Search Results

Autonomous driving is a hot topic in the automotive domain, and there is an increasing need to prove its reliability. They use machine learning techniques, which are themselves stochastic techniques based on some kind of statistical inference. The occurrence of incorrect decisions is part of this approach and often not directly related to correctable errors. The quality of the systems is indicated by statistical key figures such as accuracy and precision. Numerous driving tests and simulations in simulators are extensively used to provide evidence. However, the basis of all descriptive statistics is a random selection from a probability space. The difficulty in testing or constructing the training and test data set is that this probability space is usually not well defined. To systematically address this shortcoming, ontologies have been and are being developed to capture the various concepts and properties of the operational design domain.

The objectives of this study were to provide insights on how injury risk is influenced by occupant demographics such as sex, age, and size; and to quantify differences within the context of commonly-occurring real-world crashes. The analyses were confined to either single-event collisions or collisions that were judged to be well-defined based on the absence of any significant secondary impacts. These analyses, including both logistic regression and descriptive statistics, were conducted using the Crash Investigation Sampling System for calendar years 2017 to 2021. In the case of occupant sex, the findings agree with those of many recent investigations that have attempted to quantify the circumstances in which females show elevated rates of injury relative to their male counterparts given the same level bodily insult. This study, like others, provides evidence of certain female-specific injuries.

Over the past decade, significant progress has been made in developing algorithms and improving hardware for automated driving. However, conducting research and deploying advanced algorithms on automated vehicles for testing and validation remains costly, especially for academia. This paper presents the efforts of our research team to integrate the newest version of the open-source Autoware software with the commercially available DataSpeed Drive-by-Wire (DBW) system, resulting in the creation of a versatile and robust automated vehicle research platform. Autoware, an open-source software stack based on the 2nd generation Robot Operating System (ROS2), has gained prominence in the automated vehicle research community for its comprehensive suite of perception, planning, and control modules. The DataSpeed DBW system directly communicates with the vehicle's CAN bus and provides precise vehicle control capabilities.

To address the issue of PID control for automotive vibration, this paper supplements and develops the evaluation of automotive vibration characteristics, and proposes a vibration response quantity for evaluating the energy dissipation characteristics of automotive vibration. A two-degree-of-freedom single wheel model for automotive vibration control is established, and the conventional vibration response variables for ride comfort evaluation and the energy consumption vibration response variables for energy dissipation characteristics evaluation are determined. This paper uses the Adaptive Differential Evolution (ADE) algorithm to tune the PID control parameters and introduces an adaptive mutation factor to improve the algorithm's adaptability. Several commonly used adaptive mutation factors are summarized in this paper, and their effects on algorithm improvement are compared.

Recently, with the advancement of autonomous driving technology, the function of external lamps has been changed. Previously, the focus was on the visibility of drivers, but with the advancement of autonomous driving technology, the concept of autonomous driving systems has been developed. Accordingly, the trend of automotive lamp lighting systems has been developed in terms of design, e-HMI (exterior-human machine interface), It is developing in accordance with three major fields such as sensor connection. Therefore, this paper will cover the prior development of road content projection headlamps that enable e-HMI implementation to reflect these new trends. Since the technology is mass-produced and sold by several manufacturers, our company also needs to quickly develop and apply the technology in advance. Only four types of symbols are allowed in European law.

To ensure adequate visibility without excessive glare, vehicle headlights are designed to use a specific source of illumination. The optical designs of headlights gather the luminous flux produced by the light source to produce a useful beam pattern that meets the relevant requirements and standards for vehicle forward lighting. With the advent of solid state, light emitting diode sources for general illumination, an increasing number of LED replacement headlight bulb products has emerged over the past decade. In most cases, these LED replacement bulbs are not permitted for legal use on public roadways, but some countries have begun to permit specific LED replacement bulbs to be used legally on the road for specific makes, models and production years of certain vehicles. If they can be demonstrated to produce a beam pattern that meets the photometric requirements for a legal headlight, they are permitted to be used legally for on-road use.

Optimizing the specifications of the parts that make up the vehicle is essential to develop a high performance and quality vehicle with price competitiveness. Optimizing parts specifications for quality and affordability means optimizing various factors such as engineering design specifications and manufacturing processes of parts. This optimization process must be carried out in the early stages of development to maximize its effectiveness. Therefore, in this paper, we studied the methodology of building a database for parts of already developed vehicles and optimizing them on a data basis. A methodology for collecting, standardizing, and analyzing data was studied to define information necessary for specification optimization. In addition, AI technology was used to derive optimization specifications based on the 3D shape of the parts. Through this study, body parts specification optimization system using AI technology was developed.

The accurate prediction of engine performance maps can guide data-driven optimization of engine technologies to control fuel use and associated emissions. However, engine operational maps are scarcely reported in literature and often have missing data. Assessment of missing-data resilient algorithms in the context of engine data prediction could enable better processing of real-world driving cycles, where missing data is a more pervasive phenomenon. The goal of this study is, therefore, to determine the most effective technique to deal with missing data and employ it in prediction of engine performance characteristics. We assess the performance of two machine learning approaches, namely Artificial Neural Networks (ANNs) and the extreme tree boosting algorithm (XGBoost), in handling missing data.

The IncQuery AUTOSAR-UML Bridge is an innovative solution for Assisted Documentation Creation and Automated Handover, aiming at driving a paradigm shift in integrated digital engineering in the automotive domain. The AUTOSAR-UML Bridge is addressing a well-known gap in the engineering ecosystem of automotive design, where the co-design of AUTOSAR models and other model-based artifacts is often hampered by tedious workflows involving manual syncing of model contents between AUTOSAR and UML/SysML tools. The Bridge is aiming at streamlining the workflow by generating high-quality UML models from AUTOSAR projects, with built-in ISO26262 and ASPICE compliance. Automotive software architects and systems engineers spend a lot of time with creating ISO26262-compliant documentation, by creating UML models from AUTOSAR architecture designs, or establishing traceability between requirements captured in SysML and design artefacts that exist in both modeling languages.

Mechanical drawing plays an important role in managing, designing and implementing engineering projects, especially in the field of the automotive industry. The need for accuracy in element design and manufacturing is greater now than ever before in engineering industries. In order to increase accuracy, the part design and function must be clearly communicated between the design engineer and the manufacturing technicians, especially in automotive industry and feeder industries projects. Geometric Dimensions and Tolerances (GD&T) system of elements determines the quality, importance and price of the designed product. The standard used in the United States to define GD&T methodology is ASME Y14.5-2009 while the standard used in Europe is ISO 1101-2017. This article discussed the importance of using GD&T system including the types of geometrical features, limitations and accuracy, datum references frame and feature control frame to handle these symbols seamlessly.

In the process of designing the aerodynamic kit for Formula SAE racing cars, there is a lot of repetitive work and low efficiency in optimizing parameters such as wing angle of attack and chord length. Moreover, the optimization of these parameters in past designs heavily relied on design experience and it's difficult to achieve the optimal solution through theoretical calculations. By establishing a parametric model in CAD software and integrating it with CFD software, we can automatically modify model parameters, run a large number of simulations, and analyze the simulation results using statistical methods. After multiple iterations, we achieve fully automatic parameter optimization and obtain higher negative lift. At the same time, the simulation process is optimized, and simulations are run based on GPUs, resulting in a significant increase in simulation speed compared to the original.

To promote real time monitoring, In use performance ratio monitoring “IUPRm” checks has been enforced in India from Apr’23 as a part of BS6-2 regulation. Since IUPRm is representative of diagnostic frequency in real driving conditions and usage pattern. therefore, a clear understanding of real-world driving is required to define IUPRm targets. This paper shares methodology and Validation steps for defining IUPRm routes for Indian market. Methodology objective is to standardize the market operating conditions over a particular region. Selected Methodology consist of three steps: For defining IUPRm route framework, first step is to have a pre-market survey to know current In use performance ratio “IUPR” status and improvement areas in existing market vehicles. Second step is to define market representative localized on road routes based on the finding of Pre-market survey.

The proposed Euro 7 regulation includes On Board Monitoring, or OBM, to continuously monitor vehicles for emission exceedances. OBM relies on feedback from existing or additional sensors to identify high emitting vehicles, which poses many challenges. Currently, sensors are not commercially available for all emissions constituents, and the accuracy of available sensors is not capable enough for in use compliance determination. On board emissions models do not offer enough fidelity to determine in use compliance and require new complex model innovation development which will be extremely complicated to implement on board the vehicle. The stack up of multi-component deterioration leading to an emissions exceedance is infeasible to detect using available sensors and models.

Understanding driving behavior is crucial for enhancing traffic safety. While previous studies have primarily explored driving behavior using either statistical or machine learning methods, comprehensive assessments employing both methods under various driving mode are limited. In this study, we employ both machine learning and statistical approaches to model driving behavior. First, we design a comprehensive driver grading system to assess the behavior of drivers under different driving modes. Additionally, we present an extended isolation forest-based model to classify driving behavior using data without labels, saving time and effort. Results illustrate that safe driving is more consistent and stable, while aggressive driving exhibits more intensive changes. They also demonstrate that drivers can exhibit various behaviors under different modes, serving as a benchmark for further driver modeling.

Engineering design-decisions often involve many attributes which can differ in the levels of their importance to the decision maker (DM), while also exhibiting complex statistical relationships. Learning a decision-making policy which accurately represents the DM’s actions has long been the goal of decision analysts. To circumvent elicitation and modeling issues, this process is often oversimplified in how many factors are considered and how complicated the relationships considered between them are. Without these simplifications, the classical lottery-based preference elicitation is overly expensive, and the responses degrade rapidly in quality as the number of attributes increase. In this paper, we investigate the ability of deep preference machine learning to model high-dimensional decision-making policies utilizing rankings elicited from decision makers.

This paper compares the results from three human factors studies conducted in a motion-based simulator in 2008, 2014 and 2023, to highlight the trends in driver's response to Forward Collision Warning (FCW). The studies were motivated by the goal to develop an effective HMI (Human-Machine Interface) strategy that enables the required driver's response to FCW while minimizing the level of annoyance of the feature. All three studies evaluated driver response to a baseline-FCW and no-FCW conditions. Additionally, the 2023 study included two modified FCW chime variants: a softer FCW chime and a fading FCW chime. Sixteen (16) participants, balanced for gender and age, were tested for each group in all iterations of the studies. The participants drove in a high-fidelity simulator with a visual distraction task (number reading). After driving 15 minutes in a nighttime rural highway environment, a surprise forward collision threat arose during the distraction task.

Even if an optimal design is produced in the mid-to-late stages of development, when the maturity of development is increasing, it is already difficult to accept the proposal between the organizations and functions. In case the optimal proposal is made with a small amount of information in the preceding stage, it will be helpful for mutual decision-making. In addition, if all members have a system and development environment that enables access and utilization of necessary data in a timely manner, it is possible to produce quick results through collaboration. To implement such a system and development environment, “digital modeling" of tangible and intangible assets will be essential and to implement an "integrated IT environment" that can access and utilize digital models. Until now, Hyundai Motor Company has not yet fully established a digital development environment that all researchers can simultaneously utilize during the concept development stage.

With the rapid development of intelligent driving technology, there has been a growing interest in the driving comfort of automated vehicles. As vehicles become more automated, the role of the driver shifts from actively engaging in driving tasks to that of a passenger. Consequently, the study of the passenger experience in automated driving vehicles has emerged as a significant research area. In order to examine the impact of automatic driving on passengers' riding experience in vehicle platooning scenarios, this study conducted real vehicle experiments involving six participants. The study assessed the subjective perception scores, eye movement, and electrocardiogram (ECG) signals of passengers seated in the front passenger seat under various vehicle speeds, distances, and driving modes. The results of the statistical analysis indicate that vehicle speed has the most substantial influence on passenger perception.

The pursuit of maintaining a zero-sideslip angle has long driven the development of four-wheel-steering (4WS) technology, enhancing vehicle directional performance, as supported by extensive studies. However, strict adherence to this principle often leads to excessive understeer characteristics before tire saturation limits are reached, resulting in counter-intuitive and uncomfortable steering maneuvers during turns with variable speeds. This research delves into the phenomenon encountered when a 4WS-equipped vehicle enters a curved path while simultaneously decelerating, necessitating a reduction in steering input to adapt to the increasing road curvature. To address this challenge, this paper presents a novel method for dynamically regulating the steady-state yaw rate of 4WS vehicles. This regulation aims to decrease the vehicle's sideslip angle and provide controlled understeer within predetermined limits.

The use of hydrogen in internal combustion engines is an effective approach to significantly support the reduction of CO2 emissions from the transportation sector using technically affordable solutions. The use of direct injection is the most promising approach to fully exploit hydrogen potential as a clean fuel, while preserving targets in terms of power density and emissions. In this frame, the development of an effective combustion system largely relies on the hydrogen-air mixture formation process, so to adequately control the charge stratification to mitigate pre-ignitions and knock and to minimize NOx formation. Hence, improving capabilities of designing a correct gas jet-air interaction is of paramount importance. In this paper the analysis of the evolution of a high-pressure gas jet produced by a single-hole prototype injector operated with different pressure ratios is presented.