Vehicle Dynamics Data Research Articles

Method of Land Cover Change and Number of Vehicles Increasing on Land Surface Temperature: Case Research of Surabaya City, East Java Province, Indonesia

Urban Heat island phenomenon that occurs in some major cities to Indonesia can be caused by several factors like a construction of high building, land cover change and air pollution by motor vehicles. The phenomenon of small climate change in urban areas such as the city of Surabaya, the province of Jawa Timur can cause rising surface temperatures that can impact the decline in health and environmental degradation. This research was conducted to know the dynamics of land cover change, and dynamics of the vehicle to surface temperature in Surabaya city. Methods that are in use are a spatial analysis of multi-temporal imagery and analysis by using statistical data. The data used in surface temperature analysis and land cover analysis in the form of vegetation is Landsat-8 (OLI/TIRS) during the recording period from 2014 to 2019 and vehicle dynamics data. The results showed a change of land cover with an estimated area of 2989,8 ha, a rise in surface temperature with an estimate of 3.95 0C, and with the increase in a several of vehicles by 929.225 units from 2013 until 2018. Large surface temperature shows the areas with crowded activities such as settlements area, industrial zone, and government zone.

Integrating road traffic externalities through a sustainability indicator.

Road traffic poses negative externalities on society and represents a key challenge in sustainable transportation. However, the existing literature about the assessment of traffic externalities drawn on a common measure is scarce. This paper develops a sustainability indicator that integrates traffic-related externalities as means of traffic congestion, noise, greenhouse gases (GHG) and nitrogen oxides emissions, health impacts and road crash related costs, and adjusted to local contexts of vulnerability. Traffic, road crashes, acoustic and vehicle dynamic data were collected from one real-world intercity corridor pair comprising three alternative routes. The site-specific operations were characterized using a modeling platform of traffic, emissions, noise and air quality. A specific methodology is applied for each road traffic externality and translated in a single factor - external cost. The results indicated that road crashes presented the largest share in the partly rural/urban route while GHG emissions had the highest contribution in external costs for the highway routes. Also, the distribution of external cost component varied according to the type of road, mostly due to different levels of exposed inhabitants. This paper offers a line of research that produced a method for decision-makers with a reliable and flexible cost analysis aimed at reducing the negative impacts of road traffic. It also encourages the design of eco-traffic management policies considering the perspective of drivers, commuters and population.

A Driver's Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model.

Lane changing is considered as one of the most dangerous driving behaviors because drivers have to deal with the traffic conflicts on both the current and target lanes. This study aimed to propose a method of predicting the driving risks during the lane-changing process using drivers’ physiology measurement data and vehicle dynamic data. All the data used in the proposed model were obtained by portable sensors with the capability of recording data in the actual driving process. A hidden Markov model (HMM) was proposed to link driving risk with drivers’ physiology information and vehicle dynamic data. The two-factor indicators were established to evaluate the performances of eye movement, heart rate variability, and vehicle dynamic parameters on driving risk. The standard deviation of normal to normal R–R intervals of the heart rate (SDNN), fixation duration, saccade range, and average speed were then selected as the input of the HMM. The HMM was trained and tested using field-observed data collected in Xi’an City. The proposed model using the data from the physiology measurement sensor can identify dangerous driving state from normal driving state and predict the transition probability between these two states. The results match the perceptions of the tested drivers with an accuracy rate of 90.67%. The proposed model can be used to develop proactive crash prevention strategies.

CYCLING AT INTERSECTIONS: A MULTI-OBJECTIVE ASSESSMENT FOR TRAFFIC, EMISSIONS AND SAFETY

This paper evaluated in an integrated manner the traffic performance, pollutant emissions and road conflicts between bicycles and motor vehicles at a signalized intersection. Two alternative scenarios were examined: (1) bicycles increment and motor vehicles replacement within the cycle-fixed traffic signal; (2) replacing the existing traffic control by a conventional two-lane roundabout and evaluating the impacts of bicycles increment. For each scenario, bicycle demand was varied from 9 to 270 bicycles per hour (bph). Traffic flow and vehicle dynamic data were collected from a three-leg signalized intersection in Aveiro (Portugal). The microscopic traffic model (VISSIM) paired with an emission (Vehicle Specific Power – VSP) methodology and safety (Surrogate Safety Assessment Methodology – SSAM) model were used to assess intersection-specific operations. The fast Non-Dominated Sorting Genetic Algorithm (NSGA-II) was used to find the optimal bicycle demands. The results showed that two-lane roundabout outperformed the existing traffic control, namely in highest bicycle demand scenario (number of stops and travel time reduced in 78 and 14%, respectively; CO2, NOx, and HC decreased 9, 7, and 12%, respectively). It was also found that the number of conflicts was significantly reduced (–49%) with this latter layout even in maximum bicycle demand scenario (270 bph). However, roundabout layout lead to more severe conflicts and potential crashes. The analysis showed that bicycle demands of 75, 95 and 110 bph delivered good environmental and safety outcomes for the intersection.

Real-time detection of driver distraction: random projections for pseudo-inversion-based neural training

There is an accumulating evidence that distracted driving is a leading cause of vehicle crashes and accidents. In order to support safe driving, numerous methods of detecting distraction have been proposed, which are empirically focused on certain driving contexts and gaze behaviour. This paper aims at illustrating a method for the non-intrusive and real-time detection of visual distraction based on vehicle dynamics data and environmental data, without using eye-tracker information. Experiments are carried out in the context of the automotive domain of the European project Holides, which addresses development and qualification of adaptive cooperative human–machine systems, and is co-funded by ARTEMIS Joint Undertaking and Italian University, Educational and Research Department. The collected data are analysed by a single-layer feedforward neural network trained through pseudo-inversion methods, characterized by direct determination of output weights given randomly set input weights and biases. One main feature of our work is the convenient setting of input weights by the so-called sparse random projections: the presence of a great number of null elements in the involved matrices makes especially parsimonious the use at run time of the trained network. Moreover, we use a genetic approach to better explore the input weights network space. The obtained results show better performance with respect to classical pseudo-inversion methods and effective and parsimonious use of memory resources.

Evaluating individual risk proneness with vehicle dynamics and self-report data ˗ toward the efficient detection of At-risk drivers

Vehicle-dynamics data, now more readily available thanks to moderate-cost, embedded data logging solutions, have been used to study drivers' behavior (acceleration, braking, and yaw rate) through naturalistic driving research aimed at detecting critical safety events. In addition, self-reported measures have been developed to describe these events and to assess various individual risk factors such as sensation seeking, lack of experience, anger expression while driving, and sensitivity to distraction. In the present study, we apply both of these methods of gathering driving data in order to assess risk proneness as accurately as possible. Data were obtained from 131 drivers, who filled in an introductory questionnaire pertaining to their driving habits. Their vehicles were equipped with an external, automatic data-capture device for approximately two months. During that period, the participants reported critical safety events that occurred behind the wheel by (a) pressing a button connected to the device and (b) describing the events in logbooks. They also filled in weekly questionnaires, and at the end of the participation period, a final questionnaire with various self-reported measures pertaining to their driving activity. We processed the data by (a) performing a multiple correspondence analysis of the characteristics assessed via the automatic data capture and self-reports, and (b) categorizing the participants via hierarchical clustering of their coordinates on the dimensions obtained from the correspondence analysis. This allowed us to identify a group of drivers (n = 43) at risk, based on several self-reported measures, in particular, their recent crash involvement, and the frequency of critical acceleration/deceleration events as an objective measure. However, the at-risk drivers did not themselves report more critical safety events than the other two groups.

Research on a Conflict Early Warning System Based on the Active Safety Concept

In order to reduce traffic conflicts on cross-intensive roads, this paper proposes a new early warning system based on the active safety concept. The system collects real-time vehicle data using roadside sensors and transmits the results to drivers on the major road in a timely manner via roadside warning lights. In this research, the principles of the warning system are discussed in detail, including how the vehicle dynamics data are collected and how potential collisions are identified and avoided. Through a driving simulation experiment, the speed prediction model after implementation of the warning system was examined. Results indicated that it can accurately identify the vehicle operating status, accurately guide driving behavior, and effectively reduce traffic conflict. To verify the reliability of the proposed warning logic and algorithm, numerical simulations were carried out via CarSim/Simulink cosimulation. The simulation results indicate that the proposed system enables drivers to perceive conflicting vehicles in advance, avoid the sudden braking phenomenon, and ensure safe driving.

Real-time detection of drivers’ texting and eating behavior based on vehicle dynamics

With rapid advancement in cellphones and intelligent in-vehicle technologies along with driver’s inclination to multitasking, crashes due to distracted driving had become a growing safety concern in our road network. Some previous studies attempted to detect distracted driving behaviors in real-time to mitigate their adverse consequences. However, these studies mainly focused on detecting either visual or cognitive distractions only, while most of the real-life distracting tasks involve driver’s visual, cognitive, and physical workload, simultaneously. Additionally, previous studies frequently used eye, head, or face tracking data, although current vehicles are not commonly equipped with technologies to acquire such data. Also those data are comparatively difficult to acquire in real-time during traffic monitoring operations. To address the above issues, this study focused on developing algorithms for detecting distraction tasks that involve simultaneous visual, cognitive, and physical workload using only vehicle dynamics data. Specifically, algorithms were developed to detect driving behaviors under two distraction tasks – texting and eating. Experiment was designed to include the two distracted driving scenarios and a control with multiple runs for each. A medium fidelity driving simulator was used for acquiring vehicle dynamics data for each scenario and each run. Several data mining techniques were explored in this study to investigate their performance in detecting distraction. Among them, the performance of two linear (linear discriminant analysis and logistic regression) and two nonlinear models (support vector machines and random forests) is reported in this article. Random forests algorithms had the best performance, which detected texting and eating distraction with an accuracy of 85.38% and 81.26%, respectively. This study may provide useful guidance to successful development and implementation of distracted driver detection algorithms in connected vehicle environment, as well as to auto manufacturers interested in integrating distraction detection systems in their vehicles.

The Potential of Metering Roundabouts: Influence in Transportation Externalities

Roundabouts are increasingly being used on busy arterial streets for traffic calming purposes. However, if one roundabout leg is near a distribution hub, for example, parking areas of shopping centers, the entry traffic volumes will be particularly high in peak hours. This paper investigated a partial-metering-based strategy to reduce traffic-related costs in a corridor. Specifically, the resulting traffic performance, energy, environmental, and exposure impacts associated with access roundabouts were studied in an urban commercial area, namely: (a) to characterize corridor operations in terms of link-specific travel time, fuel consumption, carbon dioxide and nitrogen oxides emissions, and noise costs; (b) to propose an optimization model to minimize these outputs; and (c) to demonstrate the model applicability under different traffic demand and directional splits combinations. Traffic, noise, and vehicle dynamics data were collected from a corridor with roundabouts and signalized intersections near a commercial area of Guimarães, Portugal. Microscopic traffic and emission modeling platforms were used to model traffic operations and estimate pollutant emissions, respectively. Traffic noise was estimated with a semi-dynamical model. Link-based cost functions were developed based on the integrated modeling structure. Lastly, a sequential quadratic programming-type approach was applied to find optimal timing settings. The benefit of the partial-metering system, in terms of costs, could be up to 13% with observed traffic volumes. The efficiency of the proposed system increased as entering traffic at the metered approaches increased (~7% less costs). The findings enable quantification of metering benefits near shopping areas.

Multi-Criteria Assessment of Crosswalk Location on a Corridor with Roundabouts: Incorporating a Noise Related Criterion

Abstract Traffic noise is an important source of environmental stress that can damage human health. This phenomenon may be sensitive nearby roundabouts where noise levels may exceed exposure limits. However, the quantification about noise perceived by pedestrians at influence areas of roundabouts is lacking. This research assessed the characteristics of noise along an urban corridor with two roundabouts. A deeper understanding about the exposure to noise levels perceived by pedestrians is a contribution of the paper. The specific objectives are: 1) to characterize corridor-specific operations in terms of traffic and pedestrian performance, carbon dioxide and nitrogen oxide emissions, and noise; and 2) to explore the differences in the optimal crosswalk locations considering above-related criteria. Traffic and pedestrian volumes, vehicle dynamic and noise data were collected during morning and evening peak periods in the selected site. Traffic and pedestrian performance, and vehicular emissions were evaluated using VISSIM traffic model and Vehicle Specific Power (VSP), respectively. Traffic noise was estimated with a semi-dynamical model and an estimation of the corridor Source Power Level (SPL) was used as a distance-free parameter to be compared with other emissions. Finally, a Genetic Algorithm (GA) was applied to find optimal crosswalk locations. It was found that crosswalks nearby the present location (PC = 33 m) provided a good balance among traffic and pedestrian performance, emissions and noise, regardless of peak period. The inclusion of noise related-criteria resulted in some optimal locations next roundabout exit section (13-23 m) mostly due to low pedestrian volumes in the study locations.

Insights into vehicle trajectories at the handling limits: analysing open data from race car drivers

ABSTRACTRace car drivers can offer insights into vehicle control during extreme manoeuvres; however, little data from race teams is publicly available for analysis. The Revs Program at Stanford has built a collection of vehicle dynamics data acquired from vintage race cars during live racing events with the intent of making this database publicly available for future analysis. This paper discusses the data acquisition, post-processing, and storage methods used to generate the database. An analysis of available data quantifies the repeatability of professional race car driver performance by examining the statistical dispersion of their driven paths. Certain map features, such as sections with high path curvature, consistently corresponded to local minima in path dispersion, quantifying the qualitative concept that drivers anchor their racing lines at specific locations around the track. A case study explores how two professional drivers employ distinct driving styles to achieve similar lap times, supporting the idea that driving at the limits allows a family of solutions in terms of paths and speed that can be adapted based on specific spatial, temporal, or other constraints and objectives.

Exploring the association of rear-end crash propensity and micro-scale driver behavior

The relationship between driver behavior at the tactical level and crash experience is a long sought association that has been elusive to explore. The availability of in-vehicle sensing devices capable of capturing and documenting micro-scale dynamic driver behavior offers the opportunity to begin such an exploration. This study integrates rear-end crash data experienced on a 63-mile section of I-40 in North Carolina over a four-year period with three months of micro-scale driving behavioral data gathered by an in-vehicle sensing system (i2D) that records and dispatches second by second vehicle dynamics data to a central database. The information collected by the i2D devices came from a fleet of about 20 vehicles driven by volunteers in their naturalistic driving environment. Additionally all crash and driver data were geo-located onto a link-based GIS environment. The objective of this study is to explore the association of crash propensity and micro-scale driving behavior. The initial findings of this research are promising. First, over 85% of all rear-end crashes occurred on 30 segments extending from 2000 feet upstream of an on-ramp to the on-ramp itself. Secondly, on those segments with high crash rates we have detected a high propensity of drivers to decelerate at high rates (4m/s2 or more). We have also tested and confirmed that the sharp deceleration phenomenon is not confined to a few drivers, but appears to be common for the high-crash segments, using trip-based analyses.

Real-Time Detection System of Driver Distraction Using Machine Learning

There is accumulating evidence that driver distraction is a leading cause of vehicle crashes and incidents. In particular, increased use of so-called in-vehicle information systems (IVIS) and partially autonomous driving assistance systems (PADAS) have raised important and growing safety concerns. Thus, detecting the driver's state is of paramount importance, to adapt IVIS and PADAS accordingly, therefore avoiding or mitigating their possible negative effects. The purpose of this paper is to show a method for the nonintrusive and real-time detection of visual distraction, using vehicle dynamics data and without using the eye-tracker data as inputs to classifiers. Specifically, we present and compare different models that are based on well-known machine learning (ML) methods. Data for training the models were collected using a static driving simulator, with real human subjects performing a specific secondary task [i.e., a surrogate visual research task (SURT)] while driving. Different training methods, model characteristics, and feature selection criteria have been compared. Based on our results, using a support vector machine (SVM) has outperformed all the other ML methods, providing the highest classification rate for most of the subjects. Potential applications of this paper include the design of an adaptive IVIS and of a “smarter” PADAS.

FAST INSPECTION AND IDENTIFICATION TECHNIQUES FOR TRACK IRREGULARITIES BASED ON HHT ANALYSIS

The requirements for inspection and management of track irregularity have to be more rigorous due to safety and serviceability concerns as the increment of the train speed of high-speed train. The wavelength of track irregularity significantly affects running safety and riding comfort. To improve inspection, operation, efficiency, and address, the inability of the mid-chord offset method currently used on track inspection cars to identify some irregularity wavelengths, a measurement and assessment method is presented in this paper. Nonstationary and nonlinear vehicle dynamic response data caused by irregularities were measured by accelerometers mounted on the axle-box of a vehicle. The Hilbert–Huang transform (HHT) technique was applied to analyze the data. Components of specific irregularity wavelength could be effectively identified and accurately matched to the data measured by the track inspection car. Additionally, to obtain the complete waveform of the track irregularity, we propose a restoration method of track irregularity that combines the characteristics of the measurement magnification in the chord-based measuring system and the HHT signal-processing techniques.

Detailed FOT for the Analysis of Effects between Nomadic Devices and ADAS

In the last years the market penetration of nomadic devices has increased significantly. Although these devices aim to support drivers in different levels of the driving task the impacts of these devices have not yet been assessed in detail.The TeleFOT project is a large scale collaborative project under the 7th FP, co-funded by the European Commission. The objective of this project is to test the impacts of driver support functions in real-life driving conditions.There will be up to 3000 drivers involved in large scale or detailed FOTs, which take place at 8 different test sites in Europe. One detailed FOT is undertaken in Germany with the focus on the effects of the combination of nomadic devices and ADAS.This paper will present the content and the implementation of the German detailed FOT. Within the German FOT a demonstrator vehicle with adaptive cruise control, lane keeping assist and forward collision warning is set up and additionally equipped with cameras, eye tracking software and D-GPS. Based on these sensors and vehicle dynamic data driver behaviour and vehicle movement can be recorded and analysed. A nomadic device is implemented into the test vehicle with the functions: static navigation, speed limit information and speed alert.A first set of data is available and presented in this paper. The driving behaviour regarding the velocity distribution of different equipped test drives are shown exemplarily in the situation of a speed limit transition on the motorway. Furthermore the boundary conditions, which have to be taken into account, are provided in this paper.

Feat Of Submerged Scheme Relevance In Power Pc Processor Based Fpga Using Fpga Ip Cores

Under water systems use processor based rooted systems to provide control and guidance to the under water vehicles. They obtain target and vehicle dynamics data from sensors and gyros, and process this data as per control and guidance algorithms to generate control and guidance parameters to the actuation system. Traditionally x86 families are being used in these systems in amassing to memory, I/Os and other peripherals being on the card. The recent developments in FPGA (Field Programmable Gate Array) technology has made pavement to use superior FPGAs with IP cores to develop under water systems. The modern FPGA devices include 32-bit Power PC processor, memory blocks and programmable area to comprise peripheral blocks. Under water systems developed out of the FPGA cores are definitely have several advantages like, saving the card size (FPGA accommodates several of the components in addition to the processor), flexibility to adopt changes in design (as FPGA can be programmed by the end user), preventing obsolescence of components. Building an under water systems based on FPGA IP cores is an innovative and hottest technological demonstration with several advantages to prophesy. The present work describes the dwindling in power consumption and size of the Under Water System. This work will also be productive to CSS Division of NSTL in designing and miniaturizing embedded systems such as MCS, MDAC etc. used in marine systems. The present work describes the mellowness of under water system relevance in Power PC Based FPGA using FPGA IP Cores. This work includes understanding the design flow of EDK and learns about various IP Cores Provided by Xilinx EDK 10.1. The underwater system application has been implemented in ‘C’ language by using Xilinx Device Drivers. A custom logic in VHDL has been developed for truncating extra bits of ADC. In Xilinx ISE10.1 project navigator the developed VHDL Code has been integrated with C. The combined bit file generated has been downloaded into Xilinx Virtex-II Pro FPGA Proto Board.

Performance Evaluation of Automotive Radars Using Carrier-Phase Differential GPS

We describe a centimeter-level ground-truth system based on GPS relative positioning. One of the goals of the system is for performance evaluation of automotive radars by creating objective test procedures for the radars. In support of this goal, an effective scheme is used to handle signal attenuation or blockage that leads to the changes of satellite visible map and cycle slip. Multiplatform GPS measurements and low-cost vehicle dynamic sensor data are integrated for vehicle trajectory reconstruction. Experimental results are presented to illustrate the system's actual centimeter-level accuracy and superior performance over Kalman smoothing. The effectiveness of the proposed ground-truth system is demonstrated by evaluating mono-pulse short-range radars whose accuracy is about submeter level.

A Situation-Adaptive Lane-Keeping Support System: Overview of the SAFELANE Approach

Going beyond standard lane-departure-avoidance systems, this paper addresses the development of a system that is able to deal with a large set of different traffic situations. Its foundation lies on a thoroughly constituted environment detection through which a decision system is built. From the output of the decision module, the driver is warned or corrected through suited actuators that are coupled to control strategies. The input to the system comes from cameras, which are supplemented by active sensors (such as radar and laser scanners) and vehicle dynamic data, digital road maps, and precise vehicle-positioning data. In this paper, the presented system design is divided into three layers: the perception layer, which is responsible for the environment perception, and the decision and action layers, which are responsible for evaluating and executing actions, respectively.

Analysis of Real-World Lead Vehicle Operation for Modal Emissions and Traffic Simulation Models

New models and data are needed in microscopic traffic simulation tools to allow effective use with newer modal tailpipe emissions models. Traffic simulation models offer the ability to simulate large, second-by-second vehicle operation data sets as input for emissions models. However, more data are needed to improve simulation of second-by-second vehicle speed. This research analyzes and models the vehicle dynamics of unconstrained drivers in real-world driving situations based on road geometry. Vehicle dynamics data were collected by using an instrumented vehicle driven by 22 volunteers over a 17-mi predetermined test route. The objective of this research was to analyze and model the nonrandom speed variations in unconstrained lead drivers. The results of this study suggest that horizontal and vertical curvatures have a significant impact on the second-by-second operation of an unconstrained lead vehicle. Furthermore, these nonrandom changes in speed are important considerations since they can produce considerable variations in the level of tailpipe emissions.

Lateral load transfer and normal forces estimation for vehicle safety: experimental test

Knowledge of vehicle dynamics data is important for vehicle control systems that aim to enhance vehicle handling and passenger safety. This study introduces observers that estimate lateral load transfer and wheel–ground contact normal forces, commonly known as vertical forces. The proposed method is based on the dynamic response of a vehicle instrumented with cheap and currently available standard sensors. The estimation process is separated into three blocks: the first block serves to identify the vehicle’s mass, the second block contains a linear observer whose main role is to estimate the roll angle and the one-side lateral transfer load, while in the third block we compare linear and nonlinear models for the estimation of four wheel vertical forces. The different observers are based on a prediction/estimation filter. The performance of this concept is tested and compared with real experimental data acquired using the INRETS-MA (Institut National de Recherche sur les Transports et leur Sécurité – Département Mécanismes d’Accidents) Laboratory car. Experimental results demonstrate the ability of this approach to provide accurate estimation, thus showing its potential as a practical low-cost solution for calculating normal forces.