Search Results

The steering system is a critical component for controlling a vehicle's direction. In the context of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, where drivers may not always be actively holding the steering wheel, early detection of precursor noise signals is essential to prevent serious accidents resulting from the loss of steering system functionality. It is therefore imperative to develop a device capable of early detection and notification of steering system malfunctions. Therefore, the current study aimed to quantify the noise levels generated within the Column-based Electric Power Steering (C-EPS) system of a D-segment sedan. To this end, we measured the uniaxial acceleration in nine noise-generating areas while simultaneously collecting data from three Controller Area Network (CAN) sources that are directly related to steering operation.

Dust testing of vehicles on unpaved roads is crucial in the development process for automotive manufacturers. These tests aim to ensure the functionality of locking systems in dusty conditions, minimize dust concentration inside the vehicle, and enhance customer comfort by preventing dust accumulation on the car body. Additionally, deposition on safety-critical parts, such as windshields and sensors, can pose threats to driver vision and autonomous driving capabilities. Currently, dust tests are primarily conducted experimentally at proving grounds. In order to gain early insights and reduce the need for costly physical tests, numerical simulations are becoming a promising alternative. Although simulations of vehicle contamination by dry dust have been studied in the past, they have often lacked detailed models for tire dust resuspension. In addition, few publications address the specifics of dust deposition on vehicles, especially in areas such as door gaps and locks.

The improvement of vehicle soiling behavior has increasing interest over the past few years not only to satisfy customer requirements and ensure a good visibility of the surrounding traffic but also for autonomous vehicles, for which soiling investigation and improvement are even more important due to the demands of the cleanliness and induced functionality of the corresponding sensors. The main task is the improvement of the soiling behavior, i.e., reduction or even prevention of soiling of specific surfaces, for example, windows, mirrors, and sensors. This is mostly done in late stages of vehicle development and performed by experiments, e.g., wind tunnel tests, which are supplemented by simulation at an early development stage. Among other sources, the foreign soiling on the side mirror and the side window depend on the droplet detaching from the side mirror housing.

Dry dust testing of vehicles on unpaved dust roads plays a crucial role in the development process of automotive manufacturers. One of the central aspects of the test procedure is ensuring the functionality of locking systems in the case of dust ingress and keeping the dust below a certain concentration level inside the vehicle. Another aspect is the customer comfort because of dust deposited on the surface of the car body. This also poses a safety risk to customers when the dust settles on safety-critical parts such as windshields and obstructs the driver’s view. Dust deposition on sensors is also safety critical and is becoming more important because of the increasing amount of sensors for autonomous driving. Nowadays, dust tests are conducted experimentally at dust proving grounds. To gain early insights and avoid costly physical testing, numerical simulations are considered a promising approach. Simulations of vehicle contamination by dry dust have been studied in the past.

‘Active safety systems’ are actively being developed to prevent collisions. The integration of ‘active safety systems’ and traditional ‘passive safety systems’ such as seatbelt and airbags is an important issue. The ‘Integrated safety’ performance is that comprehensively controls the performance of ‘active’ and ‘passive’ safety systems to reduce occupant injuries. To develop ‘integrated safety’ performance, it is important to develop crash scenarios for autonomous vehicles. This study is about the development of ‘Estimation Tool of Occupant Injury Risk’ for deriving risk integrated safety scenarios focused on occupant injury. The results of random traffic simulation using ‘Virtual Prototype’ were used to select parameters, and ‘MADYMO Equivalent Simplified Vehicle Crash Analysis Model’ was used to derive F-D characteristics for each vehicle collision condition.

Bad weather conditions such as torrential rain, heavy snow, and thick fog frequently occur worldwide. Vehicle accidents in such bad weather conditions account for a significant portion of all vehicle accidents, and the level of damage is relatively severe compared to other accidents that occur in clear weather. This paper analyzes the driver's driving stability in bad weather conditions, which has such a significant meaning, in various ways through experiments on the inconvenience experienced by the driver. In this study, three levels of bad weather conditions were implemented in a driving simulator environment to evaluate driver inconvenience for six activities. Through driving experiment, quantitative bio-signals and vehicle signals were analyzed in each weather condition. The SD survey was used to assess the driver's inconvenience level for activities performed while driving and analyze the ranking of inconvenience.

Expanding various future mobilities such as purpose built vehicle (PBV), urban air mobility (UAM), and robo-taxi, the application of autonomous driving system (ADS) technology is also spreading. The main point of ADS is to ensure safety by monitoring vehicle anomalies to prevent functional failure or accident. In this study, a model-based diagnosis and prognosis process was established using degradation data generated during autonomous driving simulation. A vehicle model was designed using Modelica/Dymola, and autonomous driving simulation was performed by integrating the lane keeping assistant (LKA) system with the vehicle model using Matlab/Simulink. Degradation data for the 3 components (a shock absorber damper, a suspension bush, and a tire) of the chassis system were input into the integrated simulation model. The degradation behavior was monitored with K-nearest neighbor (K-NN) and Gaussian mixture model (GMM).

As the AVN display in the car interior becomes larger and located above the center fascia, the driver's visual visibility is becoming important. In addition, since an expensive touch sensor is installed, a transparent electrode cost reduction technology for a display touch sensor that can replace an indium material, which is an expensive rare metal, is required. In this paper, we developed new transparent electrode materials and manufacturing methods for the touch sensor film which light reflectance is low and flexible without a separate low-reflection multi-layer, so that the design freedom is high and the material cost is low. By optimizing the amount of fluorine doping ratio in tin oxide, excellent electrical conductivity and high optical transmittance are secured, and the surface reflectance is reduced by adjusting the diameter and length of the silver nanowire. As a result, it was shown that the AVN display image and font readability was improved.

The investigation of vehicle soiling by improvement of vehicle parts to optimize the surrounding airflow is of great importance not only because of the visibility through windows and at mirrors but also the functionality of different types of sensors (camera, lidar, radars, etc.) for the driver assistance systems and especially for autonomous driving vehicles has to be guaranteed. These investigations and corresponding developments ideally take place in the early vehicle development process since later changes are difficult to apply in the vehicle production process for many reasons. Vehicle soiling is divided into foreign soiling and self-soiling with respect to the source of the soiling water, e.g., direct rain impact, swirled (dirty) water of other road users and own rotating wheels. The investigations of the soiling behavior of vehicles were performed experimentally in a wind tunnel and street tests.

With the spread of new trends such as autonomous driving and vehicle subscription service, drivers may pay less attention to the maintenance of the vehicle. Brake pads being safety critical components, the wear condition of all service brakes is required by regulation to be indicated by either acoustic of optical devices or a means of visually checking the degree of brake lining wear [1]. Current application of the wear indicator in the market uses either sound generating metal strip or wire harness based pad wear sensor. The former is not effective in generating clear alarm to the driver, and the latter is not cost effective, and there is a need for more effective and low cost solution. In this paper, a pad wear monitoring system using MOC(Motor On Caliper) EPB(Electric Parking Brake) ECU is proposed. An MOC EPB is equipped with a motor, geartrain and an ECU. The motor current when applying the parking brake is influenced by the mechanical load at the brake pad side of the system.

As autonomous driving vehicles are developed, automotive makers start focusing on implementing new door types, such as a falcon wing door or a B-pillarless dual sliding door, which could be one of the best-selling points. To make these doors electrically operate, applying advanced sensors like a RADAR or an Ultrasonic sensor is almost mandatory. Without these sensors, the door could be easily damaged or the customers could be seriously injured. Due to physical limitation, however, every sensor has a noise in nearby area and has a specification of the minimum detection range, which causes us not to be able to precisely detect the object in close area. If the controller cannot detect the precise distance of the object, the door could malfunction, since it could misidentify the obstacles. In this paper, we propose a method to reduce the minimum detection range by applying a prior estimation scheme.

In the path following problem, a commercial vehicle has a delay of a hydraulic steering actuator and slow steering response accordingly. In addition, there are disturbances due to the harsh driving conditions of commercial vehicles. These disturbances may include uncertainties about actuator dynamic delay, modeling error and steering angle sensor offset. Designing a lateral controller with good performance that can overcome this problem is the key to successfully carrying out autonomous driving of commercial vehicles. Usually, it is difficult to consider disturbances with uncertainties in the geometric based control methods. Therefore, this paper proposed a lateral controller using feedback augmented disturbance observer for the commercial vehicle. First, a dynamics was modeled which can describe delay of the hydraulic actuator of the commercial vehicle. After that, a lateral controller was designed based on this dynamics model.

This paper presents the research related to the self-driving system that has been actively carried out recently. Previous studies have been limited to ensure the path following performance in linear and steady state-alike handling region with small lateral acceleration. However, in the high speed driving, the vehicle cornering response is extended to nonlinear region where tire grips are saturated. This requires a technology to create the driving path for minimum time maneuvering while grasping the tire grip limits of the vehicle in real time. The entire controller consists of three stages-hierarchy: The target motion is determined in the supervisor phase, and the target force to follow the target behavior is calculated in the upper stage controller. Finally, the lower stage controller calculates the actuator phase control input corresponding to the target force.

This paper investigates the sensitivity of stiffness of front and rear suspension systems on the structure-borne road noise inside a vehicle cabin. A flexible multi-body dynamics based approach is used to simulate the structural dynamics of suspension systems including rubber bushings, suspension arms, a subframe and a twist beam. This approach can accurately predict the force transfer to the trimmed body at each suspension mounting point up to a frequency range of 0 to 300 Hz, which is validated against a force measurement test using a suspension test rig. Predicted forces at each mounting point are converted to road noise inside the cabin by multiplying it with experimentally obtained noise transfer functions. All of the suspension components are modeled as flexible bodies using Craig-Bampton component mode synthesis method.

This paper summarizes the development of a wireless message from infrastructure-to-vehicle (I2V) for safety applications based on Dedicated Short-Range Communications (DSRC) under a cooperative agreement between the Crash Avoidance Metrics Partners LLC (CAMP) and the Federal Highway Administration (FHWA). During the development of the Curve Speed Warning (CSW) and Reduced Speed Zone Warning with Lane Closure (RSZW/LC) safety applications [1], the Basic Information Message (BIM) was developed to wirelessly transmit infrastructure-centric information. The Traveler Information Message (TIM) structure, as described in the SAE J2735, provides a mechanism for the infrastructure to issue and display in-vehicle signage of various types of advisory and road sign information. This approach, though effective in communicating traffic advisories, is limited by the type of information that can be broadcast from infrastructures.

Adoption of high-pressure common-rail (HPCR) fuel systems, which subject diesel fuels to higher temperatures and pressures, has brought into question the veracity of ASTM International specifications for biodiesel and biodiesel blend oxidation stability, as well as the lack of any stability parameter for diesel fuel. A controlled experiment was developed to investigate the impact of a light-duty diesel HPCR fuel system on the stability of 20% biodiesel (B20) blends under conditions of intermittent use and long-term storage in a relatively hot and dry climate. B20 samples with Rancimat induction periods (IPs) near the current 6.0-hour minimum specification (6.5 hr) and roughly double the ASTM specification (13.5 hr) were prepared from a conventional diesel and a highly unsaturated biodiesel. Four 2011 model year Volkswagen Passats equipped with HPCR fuel injection systems were utilized: one on B0, two on B20-6.5 hr, and one on B20-13.5 hr.

New technologies such as multi-core and Ethernet provide vastly improved computing and communications capabilities. This sets the foundation for the implementation of new digital megatrends in almost all areas: driver assistance, vehicle dynamics, electrification, safety, connectivity, autonomous driving. The new challenge: We must share these computing and communication capacities among all vehicle functions and their software. For this step, we need a good resource planning to minimize the probability of late resource bottlenecks (e.g. overload, lack of real-time capability, quality loss). In this article, we summarize the status quo in the field of resource management and provide an outlook on the challenges ahead.

The purpose of this study is to develop a dynamic model that can accurately predict the motion of the door handle and counterweight during side impact crash tests. The door locking system, mainly composed of the door outside handle and door latch, is theoretically modeled, and it is assumed that the door outer panel can rotate and translate in all three directions during a side impact crash. Additionally, the numerical results are compared with real crash video footage, and satisfactory qualitative agreement is found. Finally, the simplified test rig that efficiently reflects the real crash test is introduced, and its operation is analyzed.

As automotive technology has been developed, gear whine has become a prominent contributor for cabin noise as the masking has been decreased. Whine is not the loudest source, but it is of high tonal noise which is often highly unpleasant. The gear noise originates at gear mesh. Transmission Error acts as an excitation source and these vibrations pass through gears, shafts and bearings to the housing which vibrates to produce noise on surrounding air. As microgeometry optimization target to reduce the fundamental excitation source of the noise, it has been favored method to tackle gear whine noise, especially for manual transmission. However, practicality of microgeometry optimization for the planetary gear system has been still in question, because of complex system structure and interaction among multi mesh gear sets make it hard to predict and even harder to improve. In this paper, successful case of whine noise improvement by microgeometry is presented.

This paper describes an automated path following system using vision sensor. Lateral control law for path following is especially underlined which is developed by using the backstepping control design methodology. To establish the proposed control system, the lateral offset to the reference path, the heading angle of vehicle relative to tangent line to the path, and path curvature are required. Those inputs to the controller have been calculated through Kalman filter which is frequently adopted for the purpose. The lane mark detection has been achieved in an ECU (Electric Control Unit) platform with vision sensor. The yaw rate and side-slip angle also needed in the controller are estimated by Kalman estimator. To show the performance of the proposed controller under different speeds, experiment has been conducted on a proving ground having straight and curve sections with the curvature of about 260m.