Sandra Bickelhaupt, Michael Hahn, Andrey Morozov, Michael Weyrich
Software will lead the development and life cycle of vehicles in the future. Nowadays, more and more software is being integrated into a vehicle, evolving it into a Software-Defined Vehicle (SDV). Automotive High Performance Computers (HPCs) serve as enablers by providing more computing infrastructure which can be flexibly used inside a vehicle. However, this leads to a complex vehicle system that needs to function today and in the future. Detecting and rectifying failures as quickly as possible is essential, but existing diagnostic approaches based on Diagnostic Trouble Codes (DTCs) are not designed for such complex systems and lack of flexibility. DTCs are predefined during vehicle development and changes to vehicle diagnostics require a large amount of modification work. Moreover, diagnostics are not intended to handle dynamically changing software systems and have shortcomings when applied to in-vehicle software systems. In the Cloud, there are already established approaches to observe and diagnose software systems. However, these approaches are too comprehensive and cannot simply be applied to the whole vehicle. Anyway, they are a helpful addition to adapting vehicle diagnostics. Therefore, their vehicle applicability needs to be investigated. In this paper, we discuss the challenges of transferring and adapting the DTC approach to in-vehicle software systems, as well as monitoring and observability approaches to vehicles. Based on this, we introduce a concept for future vehicle diagnostics that addresses existing diagnostic approaches based on DTCs in combination with established approaches for monitoring and observability. Our presented concept provides a basis for further future work in the context of vehicle diagnostics for SDVs.
{"title":"Towards Future Vehicle Diagnostics in Software-Defined Vehicles","authors":"Sandra Bickelhaupt, Michael Hahn, Andrey Morozov, Michael Weyrich","doi":"10.4271/2024-01-2981","DOIUrl":"https://doi.org/10.4271/2024-01-2981","url":null,"abstract":"Software will lead the development and life cycle of vehicles in the future. Nowadays, more and more software is being integrated into a vehicle, evolving it into a Software-Defined Vehicle (SDV). Automotive High Performance Computers (HPCs) serve as enablers by providing more computing infrastructure which can be flexibly used inside a vehicle. However, this leads to a complex vehicle system that needs to function today and in the future. Detecting and rectifying failures as quickly as possible is essential, but existing diagnostic approaches based on Diagnostic Trouble Codes (DTCs) are not designed for such complex systems and lack of flexibility. DTCs are predefined during vehicle development and changes to vehicle diagnostics require a large amount of modification work. Moreover, diagnostics are not intended to handle dynamically changing software systems and have shortcomings when applied to in-vehicle software systems. In the Cloud, there are already established approaches to observe and diagnose software systems. However, these approaches are too comprehensive and cannot simply be applied to the whole vehicle. Anyway, they are a helpful addition to adapting vehicle diagnostics. Therefore, their vehicle applicability needs to be investigated. In this paper, we discuss the challenges of transferring and adapting the DTC approach to in-vehicle software systems, as well as monitoring and observability approaches to vehicles. Based on this, we introduce a concept for future vehicle diagnostics that addresses existing diagnostic approaches based on DTCs in combination with established approaches for monitoring and observability. Our presented concept provides a basis for further future work in the context of vehicle diagnostics for SDVs.","PeriodicalId":510086,"journal":{"name":"SAE Technical Paper Series","volume":"18 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141688159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yifan Wang, Jannes Iatropoulos, Silvia Thal, Roman Henze
AEB systems are critical in preventing collisions, yet their effectiveness hinges on accurately estimating the distance between the vehicle and other road users, as well as understanding road conditions. Errors in distance estimation can result in premature or delayed braking and varying road conditions alter road-tire friction coefficients, affecting braking distances. The integration of advanced sensors like LiDARs has significantly enhanced distance estimation. Cameras and deep neural networks are also employed to estimate the road conditions. However, AEB systems face notable challenges in urban environments, influenced by complex scenarios and adverse weather conditions such as rain and fog. Therefore, investigating the error tolerance of these estimations is essential for the performance of AEB systems. To this end, we develop a digital twin of our test vehicle in the IPG CarMaker simulation environment, which includes realistic driving dynamics and sensor models. Our simulated test vehicle is equipped with a distance estimation algorithm and AEB system designed for eventual deployment in its real-world counterpart. We test the vehicle in various simulated test scenarios. This approach facilitates accurate measurement and adjustment of distance and road-tire friction coefficients. The testing protocol begins with the European New Car Assessment Programme (EU NCAP) AEB Car-to-Pedestrian standard. Additionally, our simulation encompasses realistic urban scenarios, featuring complex traffic conditions and diverse weather scenarios, including rain, fog, and varying road surfaces like dry, wet, snow-covered, and icy. Finally, we have determined the error tolerances for various conditions. The simulation process and results reveal that the major challenges involve creating critical scenarios, modeling environments and sensors, and constructing digital twins of test vehicles. Recommendations and insights derived from these findings are also provided.
{"title":"Enhancing Urban AEB Systems: Simulation-Based Analysis of Error Tolerance in Distance Estimation and Road-Tire Friction Coefficients","authors":"Yifan Wang, Jannes Iatropoulos, Silvia Thal, Roman Henze","doi":"10.4271/2024-01-2992","DOIUrl":"https://doi.org/10.4271/2024-01-2992","url":null,"abstract":"AEB systems are critical in preventing collisions, yet their effectiveness hinges on accurately estimating the distance between the vehicle and other road users, as well as understanding road conditions. Errors in distance estimation can result in premature or delayed braking and varying road conditions alter road-tire friction coefficients, affecting braking distances. The integration of advanced sensors like LiDARs has significantly enhanced distance estimation. Cameras and deep neural networks are also employed to estimate the road conditions. However, AEB systems face notable challenges in urban environments, influenced by complex scenarios and adverse weather conditions such as rain and fog. Therefore, investigating the error tolerance of these estimations is essential for the performance of AEB systems. To this end, we develop a digital twin of our test vehicle in the IPG CarMaker simulation environment, which includes realistic driving dynamics and sensor models. Our simulated test vehicle is equipped with a distance estimation algorithm and AEB system designed for eventual deployment in its real-world counterpart. We test the vehicle in various simulated test scenarios. This approach facilitates accurate measurement and adjustment of distance and road-tire friction coefficients. The testing protocol begins with the European New Car Assessment Programme (EU NCAP) AEB Car-to-Pedestrian standard. Additionally, our simulation encompasses realistic urban scenarios, featuring complex traffic conditions and diverse weather scenarios, including rain, fog, and varying road surfaces like dry, wet, snow-covered, and icy. Finally, we have determined the error tolerances for various conditions. The simulation process and results reveal that the major challenges involve creating critical scenarios, modeling environments and sensors, and constructing digital twins of test vehicles. Recommendations and insights derived from these findings are also provided.","PeriodicalId":510086,"journal":{"name":"SAE Technical Paper Series","volume":"27 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the evolving landscape of automated driving systems, the critical role of vehicle localization within the autonomous driving stack is increasingly evident. Traditional reliance on Global Navigation Satellite Systems (GNSS) proves to be inadequate, especially in urban areas where signal obstruction and multipath effects degrade accuracy. Addressing this challenge, this paper details the enhancement of a localization system for autonomous public transport vehicles, focusing on mitigating GNSS errors through the integration of a LiDAR sensor. The approach involves creating a 3D map using the factor graph-based LIO-SAM algorithm, which is further enhanced through the integration of wheel encoder and altitude data. Based on the generated map a LiDAR localization algorithm is used to determine the pose of the vehicle. The FAST-LIO based localization algorithm is enhanced by integrating relative LiDAR Odometry estimates and by using a simple yet effective delay compensation method to enable operation at higher velocities. To robustly fuse LiDAR- and GNSS-based position estimates, an emperical motivated geobased adjustment scheme for the covariances of the two datasources is presented. The performance of the mapping and localization components is validated with real driving data, demonstrating improved stability and accuracy compared to the GNSS-based localization system.
{"title":"Environment-Adaptive Localization based on GNSS, Odometry and LiDAR Systems","authors":"Markus Kramer, Georg Beierlein","doi":"10.4271/2024-01-2986","DOIUrl":"https://doi.org/10.4271/2024-01-2986","url":null,"abstract":"In the evolving landscape of automated driving systems, the critical role of vehicle localization within the autonomous driving stack is increasingly evident. Traditional reliance on Global Navigation Satellite Systems (GNSS) proves to be inadequate, especially in urban areas where signal obstruction and multipath effects degrade accuracy. Addressing this challenge, this paper details the enhancement of a localization system for autonomous public transport vehicles, focusing on mitigating GNSS errors through the integration of a LiDAR sensor. The approach involves creating a 3D map using the factor graph-based LIO-SAM algorithm, which is further enhanced through the integration of wheel encoder and altitude data. Based on the generated map a LiDAR localization algorithm is used to determine the pose of the vehicle. The FAST-LIO based localization algorithm is enhanced by integrating relative LiDAR Odometry estimates and by using a simple yet effective delay compensation method to enable operation at higher velocities. To robustly fuse LiDAR- and GNSS-based position estimates, an emperical motivated geobased adjustment scheme for the covariances of the two datasources is presented. The performance of the mapping and localization components is validated with real driving data, demonstrating improved stability and accuracy compared to the GNSS-based localization system.","PeriodicalId":510086,"journal":{"name":"SAE Technical Paper Series","volume":"32 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141685200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jannik Kexel, Stefan Pischinger, Andreas Balazs, Benedikt Schroeder, Hagen Wegner
In response to the challenge of climate change, the European Union has developed a strategy to achieve climate neutrality by 2050. Extensive research has been conducted on the CO2 life cycle analysis of propulsion systems. However, achieving net-zero CO2 emissions requires adjusting key performance indicators for the development of these. Therefore, we investigated the ecological sustainability impacts of various propulsion concepts integrated in a C-segment sports utility vehicle assuming a 100% renewable energy scenario. The propulsion concepts studied include a hydrogen-fueled 48V mild hybrid, a hydrogen-fueled 48V hybrid, a methanol-fueled 400V hybrid, a methanol-to-gasoline-fueled 400V plug-in hybrid, an 800V battery electric vehicle (BEV), and a hydrogen fuel cell electric vehicle (FCEV). To achieve a comprehensive and objective comparison of various propulsion concepts that meet the same pre-defined customer requirements for system design, we conducted an integrated and prospective Life-Cycle Assessment (LCA) using the methodology of DIN EN ISO 14040/44 and the EU Product Environmental Footprint. Unlike other studies, we used an integrated approach to aggregate the Life-Cycle Inventory data. This approach combines model-based system design with physical-empirical simulation models and publicly available LCA databases. Assuming the defossilized energy scenario, it leads to more sustainable propulsion systems, regardless of the propulsion concept. The FCEV has slight advantages, while the BEV has disadvantages that can be improved by reducing requirements or adapting cell chemistry. Based on this, we recommend developing propulsion systems for the future in an open-minded manner, tailored to specific use-cases and targeted requirements, while considering the entire life cycle.
为应对气候变化的挑战，欧盟制定了到 2050 年实现气候中和的战略。对推进系统的二氧化碳生命周期分析进行了大量研究。然而，要实现二氧化碳净零排放，就必须调整开发这些系统的关键性能指标。因此，我们研究了集成在 C 级运动型多用途车中的各种推进概念对生态可持续性的影响，并假设了 100% 的可再生能源情景。研究的推进概念包括氢燃料 48V 轻度混合动力车、氢燃料 48V 混合动力车、甲醇燃料 400V 混合动力车、甲醇-汽油燃料 400V 插电式混合动力车、800V 电池电动车 (BEV) 和氢燃料电池电动车 (FCEV)。为了全面、客观地比较各种推进概念，满足客户对系统设计的相同预定要求，我们采用 DIN EN ISO 14040/44 和欧盟产品环境足迹的方法，进行了综合、前瞻性的生命周期评估 (LCA)。与其他研究不同的是，我们采用了一种综合方法来汇总生命周期清单数据。这种方法结合了基于模型的系统设计、物理-经验模拟模型和公开可用的生命周期评估数据库。假定采用化石能源方案，无论采用哪种推进概念，都能产生更具可持续性的推进系统。FCEV 略有优势，而 BEV 则有劣势，这些劣势可以通过降低要求或调整电池化学成分来改善。在此基础上，我们建议以开放的态度开发面向未来的推进系统，根据具体的使用情况和目标要求量身定制，同时考虑整个生命周期。
{"title":"Sustainable Propulsion in a Post-Fossil Energy World: Life-Cycle Assessment of Renewable Fuel and Electrified Propulsion Concepts","authors":"Jannik Kexel, Stefan Pischinger, Andreas Balazs, Benedikt Schroeder, Hagen Wegner","doi":"10.4271/2024-01-3013","DOIUrl":"https://doi.org/10.4271/2024-01-3013","url":null,"abstract":"In response to the challenge of climate change, the European Union has developed a strategy to achieve climate neutrality by 2050. Extensive research has been conducted on the CO2 life cycle analysis of propulsion systems. However, achieving net-zero CO2 emissions requires adjusting key performance indicators for the development of these. Therefore, we investigated the ecological sustainability impacts of various propulsion concepts integrated in a C-segment sports utility vehicle assuming a 100% renewable energy scenario. The propulsion concepts studied include a hydrogen-fueled 48V mild hybrid, a hydrogen-fueled 48V hybrid, a methanol-fueled 400V hybrid, a methanol-to-gasoline-fueled 400V plug-in hybrid, an 800V battery electric vehicle (BEV), and a hydrogen fuel cell electric vehicle (FCEV). To achieve a comprehensive and objective comparison of various propulsion concepts that meet the same pre-defined customer requirements for system design, we conducted an integrated and prospective Life-Cycle Assessment (LCA) using the methodology of DIN EN ISO 14040/44 and the EU Product Environmental Footprint. Unlike other studies, we used an integrated approach to aggregate the Life-Cycle Inventory data. This approach combines model-based system design with physical-empirical simulation models and publicly available LCA databases. Assuming the defossilized energy scenario, it leads to more sustainable propulsion systems, regardless of the propulsion concept. The FCEV has slight advantages, while the BEV has disadvantages that can be improved by reducing requirements or adapting cell chemistry. Based on this, we recommend developing propulsion systems for the future in an open-minded manner, tailored to specific use-cases and targeted requirements, while considering the entire life cycle.","PeriodicalId":510086,"journal":{"name":"SAE Technical Paper Series","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tongfang Fu, Zhipeng Xu, M. Günther, Stefan Pischinger, Simon Böld
Noise, vibration and harshness (NVH) is one of the most important performance evaluation aspects of electric motors. Among the different causes of the NVH issues of electrical drives, the spatial and temporal harmonics of the electrical drive system are of great importance. To reduce the tonal noise of the electric motors induced by these harmonics, harmonic injection methods are applied in many applications. However, a lot of existing researches focus more either on improving the optimization process of the harmonic injection parameter settings, or on the controller design of the harmonic injection process, while the structural dynamic characteristics of the motor are seldom considered. A lot of literature shows that the harmonic injection strategies can more effectively influence the mode 0 (M0) radial forces than the higher spatial orders, so it is more efficient to apply such methods at the frequencies/orders where the effect of mode 0 forces are dominant with respect to the surface vibration or acoustics of the motor. In this paper, a guideline is proposed for the design and optimization of current harmonic injection strategies, where a 2-dimensional linear transfer function is computed to quantify the contributions of different force modes and it is used as the reference for the harmonic injection control settings. The proposed method is tested and validated with the multi-physics co-simulation of a finite-element model for an interior permanent magnet synchronous motor (IPMSM), where the influence of the inverter and pulse width modulation (PWM) are also considered and analyzed. The simulation results show that the proposed scheme can effectively reduce the surface vibration (~1.5dB) at the chosen sensor location without deteriorating the torque output performance of the IPMSM model.
{"title":"Harmonic Injection Method for NVH Optimization of Permanent Magnet Synchronous Motors Considering the Structural Characteristics of the Machine","authors":"Tongfang Fu, Zhipeng Xu, M. Günther, Stefan Pischinger, Simon Böld","doi":"10.4271/2024-01-3015","DOIUrl":"https://doi.org/10.4271/2024-01-3015","url":null,"abstract":"Noise, vibration and harshness (NVH) is one of the most important performance evaluation aspects of electric motors. Among the different causes of the NVH issues of electrical drives, the spatial and temporal harmonics of the electrical drive system are of great importance. To reduce the tonal noise of the electric motors induced by these harmonics, harmonic injection methods are applied in many applications. However, a lot of existing researches focus more either on improving the optimization process of the harmonic injection parameter settings, or on the controller design of the harmonic injection process, while the structural dynamic characteristics of the motor are seldom considered. A lot of literature shows that the harmonic injection strategies can more effectively influence the mode 0 (M0) radial forces than the higher spatial orders, so it is more efficient to apply such methods at the frequencies/orders where the effect of mode 0 forces are dominant with respect to the surface vibration or acoustics of the motor. In this paper, a guideline is proposed for the design and optimization of current harmonic injection strategies, where a 2-dimensional linear transfer function is computed to quantify the contributions of different force modes and it is used as the reference for the harmonic injection control settings. The proposed method is tested and validated with the multi-physics co-simulation of a finite-element model for an interior permanent magnet synchronous motor (IPMSM), where the influence of the inverter and pulse width modulation (PWM) are also considered and analyzed. The simulation results show that the proposed scheme can effectively reduce the surface vibration (~1.5dB) at the chosen sensor location without deteriorating the torque output performance of the IPMSM model.","PeriodicalId":510086,"journal":{"name":"SAE Technical Paper Series","volume":"41 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to its physical and chemical properties, hydrogen is an attractive fuel for internal combustion engines, providing grounds for studies on hydrogen engines. It is common practice to use a mathematical model for basic engine design and an essential part of this is the simulation of the combustion cycle, which is the subject of the work presented here. One of the most widely used models for describing combustion in gasoline and diesel engines is the Wiebe model. However, for cases of hydrogen combustion in DI engines, which are characterized by mixture stratification and in some cases significant incomplete combustion, practically no data can be found in the literature on the application of the Wiebe model. Based on Wiebe’s formulas, a mathematical model of hydrogen combustion has been developed. The model allows making computations for both DI and PFI hydrogen engines. The parameters of the Wiebe model were assessed for three different engines in a total of 26 operating modes. The modified base model considers the significant incompleteness of hydrogen combustion, which occurs at high air/fuel equivalence ratio. For PFI and DI hydrogen engines, equations and numerical values for the Wiebe model coefficients were determined to describe the dynamic and duration of combustion. Based on our simulation results we suggest using the sum of two Wiebe curves to describe combustion in zones with a lean mixture in DI engines. This allows a more accurate characterization of the combustion dynamics and pressure curves. In order to model a double hydrogen injection, we suggest using the sum of three Wiebe curves representing the combustion of the first injection in the flame front, the diffusion combustion of the second injection, and the relatively slow combustion in lean mixture zones. In the paper, we present a method for selecting the coefficients of each of the Wiebe curves.
由于其物理和化学特性，氢气是一种极具吸引力的内燃机燃料，这为氢气发动机的研究提供了基础。通常的做法是使用数学模型进行发动机的基本设计，其中一个重要部分是模拟燃烧循环，这也是本文所介绍的工作的主题。用于描述汽油和柴油发动机燃烧的最广泛的模型之一是 Wiebe 模型。然而，对于 DI 发动机中的氢气燃烧，其特点是混合气分层，在某些情况下会出现严重的不完全燃烧，在文献中几乎找不到关于 Wiebe 模型应用的数据。根据 Wiebe 的公式，我们建立了一个氢气燃烧数学模型。该模型可用于 DI 和 PFI 氢气发动机的计算。在总共 26 种运行模式下，对三种不同发动机的 Wiebe 模型参数进行了评估。修改后的基本模型考虑了氢燃烧的显著不完整性，这种不完整性发生在高空气/燃料当量比的情况下。针对 PFI 和 DI 氢气发动机，确定了维伯模型系数的方程和数值，以描述燃烧的动态和持续时间。根据我们的模拟结果，我们建议使用两条 Wiebe 曲线之和来描述 DI 发动机贫混合气区域的燃烧情况。这样可以更准确地描述燃烧动态和压力曲线。为了建立双氢喷射模型，我们建议使用三条维伯曲线之和，分别代表火焰前沿的第一次喷射燃烧、第二次喷射的扩散燃烧以及贫混合气区中相对缓慢的燃烧。在本文中，我们提出了一种选择各维伯曲线系数的方法。
{"title":"Simulation of Hydrogen Combustion in Spark Ignition Engines Using a Modified Wiebe Model","authors":"O. Osetrov, Rainer Haas","doi":"10.4271/2024-01-3016","DOIUrl":"https://doi.org/10.4271/2024-01-3016","url":null,"abstract":"Due to its physical and chemical properties, hydrogen is an attractive fuel for internal combustion engines, providing grounds for studies on hydrogen engines. It is common practice to use a mathematical model for basic engine design and an essential part of this is the simulation of the combustion cycle, which is the subject of the work presented here. One of the most widely used models for describing combustion in gasoline and diesel engines is the Wiebe model. However, for cases of hydrogen combustion in DI engines, which are characterized by mixture stratification and in some cases significant incomplete combustion, practically no data can be found in the literature on the application of the Wiebe model. Based on Wiebe’s formulas, a mathematical model of hydrogen combustion has been developed. The model allows making computations for both DI and PFI hydrogen engines. The parameters of the Wiebe model were assessed for three different engines in a total of 26 operating modes. The modified base model considers the significant incompleteness of hydrogen combustion, which occurs at high air/fuel equivalence ratio. For PFI and DI hydrogen engines, equations and numerical values for the Wiebe model coefficients were determined to describe the dynamic and duration of combustion. Based on our simulation results we suggest using the sum of two Wiebe curves to describe combustion in zones with a lean mixture in DI engines. This allows a more accurate characterization of the combustion dynamics and pressure curves. In order to model a double hydrogen injection, we suggest using the sum of three Wiebe curves representing the combustion of the first injection in the flame front, the diffusion combustion of the second injection, and the relatively slow combustion in lean mixture zones. In the paper, we present a method for selecting the coefficients of each of the Wiebe curves.","PeriodicalId":510086,"journal":{"name":"SAE Technical Paper Series","volume":"154 6‐10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141686584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gina Abdelhalim, Kevin Simon, Robert Bensch, Sai Parimi, Bilal Ahmed Qureshi
Autonomous Driving is used in various settings, including indoor areas such as industrial halls and warehouses. For perception in these environments, LIDAR is currently very popular due to its high accuracy compared to RADAR and its robustness to varying lighting conditions compared to cameras. However, there is a notable lack of freely available labeled LIDAR data in these settings, and most public datasets, such as KITTI and Waymo, focus on public road scenarios. As a result, specialized publicly available annotation frameworks are rare as well. This work tackles these shortcomings by developing an automated AI-based labeling tool to generate a LIDAR dataset with 3D ground truth annotations for industrial warehouse scenarios. The base pipeline for the annotation framework first upsamples the incoming 16-channel data into dense 64-channel data. The upsampled data is then manually annotated for the defined classes and this annotated 64-channel dataset is used to fine-tune the Part-A2-Net that has been pretrained on the KITTI dataset. This fine-tuned network shows promising results for the defined classes. To overcome some shortcomings with this pipeline, which mainly involves artefacts from upsampling and manual labeling, we extend the pipeline to make use of SLAM to generate the dense point cloud and use the generated poses to speed up the labeling process. The progression, therefore shows the three generations of the framework which started with manual upsampling and labeling. This then was extended to a semi-automated approach with automatic generation of dense map using SLAM and automatic annotation propagation to all the scans for all static classes and then the complete automatic pipeline that generates ground truth using the Part-A2-Net which was trained using the dataset generated from the manual and semi-automated pipelines. The dataset generated for this warehouse environment will continuously be extended and is publicly available at https://github.com/anavsgmbh/lidar-warehouse-dataset.
{"title":"Automated AI-Based Annotation Framework for 3D Object Detection from LIDAR Data in Industrial Areas","authors":"Gina Abdelhalim, Kevin Simon, Robert Bensch, Sai Parimi, Bilal Ahmed Qureshi","doi":"10.4271/2024-01-2999","DOIUrl":"https://doi.org/10.4271/2024-01-2999","url":null,"abstract":"Autonomous Driving is used in various settings, including indoor areas such as industrial halls and warehouses. For perception in these environments, LIDAR is currently very popular due to its high accuracy compared to RADAR and its robustness to varying lighting conditions compared to cameras. However, there is a notable lack of freely available labeled LIDAR data in these settings, and most public datasets, such as KITTI and Waymo, focus on public road scenarios. As a result, specialized publicly available annotation frameworks are rare as well. This work tackles these shortcomings by developing an automated AI-based labeling tool to generate a LIDAR dataset with 3D ground truth annotations for industrial warehouse scenarios. The base pipeline for the annotation framework first upsamples the incoming 16-channel data into dense 64-channel data. The upsampled data is then manually annotated for the defined classes and this annotated 64-channel dataset is used to fine-tune the Part-A2-Net that has been pretrained on the KITTI dataset. This fine-tuned network shows promising results for the defined classes. To overcome some shortcomings with this pipeline, which mainly involves artefacts from upsampling and manual labeling, we extend the pipeline to make use of SLAM to generate the dense point cloud and use the generated poses to speed up the labeling process. The progression, therefore shows the three generations of the framework which started with manual upsampling and labeling. This then was extended to a semi-automated approach with automatic generation of dense map using SLAM and automatic annotation propagation to all the scans for all static classes and then the complete automatic pipeline that generates ground truth using the Part-A2-Net which was trained using the dataset generated from the manual and semi-automated pipelines. The dataset generated for this warehouse environment will continuously be extended and is publicly available at https://github.com/anavsgmbh/lidar-warehouse-dataset.","PeriodicalId":510086,"journal":{"name":"SAE Technical Paper Series","volume":"55 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141688621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The global time that is propagated and synchronized in the vehicle E/E architecture is used in safety-critical, security-critical, and time-critical applications (e.g., driver assistance functions, intrusion detection system, vehicle diagnostics, external device authentication during vehicle diagnostics, vehicle-to-grid and so on). The cybersecurity attacks targeting the global time result in false time, accuracy degradation, and denial of service as stated in IETF RFC 7384 [2]. These failures reduce the vehicle availability, robustness, and safety of the road user. IEEE 1588 [3] lists four mechanisms (integrated security mechanism, external security mechanism, architectural solution, and monitoring & management) to secure the global time. AUTOSAR defines the architecture and detailed specifications for the integrated security mechanism “Secured Global Time Synchronization (SGTS)” to secure the global time on automotive networks (CAN, FlexRay, Ethernet). However, there are also external security mechanisms such as MACsec which protect all communication frames (at layer 2) on an Ethernet network. The objective of this paper is to evaluate the need of SGTS in a vehicle E/E architecture. As part of the evaluation, this paper presents the experimental data to demonstrate the impact on the precision of global time with SGTS and MACsec. It describes the constraints that prevent applying the SGTS and/or MACsec on an Ethernet network. It emphasizes the tradeoff between security and precise global time when using SGTS and/or MACsec on an Ethernet network.
{"title":"Enabling the Security of Global Time in Software-Defined-Vehicles (SGTS, MACsec)","authors":"Pavithra Kumaraswamy, Andrei Rus","doi":"10.4271/2024-01-2978","DOIUrl":"https://doi.org/10.4271/2024-01-2978","url":null,"abstract":"The global time that is propagated and synchronized in the vehicle E/E architecture is used in safety-critical, security-critical, and time-critical applications (e.g., driver assistance functions, intrusion detection system, vehicle diagnostics, external device authentication during vehicle diagnostics, vehicle-to-grid and so on). The cybersecurity attacks targeting the global time result in false time, accuracy degradation, and denial of service as stated in IETF RFC 7384 [2]. These failures reduce the vehicle availability, robustness, and safety of the road user. IEEE 1588 [3] lists four mechanisms (integrated security mechanism, external security mechanism, architectural solution, and monitoring & management) to secure the global time. AUTOSAR defines the architecture and detailed specifications for the integrated security mechanism “Secured Global Time Synchronization (SGTS)” to secure the global time on automotive networks (CAN, FlexRay, Ethernet). However, there are also external security mechanisms such as MACsec which protect all communication frames (at layer 2) on an Ethernet network. The objective of this paper is to evaluate the need of SGTS in a vehicle E/E architecture. As part of the evaluation, this paper presents the experimental data to demonstrate the impact on the precision of global time with SGTS and MACsec. It describes the constraints that prevent applying the SGTS and/or MACsec on an Ethernet network. It emphasizes the tradeoff between security and precise global time when using SGTS and/or MACsec on an Ethernet network.","PeriodicalId":510086,"journal":{"name":"SAE Technical Paper Series","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141686448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kristof Hofrichter, Clemens Linnhoff, Lukas Elster, Steven Peters
In pursuit of safety validation of automated driving functions, efforts are being made to accompany real world test drives by test drives in virtual environments. To be able to transfer highly automated driving functions into a simulation, models of the vehicle’s perception sensors such as lidar, radar and camera are required. In addition to the classic pulsed time-of-flight (ToF) lidars, the growing availability of commercial frequency modulated continuous wave (FMCW) lidars sparks interest in the field of environment perception. This is due to advanced capabilities such as directly measuring the target’s relative radial velocity based on the Doppler effect. In this work, an FMCW lidar sensor simulation model is introduced, which is divided into the components of signal propagation and signal processing. The signal propagation is modeled by a ray tracing approach simulating the interaction of light waves with the environment. For this purpose, an ASAM Open Simulation Interface (OSI) object list referencing virtual 3D objects provides the input for the ray tracer. The divergence of the continuous laser beam is approximated by super-sampling the beam with multiple rays, the calculation of the received power is supported by the future ASAM OpenMATERIAL standard. Subsequently, the output of the ray tracer serves as the input of the signal processing that adapts the so-called Fourier tracing from the field of radar sensor simulation. This approach uses the range and velocity information of the individual rays to estimate the frequency spectrum of the intermediate frequency signal. A subsequent peak detection algorithm determines the output of the model, which is provided in the form of OSI lidar detections. Verification scenarios are tested to check the plausibility of the output and the source code of the signal processing is made available as open source.
为了对自动驾驶功能进行安全验证，人们正在努力通过虚拟环境中的试驾来配合真实世界中的试驾。为了能够将高度自动驾驶功能转移到模拟中，需要建立激光雷达、雷达和摄像头等车辆感知传感器的模型。除了传统的脉冲飞行时间（ToF）激光雷达外，商用频率调制连续波（FMCW）激光雷达的日益普及也激发了人们对环境感知领域的兴趣。这是由于激光雷达具有基于多普勒效应直接测量目标相对径向速度等先进功能。本研究介绍了一种 FMCW 激光雷达传感器仿真模型，该模型分为信号传播和信号处理两个部分。信号传播模型采用光线跟踪方法，模拟光波与环境的相互作用。为此，ASAM 开放式仿真接口（OSI）对象列表引用了虚拟三维对象，为光线追踪器提供了输入。连续激光光束的发散是通过多条射线对光束进行超采样近似得到的，接收功率的计算由未来的 ASAM OpenMATERIAL 标准支持。随后，射线追踪器的输出作为信号处理的输入，该信号处理采用雷达传感器模拟领域的所谓傅立叶追踪法。这种方法利用单条射线的射程和速度信息来估算中频信号的频谱。随后的峰值检测算法决定了模型的输出，输出以 OSI 激光雷达检测的形式提供。对验证方案进行了测试，以检查输出的合理性，信号处理的源代码以开放源代码的形式提供。
{"title":"FMCW Lidar Simulation with Ray Tracing and Standardized Interfaces","authors":"Kristof Hofrichter, Clemens Linnhoff, Lukas Elster, Steven Peters","doi":"10.4271/2024-01-2977","DOIUrl":"https://doi.org/10.4271/2024-01-2977","url":null,"abstract":"In pursuit of safety validation of automated driving functions, efforts are being made to accompany real world test drives by test drives in virtual environments. To be able to transfer highly automated driving functions into a simulation, models of the vehicle’s perception sensors such as lidar, radar and camera are required. In addition to the classic pulsed time-of-flight (ToF) lidars, the growing availability of commercial frequency modulated continuous wave (FMCW) lidars sparks interest in the field of environment perception. This is due to advanced capabilities such as directly measuring the target’s relative radial velocity based on the Doppler effect. In this work, an FMCW lidar sensor simulation model is introduced, which is divided into the components of signal propagation and signal processing. The signal propagation is modeled by a ray tracing approach simulating the interaction of light waves with the environment. For this purpose, an ASAM Open Simulation Interface (OSI) object list referencing virtual 3D objects provides the input for the ray tracer. The divergence of the continuous laser beam is approximated by super-sampling the beam with multiple rays, the calculation of the received power is supported by the future ASAM OpenMATERIAL standard. Subsequently, the output of the ray tracer serves as the input of the signal processing that adapts the so-called Fourier tracing from the field of radar sensor simulation. This approach uses the range and velocity information of the individual rays to estimate the frequency spectrum of the intermediate frequency signal. A subsequent peak detection algorithm determines the output of the model, which is provided in the form of OSI lidar detections. Verification scenarios are tested to check the plausibility of the output and the source code of the signal processing is made available as open source.","PeriodicalId":510086,"journal":{"name":"SAE Technical Paper Series","volume":"347 11‐13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141686872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sebastian Michael Peter Jagfeld, Richard Weldle, Rainer Knorr, A. Fill, K. Birke
The automotive PowerNet is in the middle of a major transformation. The main drivers are steadily increasing power demand, availability requirements, and complexity and cost. These factors result in a wide variety of possible future PowerNet topologies. The increasing power demand is, among other factors, caused by the progressive electrification of formerly mechanical components and a constantly increasing number of comfort and safety loads. This leads to a steady increase in installed electrical power. X-by-wire systems1 and autonomous driving functions result in higher availability requirements. As a result, the power supply of all safety-critical loads must always be kept sufficiently stable. To reduce costs and increase reliability, the car manufacturers aim to reduce the complexity of the PowerNet system, including the wiring harness and the controller network. The wiring harness e.g., is currently one of the most expensive parts of modern cars. These challenges are met with a wide variety of concepts. To fulfill the increasing power requirements, higher voltage levels can be introduced. Availability requirements can be met with redundant subnets. The complexity of the wiring harness can be reduced by employing a zonal architecture. The changes coming with the chosen topology will have a major impact on the components used in the low-voltage PowerNet and their requirements. In some cases, entirely new components will be necessary. For carmakers and suppliers, it is crucial to understand the different topologies and their implications to develop appropriate and safe components in the future. System simulations are an important tool to support these efforts. Due to the high variance of the discussed topologies and the considerable effort for building the models, we propose the implementation of a simulation toolbox featuring an automized model built-up. Here, the description and modeling of the PowerNet is based on a modular approach, which enables a rapid and efficient model built-up and simulation. This toolbox allows for a fast evaluation and quantitative comparison of different topologies.
{"title":"What is Going on within the Automotive PowerNet?","authors":"Sebastian Michael Peter Jagfeld, Richard Weldle, Rainer Knorr, A. Fill, K. Birke","doi":"10.4271/2024-01-2985","DOIUrl":"https://doi.org/10.4271/2024-01-2985","url":null,"abstract":"The automotive PowerNet is in the middle of a major transformation. The main drivers are steadily increasing power demand, availability requirements, and complexity and cost. These factors result in a wide variety of possible future PowerNet topologies. The increasing power demand is, among other factors, caused by the progressive electrification of formerly mechanical components and a constantly increasing number of comfort and safety loads. This leads to a steady increase in installed electrical power. X-by-wire systems1 and autonomous driving functions result in higher availability requirements. As a result, the power supply of all safety-critical loads must always be kept sufficiently stable. To reduce costs and increase reliability, the car manufacturers aim to reduce the complexity of the PowerNet system, including the wiring harness and the controller network. The wiring harness e.g., is currently one of the most expensive parts of modern cars. These challenges are met with a wide variety of concepts. To fulfill the increasing power requirements, higher voltage levels can be introduced. Availability requirements can be met with redundant subnets. The complexity of the wiring harness can be reduced by employing a zonal architecture. The changes coming with the chosen topology will have a major impact on the components used in the low-voltage PowerNet and their requirements. In some cases, entirely new components will be necessary. For carmakers and suppliers, it is crucial to understand the different topologies and their implications to develop appropriate and safe components in the future. System simulations are an important tool to support these efforts. Due to the high variance of the discussed topologies and the considerable effort for building the models, we propose the implementation of a simulation toolbox featuring an automized model built-up. Here, the description and modeling of the PowerNet is based on a modular approach, which enables a rapid and efficient model built-up and simulation. This toolbox allows for a fast evaluation and quantitative comparison of different topologies.","PeriodicalId":510086,"journal":{"name":"SAE Technical Paper Series","volume":"8 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：