VANET

In a Vehicular Adhoc Network (VANET), Road Side Units (RSUs) deployed along the highways are generally dispossessed from a permanent connection to grid-power and mostly the RSU operations are sustainable on alternative sources such as solar power, wind power, etc. The power consumption of RSU is dominated by the downlink (RSU-to-vehicle) communication cost and it is a function of RSU to vehicle distance. Moreover, the RSUs cannot cover entire highway segment due to high deployment cost and they leave an outage area in between neighboring RSUs. In this case, a target vehicle entering into an outage area with unserved requests can be served by selecting relay (store-carry-forward) vehicles. This paper proposes RSU assisted relay scheduling algorithm for faster data retrieval to the target vehicle while minimizing the RSU energy consumption. Firstly, a time slot based relay scheduling problem is formulated and then the NP-hardness of the problem is proved. Secondly, a greedy algorithm (GA) and a forward relay scheduler (FRS) are proposed to schedule the relay vehicles in forward direction (i.e. target moving direction). The GA selects the relay vehicles which are near to RSU and takes less time to reach the target vehicle. The FRS apply the concepts of Auction Theory to optimally assign the relay vehicles to time slots, that can jointly minimize RSU energy consumption and end-to-end delay to the target vehicle. Here, the Auction process allows the relay vehicles to bid for time slots by using their local information such as speed, location, cooperative cache size, direction, etc. Furthermore, the proposed Auction based RSU assisted relay scheduling (RRS) triggers the neighboring RSUs which can cooperate by sharing unserved data and schedule the relay vehicles driving in both forward and backward (i.e. opposite to target) directions in a bidirectional highway. Simulation results show that the efficacy of the proposed algorithms over other relay scheduling algorithms with respect to energy consumption, end-to-end delay and residual data delivery.

MODELAREA ÎN MATLAB/SIMULINK A FUNCȚIONĂRII UNUI VEHICUL ELECTRIC ÎN DIFERITE CICLURI DE ACȚIONARE

Through this paper, we want to improve the methods of dynamic design of electric vehicles, by creating simulation models in Simulink. The models are represented by simulation schemes that consist of blocks of mathematical operators, interconnected based on mathematical calculation formulas. The initial input sizes, specified in Table 1, can be easily modified, giving the possibility to obtain several models for different design ideas. The results of the simulations are materialized in graphical diagrams and provide virtual time data on vehicle speed, acceleration, engine torque, wheel power, energy consumed, and distance traveled. In the first simulation scheme, the input variable shows a motor torque generator with values between 0-132 Nm. In the second simulation scheme, the input variable is speed, according to the ECE-15 urban cycle scenario.

Research on Propagation Characteristics of Seismic Signal for Moving Armoured Targets

This paper is focused on the Seismic Signal characteristics of wheel and track vehicles. The effect of vehicle speed of these two vehicles on the Seismic Signal was studied through experimental and theorical methods. The Seismic Signal s of these two vehicles with two different speeds were tested firstly. Then the measured signals were analyzed by means of the power spectrum method and the wavelet transform method, respectively. And the time- and frequency domain features of the signals were obtained subsequently. Finally, the influence of the speed and type of vehicle on the time- and frequency domain features of the signal and its propagation characteristic were investigated. The results show that, with the propagation of vehicle speed and distance, there is a similar varying law of the Seismic Signal for different vehicles. However, the difference of the time- and frequency domain feature between the two vehicles are striking.

CNN 기반 주행 중인 차량 간 상대속도 추정 알고리즘

In this paper, we proposed an estimation algorithm of the relative velocity between two driving vehicles based on CNN(Convolutional Neural Network), as the perception of the surrounding environment around an autonomous car, such as distance and speed of other vehicles, is important. The proposed algorithm estimated the velocity of a target vehicle by using a stereo camera without any other sensors. A stereo camera is a sensor that acquires simultaneously RGB images and depth maps, and it is widely used for autonomous vehicles to obtain various information. The developed CNN-based semantic segmentation model with high speed and accuracy was used to recognize the target vehicle, detecting the shape of the target object and omitting background information around it. Due to this effect, the distance between the autonomous vehicle and target vehicle is more accurately estimated. The relative speed between each vehicle was estimated using the distance and time difference between frames. The performance of the proposed algorithm was compared with conventional methods and it was confirmed that the speed of the target vehicle was more accurately estimated using only the stereo camera.

Experimental Analysis of Solar Assisted Refrigerating Electric Vehicle

Most refrigerating systems are driven by an internal combustion engine that increased the conventional vehicle’s oil consumption and tailpipe emissions. The solar-assisted refrigerating electric vehicle (SAREV) system powered by a hybrid energy mode has been designed. The hybrid energy (solar + grid) was stored in the battery bank to complete this vehicle’s necessary functions. The PV panels are prominently incorporated into this vehicle rooftop to charge the battery bank. In this study, the integrated system was driven by a hybrid energy mode that reducing the wastage and deterioration during temporary storage and transportation in different areas. The performance of the integrated system was tested under different operating conditions. The effect of load variation on maximum speed and travelling distance of vehicle was analyzed. The battery bank charging and discharge performance were studied with and without solar energy. The refrigerator was consuming 116 Wh energy per day to maintain a -12 oC lower temperature on the no-load condition at the higher thermostat position. The refrigerator was run continuously for 4-6 days on battery bank energy and 7-10 days on the full load condition of hybrid energy. The vehicle was travelling at a maximum of 23 km/h speed on full load condition. The vehicle needed torque 14-16 N-m at the initial phase for each load condition. Torque demand was decreasing with the increasing speed of the vehicle. The full-charged battery bank’s initial voltage was 51.04 V, and the cut-off voltage was 46.51 V. The vehicle was covering a distance of 62.4 km with the battery bank alone at full load condition. It was travelling 68.3 km distance with hybrid energy mode. The vehicle’s integrated system was the best in maintaining battery performance, power contribution capability, and drive range enhancement.

Overtake Behavior Model of Motorcycle on Accident Risk

The imbalance of transportation facilities and infrastructure, resulting in traffic jams and poor driver's aggressive behavior, especially in overtaking vehicles in front of them, cause an accident. The study's purpose is to analyze the behavior characteristics of overtaking motorcycle, test and analyze the relationship model between accident risk and motorcycle behavior and determine strategic step recommendations to reduce accidents risk to realize safety riding in Makassar City. Data collection methods used to achieve targets and study outcomes are observation, documentation, and literature review. Related statistical methods analyzed data. The variable explained is the motorcycle behavior related to driver ability to maintain a safe distance in the longitudinal and lateral directions. The results indicate a relationship between motorcycle rider behavior and accident frequency. Based on the longitudinal direction, it is known that the greater vehicle distance, the smaller accidents risk arising with a safe distance of 4.62 meters. Meanwhile, in the lateral direction, it is known that the greater distance between vehicles will increase accident risk. The safe distance between vehicles in the lateral direction is 1.88 meters.

Importance of Developing Photovoltaics-Powered Vehicles

The development of photovoltaics (PV)-powered vehicles are expected to contribute to reduce CO2 emission of vehicles and create a clean energy society. This paper presents the impact of high-efficiency solar cell modules on reduction in CO2 emission, charging cost reduction for electric vehicles, and reducing storage capacity of PV-powered electric vehicles. In this paper, the effects of solar cell module efficiency upon driving distance of PV-powered vehicles are also shown. Especially, the potential of Si tandem solar cells for PV-powered vehicle applications is discussed. This paper presents that the III-V/Si 3-junction solar cell modules with an efficiency of more than 37% have the potential of longer driving distance of 30 km/day average and more than 50 km/day on a clear day compared to an average 16 km/day driving by vehicles powered by 20% efficiency Si solar cell modules.

A comprehensive study of vehicle routing problem with time windows using an improved multi-objective evolutionary algorithms

The vehicle routing problem with time windows (VRPTW) which has both capacity and time constraints is an extension of vehicle routing problem (VRP). The problem is solved by optimizing routes for the vehicles so as to meet all given constraints as well as to minimize travelling distance and number of vehicles. This paper proposes to analyze a multi objective evolutionary algorithm (MOEA) that incorporates Various heuristics for local exploitation in the evolutionary search and the concept of pareto’s optimality to solve multi objective optimization in VRPTW. In this paper we model VRPTW as a modified version specialized for a multi objective context, using Evolutionary Algorithm to get a set of pareto optimal solutions considering three objectives, the number of vehicles, the total travel distance and the total delivery time at the same time. This new approach is validated with very good results and the comparison is performed on a standard benchmark problems showing that the algorithm outperforms highly competitive results compared with previously published studies.

Urban Logistics Delivery Route Planning Based on a Single Metro Line

This paper proposed a framework to integrate metro for urban logistics delivery in order to avert the conflicts with ground transportation, reduce delivery cost, transport distance and delivery time of vehicles. Integrating metro-based Underground Logistics System (ULS) with ground delivery vehicles (GDVs), an urban logistics delivery path optimization model based on a single metro line was built to improve delivery efficiency. In handling this model, total transport cost was taken as the objective function by considering the capacity limitations of GDVs and soft customer time window (CTW), vehicle routing problem with Time Windows (VRPTW) was also taken into account. By means of the saving algorithm (CW algorithm) and the improved Tabu Search algorithm (CWTS), our established model was solved to optimize the delivery routes under metro-based ULS. Through the case analysis of a Logistics Transport Enterprise (LTE) in Nanjing, it was shown that our proposed algorithm consistently outperformed Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) in taking less computation time and obtaining better results. Our proposed delivery mode also had a great advantage compared to the traditional truck delivery (TD) alone in terms of saving cost and guaranteeing the quality of service (QoS). The sensitivity analysis of the capacity offered an optimal GDV capacity setting to reduce total delivery cost.

Comparative Study of Markov Chain With Recurrent Neural Network for Short Term Velocity Prediction Implemented on an Embedded System

Short-term prediction models for an ego-vehicle's speed contributes to the improvement of vehicle safety, driveability, and fuel economy. To achieve these desired outcomes, an accurate forward speed prediction model and its successful implementation in a real system is a prerequisite. This paper compares six velocity prediction models based on two types of data-driven models, a Markov chain and a Recurrent Neural Network (RNN), by implementing them in an embedded system to evaluate their prediction accuracy and execution time. The inputs to each model are the driving information acquired on a specific route, such as internal vehicle information, relative speed and distance to the vehicle in the front of the ego-vehicle, and ego-vehicle's location estimated by the GPS signal along with the B-spline roadway model. The proposed prediction models predict the velocity profile of the ego-vehicle up to the prediction horizon of 150 m. The parameters of the proposed models have been optimized using Hyper-parameter Optimization via Radial basis function and Dynamic coordinate search. By applying real driving data, the Markov chain-based models show slightly lower prediction accuracy but shorter execution time than those of the RNN-based models.