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Accurately predicting the future trajectories of surrounding traffic agents is important for ensuring the safety of autonomous vehicles. To address the scenario of frequent interactions among traffic agents in the highway merging area, this paper proposes a trajectory prediction method based on interactive graph attention mechanism. Our approach integrates an interactive graph model to capture the complex interactions among traffic agents as well as the interactions between these agents and the contextual map of the highway merging area. By leveraging this interactive graph model, we establish an agent-agent interactive graph and an agent-map interactive graph. Moreover, we employ Graph Attention Network (GAT) to extract spatial interactions among trajectories, enhancing our predictions. To capture temporal dependencies within trajectories, we employ a Transformer-based multi-head self-attention mechanism.

The objective of this study was to assess the formability of two 3rd generation advanced high strength steels (3rd Gen AHSS) with ultimate strengths of 980 and 1180 MPa and evaluate their applicability to a structural B-Pillar for a mid-sized sport utility vehicle. The constitutive behavior including strain-rate effects and formability were characterized to generate the material models for use within AutoForm R8 software to design the B-pillar tooling and forming process. An extended Bressan-Williams instability model was able to deterministically predict the forming limit curves obtained using Marciniak tests. The tooling for the representative B-pillar was designed and fabricated with Bowman Precision Tooling and forming trials conducted for both 3rd Gen steels that had a thickness of 1.4 mm.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

Residual stress prediction arising from manufacturing processes provides paramount information for the fatigue performance assessment of components subjected to cyclic loading. The determination of the material model to be applied in the numerical model should be taken carefully. This study focuses on the estimation of residual stresses generated after deep rolling of cast iron crankshafts. The researched literature on the field employs the available commercial material codes without closer consideration on their reverse loading capacities. To mitigate this gap, a single element model was used to compare potential material models with tensile-compression experiments. The best fit model was then applied to a previously developed crankshaft deep rolling numerical model. In order to confront the simulation outcomes, residual stresses were measured in two directions on real crankshaft specimens that passed through the same modeled deep rolling process.

Various types of after-treatment system for BS VI Stage 1 are being assessed for the Light Duty Diesel (LDD) segment. For BS VI Stage 2, Real Driving Emission (RDE) assessment will be newly introduced, which will require more robustness in emission control system capability. Although the detailed requirements for India BS VI stage 2 are still being discussed, a reasonable assumption is that similar systems to those being developed for Euro 6d, will work for India BS VI. This paper describes typical system designs for Euro 6d and also reveals newly developed SCRF® (Selective Catalytic Reduction Filter) based systems, which demonstrate excellent RDE emissions. In addition, newly developed Lean NOx Trap (NSC) coatings, which focus on low temperature NOx control used with SCRF® (NSC + SCRF®) also show excellent emission control capability as demonstrated in this case on the ARTEMIS Cycle. These systems have potential as promising LDD solutions for India BS VI stage 2.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.

In this paper, a consensus framework for cooperative parameter estimation within the vehicular network is presented. It is assumed that each vehicle is equipped with a dedicated short range communication (DSRC) device and connected to other vehicles. The improvement achieved by the consensus for parameter estimation in presence of sensor’s noise is studied, and the effects of network nodes and edges on the consensus performance is discussed. Finally, the simulation results of the introduced cooperative estimation algorithm for estimation of the unknown parameter of road condition is presented. It is shown that due to the faster dynamic of network communication, single agents’ estimation converges to the least square approximation of the unknown parameter properly.

A three dimensional IR-TRACC (Infrared Telescope Rod for Assessment of Chest Compression) was designed for the Test Device for Human Occupant Restraint (THOR) in recent years to measure chest deflections. Due to the design intricateness, the deflection calculation from the measurements is sophisticated. An algorithm was developed in this paper to calculate the three dimensional deflections of the chest. The algorithm calculates the compression and also converts the results to the local spine coordinate system so that it can correlate with the Post Mortem Human Subject (PMHS) measurements for injury calculation. The method was also verified by a finite element calculation for accuracy, comparing the calculation from the corresponding model output and the direct point to point measurements. In addition, the IR-TRACC calibration methods are discussed in this paper.

A control algorithm is developed for active/semi-active suspensions which can provide more comfort and better handling simultaneously. A weighting parameter is tuned online which is derived from two components - slow and fast adaptation to assign weights to comfort and handling. After establishing through simulations that the proposed adaptive control algorithm can demonstrate a performance better than some controllers in prior-art, it is implemented on an actual vehicle (Cadillac STS) which is equipped with MR dampers and several sensors. The vehicle is tested on smooth and rough roads and over speed bumps.

In vehicles equipped with conventional Electric Power Steering (EPS) systems, the steering effort felt by the driver can be unreasonably low when driving on slippery roads. This may lead inexperienced drivers to steer more than what is required in a turn and risk losing control of the vehicle. Thus, it is sensible for tire-road friction to be accounted for in the design of future EPS systems. This paper describes the design of an auxiliary EPS controller that manipulates torque delivery of current EPS systems by supplying its motor with a compensation current controlled by a fuzzy logic algorithm that considers tire-road friction among other factors. Moreover, a steering system model, a nonlinear vehicle dynamics model and a Dugoff tire model are developed in MATLAB/Simulink. Physical testing is conducted to validate the virtual model and confirm that steering torque decreases considerably on low friction roads.

Parameter identification of a math-based spark-ignition engine model is studied in this paper. Differential-algebraic equations governing the dynamic behavior of the engine combustion model are derived using a quasi-dimensional modelling scheme. The model is developed based on the two-zone combustion theory with turbulent flame propagation through the combustion chamber [1]. The system of equations includes physics-based equations combined with the semi-empirical Wiebe function. The GT-Power engine simulator software [2], a powerful tool for design and development of engines, is used to extract the reference data for the engine parameter identification. The models is GT-Power are calibrated and validated with experimental results; thus, acquired data from the software can be a reliable reference for engine validation purposes.

Physical rig testing of a vehicle is often undertaken to obtain experimental data that can be used to ensure a mathematical model is an accurate representation of the vehicle under study. Kinematics and Compliance (K&C) testing is often used for this purpose. The relationship between the hard point locations and compliance parameters, and K&C characteristics of a suspension system is complex, and so automating the process to correlate the model to the test data can make the exercise easier, faster and more accurate than hand tuning the model. In this work, such a process is developed. First, the model parameters are adjusted, next a simulation is run, before the results are read and post processed. This automation processed is used in conjunction with an optimization procedure to carry out the K&C correlation.

When performing catalyst modeling and parameter tuning it is desirable that the experimental data contain both transient and stationary points and can be generated over a short period of time. Here a method of creating such concentration transients for a full scale engine rig system is presented. The paper describes a valuable approach for changing the composition of engine exhaust gas going to a DOC (or potentially any other device) by conditioning the exhaust gas with an additional upstream DOC and/or SCR. By controlling the urea injection and the DOC bypass a wide range of exhaust compositions, not possible by only controlling the engine, could be achieved. This will improve the possibilities for parameter estimation for the modeling of the DOC.

The effects of propylene (C₃H₆) and dodecane (n-C₁₂H₂₆) exposure on the NH₃-based selective catalytic reduction (SCR) performance of two Cu-exchanged zeolite catalysts were investigated. The first sample was a model Cu/beta zeolite sample and the second a state-of-the-art Cu/zeolite sample, with the zeolite material characterized by relatively small pores. Overall, the state-of-the-art sample performed better than the model sample, in terms of hydrocarbon inhibition (which was reduced) and N₂O formation (less formed). The state-of-the-art sample was completely unaffected by dodecane at temperatures lower than 300°C, and only slightly inhibited (less than 5% conversion loss), for standard SCR, by C₃H₆. There was no evidence of coke formation on this catalyst with C₃H₆ exposure. The model sample was more significantly affected by hydrocarbon exposure. With C₃H₆, inhibition is associated with its partial oxidation intermediates adsorbed on the catalyst surface.

The recent increase in oil prices and environmental concerns have attracted various research efforts on hybrid electric vehicles (HEVs) which provide promising alternatives to conventional engine-powered vehicles with better fuel economy and fewer emissions. To speed up the design and prototyping processes of new HEVs, a method that automatically generates mathematics equations governing the vehicle system response in an optimized symbolic form is desirable. To achieve this goal, we employed MapleSimTM, a new physical modeling tool developed by Maplesoft Inc., to develop the multi-domain model of a series-HEV, utilizing the symbolic computing algorithms of Maple software package to generate an optimized set of governing equations. The HEV model consists of a mean-value internal combustion engine (ICE), a chemistry-based Ni-MH battery pack, and a multibody vehicle model. Simulations are then used to demonstrate the performance of the developed HEV system.

The performance of a commercial Cu-zeolite SCR catalyst after differing degrees of hydrothermal aging (aged for 72 hours at 500, 700 and 800°C with 10% moisture balanced with air) was studied by spatially resolving different key reactions using gas-phase FTIR measurements. Gases were sampled along a channel at different positions and analyzed using FTIR, which overcomes the interference of water and nitrogen on ammonia concentration detection encountered in standard mass spectrometer-based spatial resolution measurements. The NO:NO₂ concentration ratio was changed so that the standard (NO:NO₂ = 1:0), fast (NO:NO₂ = 1:1) and NO₂ (NO:NO₂ = 0:1) SCR reactions could be investigated as a function of the catalyst's hydrothermal aging extent. In addition, the effects of hydrothermal aging on the activity of NH₃ and NO oxidation were also investigated. Hydrothermal aging had little effect on NO oxidation activity.

A method to improve the computational efficiency of analyzing wheeled vehicle systems on three-dimensional (3-D) roads has been developed. This was accomplished by creating a technique to incorporate the tire on a 3-D road in a multibody dynamics model of the vehicle with an approach that formulates the governing equations using symbolic formulation. For general handling analysis performed on the vehicle, the tire forces and moments are determined using a tire model that represents the tire as a set of mathematical expressions. Since these expressions need numerical values to determine the forces and moments, a symbolic solution does not exist. Therefore, the evaluation of the tire forces and moments needs to be done during simulation. However, symbolic operations can be used when the governing equations are formulated to develop an efficient method to evaluate these forces.

The response to increasingly stringent light duty diesel emission regulation is a nearly unanimous increase in heavy Exhaust Gas Recirculation (EGR) application to reduce feedgas NOx emissions. Little attention has been paid to the fact that heavy EGR usage is likely to push the engine operating conditions towards less efficient or even unstable regions of conventional centrifugal compressor operating maps. Moreover, the low oxygen content at part load operation also poses transient response challenges. Therefore, improving turbocharger efficiency at part load and extending the stable operating range is becoming critical for viable future low emission diesel engines. In this study of a turbocharger compression system, encompassing the airflow geometry from compressor impeller inlet to volute exit, a dual volute compressor concept was introduced, and Computational Fluid Dynamics (CFD) was used to investigate its effects on the overall expected performance level and range.

The performance characteristics of a commercial lean-NOX trap catalyst were evaluated between 200 and 500°C, using H2, CO, and a mixture of both H2 and CO as reductants before and after different high-temperature aging steps, from 600 to 750°C. Tests included NOX reduction efficiency during cycling, NOX storage capacity (NSC), oxygen storage capacity (OSC), and water-gas-shift (WGS) and NO oxidation reaction extents. The WGS reaction extent at 200 and 300°C was negatively affected by thermal degradation, but at 400 and 500°C no significant change was observed. Changes in the extent of NO oxidation did not show a consistent trend as a function of thermal degradation. The total NSC was tested at 200, 350 and 500°C. Little change was observed at 500°C with thermal degradation but a steady decrease was observed at 350°C as the thermal degradation temperature was increased.