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The aim of this work is to apply an innovative adaptive ℒ1 techniques to control flutter phenomena affecting highly flexible wings and to evaluate the efficiency of this control algorithm and architecture by performing the following tasks: i) adaptation and analysis of an existing simplified nonlinear plunging/pitching 2D aeroelastic model accounting for structural nonlinearities and a quasi-steady aerodynamics capable of describing flutter and post-flutter limit cycle oscillations, ii) implement the ℒ1 adaptive control on the developed aeroelastic system to perform initial control testing and evaluate the sensitivity to system parameters, and iii) perform model validation and calibration by comparing the performance of the proposed control strategy with an adaptive back-stepping algorithm. The effectiveness and robustness of the ℒ1 adaptive control in flutter and post-flutter suppression is demonstrated.

Many accidents occur today when distant objects or roadway impediments are not quickly detected. To help avoid these accidents, longer-range safety systems are needed with real-time detection capability and without requiring a line-of-sight (LOS) view by the driver or sensor. Early detection at intersections is required for obstacle location around blind corners and dynamic awareness of approaching vehicles on intersecting roadways. Many of today's vehicular safety systems require short LOS distances to be effective. Such systems include forward collision warning, adaptive cruise control, and lane keeping assistance. To operate over longer LOS distances and in Non-LOS (NLOS) conditions, cooperative wireless communications systems are being considered. This paper describes field results for LOS and NLOS radio links for one candidate wireless system: 5.9GHz Dedicated Short Range Communications (DSRC).

In general for Vehicle-to-Vehicle (V2V) communication, message authentication is performed on every received wireless message by conducting verification for a valid signature, and only messages that have been successfully verified are processed further. In V2V safety communication, there are a large number of vehicles and each vehicle transmits safety messages frequently; therefore the number of received messages per second would be large. Thus authentication of each and every received message, for example based on the IEEE 1609.2 standard, is computationally very expensive and can only be carried out with expensive dedicated cryptographic hardware. An interesting observation is that most of these routine safety messages do not result in driver warnings or control actions since we expect that the safety system would be designed to provide warnings or control actions only when the threat of collision is high.

”U” bolts are fixing elements and they are used to clamp an elastic joint. From the past, they still looking as an old design and unfortunately, suspension engineers are not specialists in fasteners and elastic joints. That is why we will show important assumptions and concepts to design and specifications this clamp element “U” bolt and its influence over leaf-springs. Currently, “U” bolt is used to clamp an elastic or elastic-plastic joint of heavy duty suspension, formed by leaf-spring, axle, spring pad, “U” bolt plate. This kind of suspension is typically used to trucks, buses and trailers. We are wondering, which one important assumption that an engineer must be careful when designs a new suspension changing from old designs to an updated technology. We provide a theoretical analysis and a FEA analysis to compare torque efficacy x leaf-spring reactions and what are effects this relationship can cause in a suspension.

The term triple safe is used to identify an air brake system with three separate circuits, two of which are service brake systems of virtually equal effectiveness; the third is a spring parking brake system. The paper reviews the spring parking brake chamber capability and describes a triple-safe air brake system and chambers that have similar service and emergency capacities, and parking capabilities, to meet future requirements of trucks and buses.

In the modern and fast growing automotive sector, reliability & durability are two terms of utmost importance along with weight & cost optimization. Therefore it is important to explore new technology which has less weight, low manufacturing cost and better strength. The new technology developed always seek for a quick, cost effective and reliable methodology for its design validation so that any modification can be made by identifying the failures. This paper presents the rig level test methodology to validate and to correlate the CAE derived strain levels, life cycle & failure mode of newly developed light weight stabilizer link for EV Bus suspension

An unique bonding mechanism was studied after several instances, where the linings stuck to the brake drums on transit buses, were reported. Evidences suggested that the linings were “glued” to the brake drums surface after wear debris (dust) was turned into “adhesive paste” through complicated thermal and chemical changes. Factors such as the friction materials, environment and service conditions, which could activate and deactivate the lining bonding, were observed and discussed. The prevention measures are proposed.

IN this paper the authors present some experimental results obtained by using the analysis outlined by Prof. James J. Guest before the Institution of Automobile Engineers, in 1926. To make the experimental work more understandable, they present the essential points of Professor Guest's analysis. Professor Guest begins his analysis of the movements of a car body with the simplest set of conditions and presents a graphical as well as an algebraic solution. He then includes one additional factor after another in his analysis until the principal factors in car suspension are included. After all factors are considered, the essential structure of the simple analysis is retained. The authors' efforts at the experimental determination of the moment of inertia of passenger cars were started in January, 1932, on Sir Charles Dennistoun Burney's “tear-drop” design with which he visited leading American manufacturers.

The SHIFT-MATE is a dashboard mounted computer based device that cues a truck driver to shift more efficiently. Through electronic circuitry, key vehicle parameters are monitored, computed, then via graphic display, instructs the driver when to shift for improved fuel economy. The theory of operation is described in the text.

There are many different vibration sources in a car. Engine, gears, road roughness, impacts against the wheels cause vibration and sound that can decrease the parts and the car durability as well as affect drivability, safety and passengers and community comfort. In 4×4 cars, some extra vibration sources are the parts responsible for transmitting the torque and power to the rear wheels. Each of them has their own vibration modes, excited mostly by its imbalance or by the second order engine vibration. The engine vibration is a very well known phenomena and the rear driveshaft is designed not to have any vibration mode in the range of frequencies that the engine works or its second order. The imbalance of a driveshaft is also a design requirement. That means, the acceptable imbalance of the driveshaft is limited to a maximum value.

In automotive electronics on-board diagnostics does the fault diagnosis and reporting. It provides the level of robustness required for the control electronics against various faults. The amount of diagnostic information available via on board diagnostics are depends on the type of vehicle. Pre-supply fuel pump is the component in the common rail hydraulic system. It pumps the fuel from the fuel tank to the inlet valve of the high pressure fuel pump. Electronic control unit synchronizes its operation with high pressure fuel pump. A dedicated driver module in the ECU controls the operation of pre-supply fuel pump. The driver module consist of an ASIC with internal voltage, current monitoring modules for the fault diagnosis and the pre-drivers to control external HS and LS power stages. The software part of the OBD programmed in the internal memory of the ASIC. The “Rds_on” of the power MOSFETs are used for the fault detection purpose.

The simulation system “Roadrunner” has the ability to compute 3-dimensional vehicle behavior during simulated ABS-stops or Traction Control (TC) operation on a PC based test rig system. This can be done in real-time mode with fast DSP (digital signal processor) boards and an ABS/TC-ECU as a ‘Hardware-in-the-loop’ device (HIL) or off-line (non-real-time), on the PC only. In the off-line-mode, the PC additionally performs the ABS/TC control algorithms. The simulated system includes model equations for chassis and wheel movement, hydraulics and road to tire interactions. A driver model enables the reproduction of standard steering maneuvers.

A large number of testing procedures have been developed to ensure vehicle safety in common and extreme driving situations. However, these conventional testing procedures are insufficient for testing autonomous vehicles. They have to handle unexpected scenarios with the same or less risk a human driver would take. Currently, safety related systems are not adequately tested, e.g. in collision avoidance scenarios with pedestrians. Examples are the change of pedestrian behaviour caused by interaction, environmental influences and personal aspects, which cannot be tested in real environments. It is proposed to use augmented reality techniques. This method can be seen as a new (Augmented) Pedestrian in the Loop testing procedure.

A multi vector design tool to accurately predict instrument panel obscuration was developed to insure that critical legal displays in vehicles are not obscured. The concept provides for a computer generated light source shaped to replicate the human eyes. The light source is then projected onto a 3D math based arrangement and the resultant shadows are visible on the instrument panel surface and its displays. Design studios require criteria for the placement of the instrument cluster gages and displays, various controls, switches, and steering column stalks before an interior theme can be completed. Therefore, instrument panel obscuration and visibility must be determined early in the design process. The obscured areas are a function of the instrument panel surface, steering wheel rim, hub, spokes, and the location of the driver's eyes. This light source method allows engineers and designers the ability to quickly determine obscured areas.

Vehicle dynamics is a discipline of mechanical engineering that benefited of significant improvements thanks to the progress of computational engineering. Vehicle dynamics engineers are using CAE for the development of a vehicle with MBS and FEA. The concurrent use of these two technologies is a standard in the automotive industry. However the current simulation process is not fully efficient because local geometrical and material nonlinearities are not accurately modeled in classical MBS software. This paper introduces a methodology for vehicle dynamics simulation integrating MBS capabilities in one single nonlinear FEA environment enabling an accurate modeling of nonlinearity in vehicles.

The migration of automotive companies to model based engineering for the software development of electronic control units, ECU’s, has been driven largely by three potential benefits; cycle time reduction, quality improvement, and defect reduction. Through an examination of the process and methodology used during an actual project with an automotive OEM, this paper shall discuss some of the lessons learned during the project implementation phase. It will also explore how the deployment of an effective model based engineering process framework, process components, and methodology can assist an organization in realizing the documented benefits of model based engineering.

In recent years there has been increasing interest in quantifying the emissions from aircraft in order to generate inventories of emissions for climate models, technology and scenario studies, and inventories of emissions for airline fleets typically presented in environmental reports. The preferred method for calculating aircraft engine emissions of NOx, HC, and CO is the proprietary “P3T3” method. This method relies on proprietary airplane and engine performance models along with proprietary engine emissions characterizations. In response and in order to provide a transparent method for calculating aircraft engine emissions non proprietary fuel flow based methods 1,2,3 have been developed. This paper presents derivation, updates, and clarifications of the fuel flow method methodology known as “Fuel Flow Method 2”.

The goal of this project was to investigate how to make driver distraction state classification more efficient by applying selected machine learning techniques to existing datasets. The data set used in this project included both overt driver behavior measures (e.g., lane keeping and headway measures) and indices of internal cognitive processes (e.g., driver situation awareness responses) collected under four distraction conditions, including no-distraction, visual-manual distraction only, cognitive distraction only, and dual distraction conditions. The baseline classification method that we employed was a support vector machine (SVM) to first identify driver states of visual-manual distraction and then to identify any cognitive-related distraction among the visual-manual distraction cases and other non-visual manual distraction cases.

Driver state monitoring is a crucial technology for enhancing road safety and preventing human error-caused accidents in the era of autonomous vehicles. This paper presents CogniSafe, a comprehensive driver monitoring system that uses deep learning and computer vision methods to detect various types of driver distractions and fatigue. CogniSafe consists of four modules: Driver anomaly detection and classification: A novel two-phase network that proposes and recognizes driver anomalies, such as texting, drinking, and adjusting radios, using multimodal and multiview input. Gaze estimation: A video-based neural network that jointly learns head pose and gaze dynamics, achieving robust and efficient gaze estimation across different head poses. Eye state analysis: A multi-tasking CNN that encodes features from both eye and mouth regions, predicting the percentage of eye closure (PERCLOS) and the frequency of mouth opening (FOM).