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Driving simulators allow the testing of driving functions, vehicle models and acceptance assessment at an early stage. For a real driving experience, it's necessary that all immersions are depicted as realistically as possible. When driving manually, the perceived haptic steering wheel torque plays a key role in conveying a realistic steering feel. To ensure this, complex multi-body systems are used with numerous of parameters that are difficult to identify. Therefore, this study shows a method how to generate a realistic steering feel with a nonlinear open-loop model which only contains significant parameters, particularly the friction of the steering gear. This is suitable for the steering feel in the most driving on-center area. Measurements from test benches and real test drives with an Electric Power Steering (EPS) were used for the Identification and Validation of the model.

The optimization and further development of automated driving functions offers great potential to relieve the driver in various driving situations and increase road safety. Simulative testing in particular is an indispensable tool in this process, allowing conclusions to be drawn about the design of automated driving functions at a very early stage of development. In this context, the use of driving simulators provides support so that the driving functions of tomorrow can be experienced in a very safe and reproducible environment. The focus of the acceptance and optimization of automated driving functions is particularly on vehicle lateral control functions. As part of this paper, a test person study was carried out regarding manual vehicle lateral control on the dynamic vehicle road simulator at the Institute of Automotive Engineering.

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.

In pursuing sustainable automotive technologies, exploring alternative fuels for hybrid vehicles is crucial in reducing environmental impact and aligning with global carbon emission reduction goals. This work compares methanol and naphtha as potential suitable alternative fuels for running in a battery-driven light-duty hybrid vehicle by comparing their performance with the diesel baseline engine. This work employs a 0-D vehicle simulation model within the GT-Power suite to replicate vehicle dynamics under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The vehicle choice enables the assessment of a delivery application scenario using distinct payload capacities: 0%, 25%, 50%, and 100%. The model is fed with engine maps derived from previous experimental work conducted in the same engine, in which a full calibration was obtained that ensures the engine's operability in a wide region of rotational speed and loads.

Authors : Thomas ANTOINE, Christophe THEVENARD, Pierrick BOTTA, Jerome DESTREE, Alain Le Quenven Future noise emission limits for passenger car are going to lower levels by 2024 (Third phase of R51-03, with a limit of 68dBA for the pass by noise) –Social cost of noise for France in 2021, shows clearly that the dominant source of noise pollution is indeed road traffic (81 Bn€ for a total of 146 Bn€) This R51 regulation is meant to lower the noise pollution from road traffic, however when looking closer to the sound source and their contributions, in particular the tire/road noise interaction, the environmental efficiency of this regulation is questionable. Indeed: Tire/Road interaction involves tires characteristics, that are constrained by an array of specification for energy efficiency, safety (wet grip, braking, etc…) and it has been proven that there is a physical limit to what could be expected from the tire as far as tire/road interaction noise is concerned.

Computer modelling, virtual prototyping and simulation is widely used in the automotive industry to optimize the development process. While the use of CAE is widespread, on its own it lacks the ability to provide observable acoustics or tactile vibrations for decision makers to assess, and hence optimize the customer experience. Subjective assessment using Driver-in-Loop simulators to experience data has been shown to improve the quality of vehicles and reduce development time and uncertainty. Efficient development processes require a seamless interface from detailed CAE simulation to subjective evaluations suitable for high level decision makers. In the context of perceived vehicle vibration, the need for a bridge between complex CAE data and realistic subjective evaluation of tactile response is most compelling. A suite of VI-grade noise and vibration simulators have been developed to meet this challenge.

In the present work, a new methodology for predicting the performance of centrifugal compressors is developed. The proposed method differs from existing methods found in literature by gathering principal losses in three parameters: two constants and one variable, which is a function of the compressor wheel geometrical characteristics. As those parameters are constants for a given centrifugal compressor, there is no need for additional corrective parameters in order to obtain coherent results. Indeed, the proposed methodology does not depend on the choice of the slip factor correlation for the prediction of the correct pressure ratio. However, the choice of slip factor influences the efficiency computation. The prediction of the compressor maps for two full stage centrifugal compressors is presented and they show good agreement while compared with manufacturer’s data obtained from gas stand measurements.

Quantum computing and its applications are emerging rapidly, driving excitement and extensive interest across all industry sectors, from finance to pharmaceuticals. The automotive industry is no different. Quantum computing can bring significant advantages to the way we commute, whether through the development of new materials and catalysts using quantum chemistry or improved route optimization. Quantum computing may be as important as the invention of driverless vehicles. Emergence of Quantum Computing Technologies in Automotive Applications: Opportunities and Future Use Cases attempts to explain quantum technology and its various advantages for the automotive industry. While many of the applications presented are still nascent, they may become mainstream in a decade or so. Click here to access the full SAE EDGETM Research Report portfolio.

Roundabouts are one of the most complex traffic environments in urban roads, and a key challenge for intelligent driving decision-making. Deep reinforcement learning, as an emerging solution for intelligent driving decisions, has the advantage of avoiding complex algorithm design and sustainable iteration. For the decision difficulty in roundabout scenarios, this paper proposes an artificial potential field based Soft Actor-Critic (APF-SAC) algorithm. Firstly, based on the Carla simulator and Gym framework, a reinforcement learning simulation system for roundabout driving is built. Secondly, to reduce reinforcement learning exploration difficulty, global path planning and path smoothing algorithms are designed to generate and optimize the path to guide the agent.

For intelligent vehicles, a robust perception system relies on training datasets with a large variety of scenes. The architecture of federated learning allows for efficient collaborative model iteration while ensuring privacy and security by leveraging data from multiple parties. However, the local data from different participants is often not independent and identically distributed, significantly affecting the training effectiveness of autonomous driving perception models in the context of federated learning. Unlike the well-studied issues of label distribution discrepancies in previous work, we focus on the challenges posed by scene heterogeneity in the context of federated learning for intelligent vehicles and the inadequacy of a single scene for training multi-task perception models. In this paper, we propose a federated learning-based perception model training system.

With economic development and the increasing number of vehicles in cities, urban transport systems have become an important issue in urban development. Efficient traffic signal control is a key part of achieving intelligent transport. Reinforcement learning methods show great potential in solving complex traffic signal control problems with multidimensional states and actions. Most of the existing work has applied reinforcement learning algorithms to intelligently control traffic signals. In this paper, we investigate the agent-based reinforcement learning approach for the intelligent control of ramp entrances and exits of urban arterial roads, and propose the Proximal Policy Optimization (PPO) algorithm for traffic signal control. We compare the method controlled by the improved PPO algorithm with the no-control method.

This paper details the advancements and outcomes of the NEXTCAR (Next-Generation Energy Technologies for Connected and Automated on-Road Vehicles) program, an initiative led by the Advanced Research Projects Agency-Energy (ARPA-E). The program focusses on harnessing the full potential of Connected and Automated Vehicle (CAV) technologies to develop advanced vehicle dynamic and powertrain control technologies (VD&PT). These technologies have shown the capability to reduce energy consumption by 20% in conventional and hybrid electric cars and trucks at automation levels L1-L3 and by 30% L4 fully autonomous vehicles. Such reductions could lead to significant energy savings across the entire U.S. vehicle fleet.

The SAE AutoDrive Challenge II is a four-year collegiate competition dedicated to developing a Level 4 autonomous vehicle by 2025. In January 2023, the participating teams each received a Chevy Bolt EUV. Within a span of five months, the second phase of the competition took place in Ann Arbor, MI. The authors of this contribution, who participated in this event as team Wisconsin Autonomous representing the University of Wisconsin–Madison, secured second place in static events and third place in dynamic events. This has been accomplished by reducing reliance on the actual vehicle platform and instead leveraging physical analogs and simulation. This paper outlines the software and hardware infrastructure of the competing vehicle, touching on issues pertaining sensors, hardware, and the software architecture employed on the autonomous vehicle. We discuss the LiDAR-camera fusion approach for object detection and the three-tier route planning and following systems.

Digital twin technology has become impactful in Industry 4.0 as it enables engineers to design, simulate, and analyze complex systems and products. As a result of the synergy between physical and virtual realms, innovation in the “real twin” or actual product is more effectively fostered. The availability of verified computer models that describe the target system is important for realistic simulations that provide operating behaviors that can be leveraged for future design studies or predictive maintenance algorithms. In this paper, a digital twin is created for an offroad tracked vehicle that can operate in either autonomous or remote-control modes. Mathematical models are presented and implemented to describe the twin track and vehicle chassis governing dynamics. These components are interfaced through the nonlinear suspension elements and distributed bogies.

The objective of this study is to introduce and assess a computational tool designed to facilitate product development via sensory scores, which serve as a quantifiable representation of human sensory experiences. In the context of designing ride comfort performance, the specialized terminology—either technical or sensory—often served as a barrier to comprehension among the diverse set of specialists constituting the multidisciplinary team. In a previous study by the authors introduced a tool that incorporated a model of sensory performance, utilizing sensory scores as universally comprehensible metrics. However, the tool had yet to be appraised by a genuine cross-functional team. In this study, the tool underwent evaluation through a user-testing process involving twenty-five cross-functional team members engaged in the conceptual design phase at an automotive manufacturing company.

This paper demonstrates a data-driven methodology for the system-level design of high-power traction motor drives in modern battery electric vehicles. With the immense growth of battery electric vehicles in this transformative decade, the expected time to develop and market these powertrain components is becoming significantly shorter than for internal combustion engines. This rising demand is further complicated due to more stringent cost, efficiency and power density targets set by the U.S. Department of Energy. Hence, a system-level perspective is maintained in this data-driven methodology to identify the design requirements for traction motor drives by relying on a dynamic vehicle simulation toolchain and various drive cycles (e.g., EPA MCT, WLTC, US06, etc.). The proposed data-driven approach can be used across different battery electric vehicle platforms including passenger and commercial types.

Plug-in hybrid electric vehicles have the potential of combining the benefits of electric vehicle in terms of low emissions and internal combustion engine vehicles in terms of vehicle range. With the addition of a renewable fuel, the CO2 potential reduction increase even more. The last trends for PHEV are small combustion engine known as range extender, with battery package between full hybrid and electric powertrains. Thus, allowing an improvement in vehicle’s range, reducing battery materials while converting fuel energy through a highly efficient path. Although these vehicles have been proved to be a convenient strategy for decarbonizing the light vehicles, the use of alternative fuels is poorly studied. In this work, hydrous ethanol is chosen because is already available in some countries, such as USA and Brazil, and have an ultra-low well-to-tank CO2 emission.

Electrified vehicles represent mobility’s future, but they impose challenging and diverse requirements like range and performance. To meet these requirements, various components, such as battery cells, electric drives, fuel cells, and hydrogen vessels need to be integrated into a drive and storage system that optimizes the key performance indicators (KPI). However, finding the best combination of components is a multifaceted problem in the early phases of development. Therefore, advanced simulation tools and processes are essential for satisfying the customer´s expectation. EDAG Engineering GmbH has developed a flat storage platform, which is suitable for both, BEV and FCEV. The platform allows for the flexible and modular integration of batteries and hydrogen vessels. However, package space is limited and the impact of the design choices regarding the vehicle’s KPI need to be considered.

Polymer electrolyte membrane (PEM) fuel cells will play a crucial role in the decarbonization of the transport sector, in particular for heavy duty applications. However, performance and durability of PEMFC stacks is still a concern especially when operated under high power density conditions, as required in order to improve the compactness and to reduce the cost of the system. In this context, the optimization of the geometry of hydrogen and air distributors represents a key factor to improve the distribution of the reactants on the active surface, in order to guarantee a proper water management and avoiding membrane dehydration.

For safe driving function, signs must be visible. Sign visibility is function of its luminance intensity. During day, due to ambient light conditions sign luminance is not a major concern. But during night, due to absence of sun light sign board retro-reflectivity plays a crucial role in sign visibility. The vehicle headlamp color, beam pattern, lamp installation position, the relative seating position of driver and moon light conditions are important factors. Virtual simulation approach is used for analyzing the sign board visibility. Among various factors for example the headlamp installation position from ground, distance between two lamps and eye position of driver are considered for analyzing the sign board visibility in this paper. Many automotive organizations have widely varying requirements and established testing guidelines to ensure visibility of signs in head lamp physical testing but there are no guidelines during design stage for headlamp for sign visibility.