Measurements Of Vehicle Dynamics Research Articles

Periodical Vehicle Inspections with Smart Technology

This paper presents the major outcomes of the utilization of smart phone applications in the periodical technical inspection process. Road safety is one of the main topics in transportation. Technical defects of vehicles can play an important role in fatal accidents; therefore, periodical vehicle inspection is a common practice amongst many countries across all continents. The authors observed that advancements in smart technologies presented an opportunity to enhance the efficiency and effectiveness of vehicle inspections. They specifically focused on the Slovak Republic and described the transition of technical inspections towards smart technologies in this country. The goal was to utilize new technologies and improvements without increasing costs dramatically. The paper discusses the outcomes of the first two years of using this app at every technical inspection station in the Slovak Republic. The smart phones were used not only to help with process management but the novelty is that they also collected data from the sensors and used their own sensors for the vehicle dynamics measurements. One of the important factors is the successful implementation of low cost devices at such a large scale, country wise.

Monitoring of lateral and cross level track geometry irregularities through onboard vehicle dynamics measurements using machine learning classification algorithms

In recent years, significant studies have focused on monitoring the track geometry irregularities through measurements of vehicle dynamics acquired onboard. Most of these studies analyse the vertical irregularity and the vertical vehicle dynamics since the lateral direction is much more challenging due to the non-linearities caused by the contact between the wheels and the rails. In the present work, a machine learning-based fault classifier for the condition monitoring of track irregularities in the lateral direction is proposed. The classifiers are trained with a dataset composed of numerical simulation results and validated with a dataset of measurements acquired by a diagnostic vehicle on the straight track sections of a high-speed line (300 km/h). Classifiers based on decision tree, linear and Gaussian support vector machine algorithms are developed and compared in terms of performance: good results are achieved with the three algorithms, especially with the Gaussian support vector machine. Even though classifiers are data driven, they retain the essence of lateral dynamics.

Estimating Vertical Profile Irregularities From Vehicle Dynamics Measurements

Track geometry is an important parameter, used by railways, for routine track maintenance. Laser based Track Recording Coaches (LTRCs) are widely availed by railways, for recording track geometry defects, such as: vertical profile, alignment, gauge etc. In this paper, a novel method for monitoring vertical profile irregularities by in-service rail vehicle dynamics measurements has been proposed. The procedure takes as input, data from bogie installed inertial measurement unit (IMU) measuring vertical acceleration and pitch-rate, and encoder measuring longitudinal velocity. Extended Kalman Filter (EKF) with Rauch-Tung-Striebel (RTS) smoothing and Extended Kalman Particle Filter (EPF) are reviewed, for detecting vertical profile irregularities. Process model of nonlinear state estimation filters, EKF and EPF, has been developed by applying a novel analytical technique that approximates osculating circle of a point on the vertical profile curve. Further, measurement model has been devised by a novel procedure estimating curvatures from circles approximating trajectory sensed by vertical acceleration and pitch-rate curvature. Proposed method has been field-tested by comparing its output with LTRC’s recorded data.

Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges

Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context, as well as the driver status since ADAS share the vehicle control authorities with the human driver. This paper provides an overview of the ego-vehicle driver intention inference (DII), which mainly focuses on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consist of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.

Estimation of lateral and cross alignment in a railway track based on vehicle dynamics measurements

Abstract Monitoring the condition of the infrastructure based on measurements coming from in-service trains is a recent trend in the policies of railway infrastructure managers and is seen as a major factor to reduce maintenance costs retaining the present levels of passengers’ ride comfort and availability of the lines. Aim of this paper is to develop and assess model-based methods for the identification of geometric track irregularities from acceleration measurements taken on-board vehicles travelling on the track. In particular, the paper focusses on the identification of lateral alignment irregularities, a yet unsolved problem which turns out to be much more demanding compared to the identification of longitudinal level irregularities due to the relative wheel-rail motion in lateral direction which occurs on account of the peculiar geometry of wheel-rail contact. The paper presents three different approaches to the estimation of the lateral and cross-level track irregularities, one defined in the frequency domain and two in the time domain. For each method, an assessment is performed based on numerical experiments in which virtual measurements from vehicle-mounted sensors are obtained by means of numerical simulations performed using a multi-body model of a railway vehicle. For the method defined in the frequency domain, a first application to line measurements is presented.

Experimental Investigation on Condition Monitoring Opportunities of Tramway Tracks

The increasing demands for guided transportation modes in urban areas generate the need of high-frequency services. Due to the frequent services, the track deterioration process will be accelerated. Therefore, the exact knowledge of track quality is highly important for every railway company to provide high quality service level.For monitoring of tramway tracks, an unconventional vehicle dynamics measurement setup is developed, which records the data of 3-axes wireless accelerometers mounted on wheel discs of regular in-service tram. In the implementation of prototype system, the bogie side-frame and car body mounted sensors are also fitted to the instrumented vehicle to compare the efficiency of these conventional solutions with the developed arrangement. At the first test period, the instrumented vehicle works as a dedicated inspection vehicle, in order to keep the constant velocity and help to determine the influencing factors on results. Accelerations are processed to obtain the track irregularities, in order to determine whether the track needs to be repaired. Real data come from measurements taken on tram line 49 of the Budapest (Hungary) and they have been validated by comparing results to the actual state of the track provided by a track geometry monitoring trolley and visual inspection. This paper presents the developed methods used for validation and the analysis of preliminary results of the wheel discs mounted accelerometers. This vehicle dynamic measurement system is cheap to implement and no significant modification of the vehicle is required. Therefore, in-service vehicles equipped with this system may serve a good opportunity for monitoring tramway track, while it multiple passes over same track section.

Development and Implementation of Conflict-Based Assessment of Pedestrian Safety to Evaluate Accessibility of Complex Intersections

This paper develops and implements the Conflict-based Assessment of Pedestrian Safety (CAPS) methodology for evaluating pedestrian accessibility at complex intersections. In past years, a significant research has been done on pedestrian access to modern roundabouts and other complex intersection forms, including a significant focus on the accessibility for pedestrians who are blind. A majority of these studies have relied on actual street crossings by study participants under supervision of trained Orientation and Mobility (O&M) Specialist. These crossing studies were used to evaluate risk from a measurement of intervention events, where the O&M specialist had to physically stop the participant from crossing. While providing arguably the most accurate data for the crossing risk at a particular intersection, actual street crossings can be dangerous to the study participants, and are further very time consuming and expensive to conduct. The CAPS method presented in this paper emphasizes the use of conflict-based safety factors to quantify risk. The CAPS method relates pedestrian crossing decisions to advanced measurements of vehicle dynamics to estimate lane-by-lane conflicts. CAPS identifies the grade of conflict based on a score generated on a five-criterion rating scale. Each of these criteria or factors has different severity levels, and when combined, provides an overall risk rating of the crossing decision. The CAPS framework was applied to a study of blind pedestrian crossings at a multi-lane roundabout. The resulting risk scores were calibrated from actual O&M interventions observed during the study to give confidence in the CAPS performance. The calibrated CAPS framework correctly matched all (high risk) O&M intervention events, and further identifies other (lower risk) pedestrian-vehicle conflicts. The resulting method has the potential to allow for a faster and most importantly safer evaluation of complex intersections for pedestrian access. Since all factors are measured prior to the pedestrian stepping into the roadway, this approach is compatible with crossing indicator studies, where the participants merely indicate when they would cross, rather than actually stepping into the roadway. The CAPS framework therefore allows for a more objective and consistent safety assessment of pedestrian crossings in a research context without having pedestrians physically step into the roadway.

New Regressors for the Direct Identification of Tire Deformation in Road Vehicles Via “In-Tire” Accelerometers

The interaction between the tire and the road is crucial for determining the dynamic behavior of a road vehicle, and the road-tire contact forces are key variables in the design of traction, braking, and stability control systems. Traditionally, road-tire contact forces are indirectly estimated from vehicle-dynamics measurements (chassis accelerations, yaw-roll rates, suspension deflections, etc.). The emerging of the ldquosmart-tirerdquo concept (tire with embedded sensors and digital-computing capability) has made possible, in principle, a more direct estimation of contact forces. In this field - still in its infancy - the main open problems are the choice of the sensor(s) and the choice of the regressor(s) to be used for force estimation. The objective of this work is to present a new sensor-regressor choice, and to provide some preliminary experimental results, which confirm the validity of this choice. The idea is to use a wheel encoder and an accelerometer mounted directly in the tire. The measurement of the in-tire acceleration is transmitted through a wireless channel. The key innovative concept is to use the phase shift between the wheel encoder and the pulse-like signals provided by the accelerometer as the main regressor for force estimation.

Vehicle dynamics measurements

The modern vehicle development process relies evermore on the support of extensive subjective evaluation by objective vehicle dynamics measurements. Performing open-loop manoeuvres in conjunction with a steering robot greatly increases the reproducibility and eliminates influences caused by the driver. The obtained time data require considerable processing, in order to generate meaningful parameters, e.g. such as those defined in the ISO standards. The occurrence of time delay in the chain of measurements signals requires consideration in particular. Furthermore, the measured quantities must relate to a vehicle reference point. In the combined work of the BMW Group and the University of Bath, evaluation routines were developed, which significantly reduced the time spent on data processing by simultaneous improvement in the data quality. By employing these procedures, vehicle dynamics measurements were produced in a magnitude and to a quality previously unseen in the development process.

Estimation of vehicle side slip angle with artificial neural network

The side slip angle provides a key state value that defines the yawing and lateral motion of a vehicle. Accordingly, would it be possible to detect or estimate the side slip angle of a real vehicle with higher precision, the vehicle could be properly controlled with a more ideal state feedback control. There are two methods of measuring side slip angles. One is a direct method that measures the longitudinal and lateral velocity respectively, using optical speed sensors. The other is an indirect method that integrates the difference between the lateral acceleration/vehicle speed and the yaw velocity. These methods have been generally used for measurements of vehicle dynamics. Although, the only representative method for estimation would be the application of a linear vehicle model, like an observer, precision and reliability are not sufficient in various conditions of production cars. The objective of this paper is to estimate side slip angles with higher precision using an artificial neural network, without using optical speed sensors or an integral calculation method. The description is devoted to a settlement of basic system performance using simulation and to a verification of test results using actual vehicles.