International Automotive Engineering (M. Eng.)
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A simulation study to analyse the impact of V2X communication on the emergency vehicle response time
(2024)
Focused on the crucial intersection of urban mobility and public safety, the thesis investigates the practical implications of integrating V2X technology for the streets of Ingolstadt. This study employs simulation techniques to mimic real-world situations, evaluating the tangible benefits of V2X-enabled vehicles in speeding up emergency arrival time.
By connecting theory with simulations, this work aims to guide policymaking and technology development dept. for improved emergency response systems, for the roads of Ingolstadt.
The increasing demand for electric vehicles EV in recent years has led to a growing need for advanced BMS that can accurately estimate the state of health SOH of batteries. The SOH is a critical parameter that determines the performance and lifespan of batteries, and accurate estimation of these parameters is essential for optimizing battery utilization and improving the overall efficiency and reliability of EV. Accurately estimating the SOH of batteries in real driving conditions is a challenging task due to the dynamic nature of driving cycles, which can cause significant variations in battery behavior. Moreover, the accuracy of existing estimation techniques is often affected by factors such as battery degradation, temperature variations, and non-linearities in battery behavior.
To address these challenges, researchers and engineers have developed a wide range of techniques and algorithms for estimating the SOH of batteries in real driving conditions. These techniques include model-based approaches, data-driven methods, and hybrid techniques that combine both model-based and data-driven approaches. The objective of this master thesis is to critically review the existing literature on estimation techniques for SOH in real driving cycles, identify the strengths and limitations of different approaches, and propose a novel estimation technique that can overcome the limitations of existing approaches. The proposed technique will be evaluated using real-world data obtained from a test vehicle.
It is vital to do a precise assessment of the condition of these batteries in order to guarantee that they can be used safely and to prevent explosions that may possibly be catastrophic. The challenges that were discussed before could be solved with the assistance of prediction models. The purpose of this research is to evaluate the accuracy of predictions made by a variety of machine learning algorithms on the state of the battery. In order to achieve this result, time series forecasting techniques are used to data metrics. It was shown that Long Short-Term Memory LSTM models perform very well when it comes to the creation of forecasts that can be relied upon. An accurate forecast made with the aid of machine learning models may assist in increasing sales of electric vehicles and ensuring that these batteries are used in a secure manner.
This paper delves into the integration of Artificial Intelligence (AI) within the infotainment industry and its impact on privacy concerns. The rise of personalized experiences in infotainment has transformed user engagement by offering tailored recommendations across various content categories such as news, music, videos, and social media updates. However, the continuous gathering of personalized data by AI raises privacy issues. Despite these concerns, AI algorithms analyze user preferences, behaviors, and demographics to provide personalized content, improving user satisfaction and interaction. While some regions have implemented regulations to protect user privacy, finding a balance between the benefits of AI, user expectations, and privacy protection presents a challenge. Enhancing transparency, user control, and consent procedures are essential to ensure the ethical use of AI systems and safeguard user information. Recognizing the potential risks associated with AI-driven infotainment allows stakeholders to collaborate on a comprehensive framework that maximizes the advantages of AI while preserving privacy.
Traffic accidents remain a major global health concern, with millions losing their lives and many more suffering life-altering injuries each year. Especially the delay in emergency services' arrival at the accident scene, caused by various factors, can significantly impact the outcome for the victims. Hence, prompt help from bystanders can save lives and decrease damage to health in the case of an accident. Incorporating innovative systems can help improve response time and provide rapid and reliable access to first aid at the crash scene, ultimately contributing to reducing fatalities and disabilities resulting from road traffic accidents.
This thesis presents the development of a first aider request system that is activated by the eCall, automatically identifying and calling first aiders to the accident scene. The system is designed for usage in vehicles and notifies nearby first aiders via smartphone application or in-vehicle infotainment system. The core of this thesis delves into the design, development, and rigorous evaluation of this system. It explores various technical considerations, system configurations, and task distribution strategies, ensuring optimal system functionality and efficiency. By incorporating an intuitive smartphone application and seamless in-vehicle integration, the system prioritizes accessibility and ease of use for potential first aiders.
The results hold promise for enhancing post-crash response. By harnessing the help of nearby first aiders, the system has the potential to reduce casualties, improve patient outcomes, and ultimately save lives. The findings presented in this thesis demonstrate the first aider request system's valuable contribution to advancing road safety and lifesaving efforts.
In scalable traffic analysis for smart city applications and the increasing trend of autonomous driving in the foreseeable future, the implementation of multiple sensor systems for environmental perception will become essential. With the possibility of raw data transmission using 5G communication technology, a centralized approach using sensor data fusion of multiple sensors has been implemented to provide better collaborative environmental perception in infrastructure. Multi-target tracking plays an important role in environmental perception and has attracted enormous interest and effort in the research community resulting in approaches with different benefits and shortcomings. The thesis focuses on the implementation and evaluation of different types of model-based approaches for data association problems, namely Global Nearest Neighbour (GNN), Joint Probabilistic Data Association (JPDA) and Multiple Hypothesis Tracking (MHT) in the context of centralized multi-target tracking. The thesis presents methods for data processing and measurement mapping from sensor coordinates to world coordinates. The thesis also presents the use of homography to determine the position and orientation of the sensors. The implementation of the trackers is conducted in MATLAB and the evaluation of the trackers is performed using GOSPA metrics. The results indicate that GNN achieves optimal performance when targets are spatially distant and is computationally the most cost effective. JPDA demonstrates superior efficacy when targets are in close proximity. Overall, the performance of MHT is compromised by its computational expense.
The thesis explores the critical role of advanced testing methodologies in enhancing the performance, safety, and reliability of autonomous vehicle electronics. It delves into various testing techniques like Hardware-in-the-Loop (HIL), Software-in-the-Loop (SIL), and Model-in-the-Loop (MIL) testing, among others, and their application in ensuring the dependability of these systems. The study also addresses the challenges in testing autonomous vehicles, such as real-world scenario simulation and scalability, and proposes a comprehensive testing framework. It includes case studies to demonstrate the application of these methods and discusses the implications for autonomous vehicle safety. The thesis aims to establish a roadmap for integrating advanced testing methods, contributing to safer and more effective autonomous driving technology.
Hochdynamisches Fahrdynamikregelsystem für ein allradgetriebenes Formula Student Electric Fahrzeug
(2023)
Die Formula Student ist ein internationaler Designwettbewerb, bei dem studentische Teams ein einsitziges Rennfahrzeug entwickeln, auslegen, fertigen und auf verschiedenen Events gegeneinander antreten lassen. Ein beliebtes Antriebskonzept bei diesen Fahrzeugen ist das einzelne Antreiben der Räder mit 4 Elektromotoren. Auch der SRe23, das Fahrzeug des Schanzer Racing Electric e.V., verfügt über dieses Antriebskonzept.
Um die dadurch entstehenden Freiheiten in der Drehmomentverteilung bestmöglich auszunutzen wird in dieser Arbeit ein System entworfen, welches auf Basis des Reifenschlupfausnutzungskoeffizient und der Fahrzeuggierrate die Drehmomente verteilt.
Als Basis für die Berechnung der Schlupfwerte und der Sollgierrate werden Fahrzeuggeschwindigkeit und -Schwimmwinkel benötigt. Zur Ermittlung dieser Eingangsgrößen werden verschiedene Systeme entworfen, simuliert und im Fahrzeug getestet. Ein auf Basis eines Extended Kalman Filter mit GNSS und Inertialsensorik als Eingangsgrößen basierendes System hat zwar in der Simulation gute Ergebnisse geliefert, diese haben sich allerdings nicht in den Fahrversuchen reproduzieren lassen. Ein System auf Basis der Raddrehzahlen hingegen erwies sich als robust und funktional.
Das Gesamtsystem wird mit in der Fahrdynamik üblichen, als auch Formula Student spezifischen Manövern, in Simulation und Fahrversuch untersucht und eine Bewertung vorgenommen. Dabei konnte eine Verbesserung der Fahrdynamik und eine um 2,73 % respektive 2,34 % verbesserte Rundenzeit in den Disziplinen Skidpad und Autocross erzielt werden.
The motivation behind this thesis is to investigate the potential of machine learning technique, specifically neural networks and Reinforcement Learning (RL) that can by-self learn to improve vehicle handling. With the advancement in machine learning techniques, RL has gained interest in recent years in the field of vehicle controls due to its ability in effectively handling complex tasks. The All-Wheel Steering (AWS) has shown potential in enhancing the vehicle stability and maneuverability by individually steering each wheel. The primary goal of this thesis is to leverage Deep Reinforcement Learning (DRL) to further optimize the AWS control. The algorithm was trained and evaluated in a virtual environment by altering the vehicle control module imported into the CarMaker for Simulink interface. This algorithm observed vehicle states and steering input to learn best action which regulates the steering at rear axle in addition to front wheel steering. The results from this study demonstrated that the trained RL agent led to decrease in vehicle sideslip, average lateral acceleration on vehicle and deviation from path compared to an existing control method without requiring knowledge of the model. Two different reward function were considered to investigate the learning behavior. When a function was guided to minimize the lateral velocity, it was observed that the trained agent in training scenario resulted in almost 6 times lower average vehicle sideslip than with the rear wheel steer controller from a state-of-the-art model.
Simulating the virtual environment for autoware based automated driving vehicle in IPG CarMaker
(2023)
In the rapidly changing Automotive industry, particularly in the areas of Automated Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS), the demand for effective virtual testing methods has increased significantly. Manual testing of automated vehicles, such as ANTON, on proving grounds needs remarkable costs and triggers substantial safety risks. To tackle such challenges, the study commenced by conducting a comprehensive analysis of different co-simulation platforms. Consequently, Autoware.AI-Carla has been identified as the leading co-simulator, while CarMaker was distinguished for its exceptional vehicle dynamics simulation capabilities. The extensive evaluation of the Aslan-Carmaker project identified complexities and challenges encountered during the reconstruction, which provided a solid foundation for the Autoware.AI-Carmaker project. With this detailed understanding, The primary objective of this research was to create a reliable ROS bridge between Autoware.ai and IPG CarMaker, enabling efficient interaction. Even
in its early stages of development, the bridge has shown its potential by successfully controlling the Car in the CarMaker simulation through Autoware’s ’control’ tab. After rigorous research, this thesis has successfully achieved all its objectives, providing an authoritative resource for future research endeavours. This research not only demonstrates the potential of virtual testing in automated driving but also establishes a connection between testing platforms for seamless
communication, paving the way for future advancements in the field.
Remarkable progress has recently occurred in the field of reinforcement learning (RL), especially with the development of groundbreaking deep reinforcement learning (DRL) solutions. DRL demonstrates outstanding potential in automating complex decision-making and control tasks, such as autonomous driving and robotics manipulation. However, numerous challenges persist, necessitating further research to effectively apply these methods in specific use cases. One of the major challenges in deploying DRL methods in safety-critical real-world settings is ensuring the system's reliability in handling various situations encountered during production use.
This thesis focuses on investigating the behavior of a self-driving vehicle (SDV) trained using the Proximal Policy Optimization (PPO) algorithm and ensemble learning. We conducted the training of the SDV using DriverGym, an open-source OpenAI Gym-compatible environment designed for developing RL algorithms for autonomous driving with real-world scenarios. Our approach involved an ensemble-based DRL strategy using PPO. We aimed to investigate whether we could improve the model's performance and estimate epistemic uncertainty with the ensemble-based model. We explored the creation of an ensemble of multiple policies using a snapshot ensemble technique, without incurring additional training costs. Additionally, we also conducted a few experiments with an ensemble using the traditional random initialization approach.
Compared to the performance of a single member, in terms of the average displacement error, the snapshot-based ensemble model generally exhibited improvements. Additionally, it provided a means to estimate epistemic uncertainty, allowing for the identification of out-of-distribution (OOD) driving scenes that the SDV had not encountered during training. Initial results from a traditional ensemble using random initialization suggest that the increased diversity this approach can offer may lead to a better estimate of epistemic uncertainty.
One potential application of this ensemble-based uncertainty estimation approach is the identification of challenging driving scenarios through virtual simulators, which can be valuable for validating SDV planning and decision-making models. Another important application is to provide the trained system with enhanced confidence in handling previously unseen situations, especially in safety-critical contexts.
We expect that our study and approach will contribute to advancing DRL models for autonomous systems, providing valuable insights and inspiring future research, particularly to enhance the capabilities and reliability of autonomous driving decision-making systems.
With the perpetual advancements in the field of automated driving, the first and foremost concern with regard to the acceptance of the technology by the general public is safety assurance. Safety assurance can only be established by displaying the consistency and reliability of the technology, proving that the manufacturer has taken all possible actions to mitigate the failures that might arise from the uncertainties and randomness that are introduced into the system. Statistical quantification of risks arising from potential failures is a proven metric for safety conformity as well as to determine acceptable safety levels. This thesis focuses on determining the overall injury risk by simulation of lane-keeping failure in an automated vehicle. A highly parameterized traffic model capable of simulating the interactive behavior of the automated vehicle along with other road users in normal highway traffic conditions is described in this thesis work. The overall injury risk is then determined by simulating the parameterized traffic model based on Monte Carlo simulation methodology, such that the traffic model can simulate the failure in nearly every possible traffic scenario, in concurrence with the likelihood of each state of the individual parameters that define a traffic scenario. Quantification of the risk of automated driving functions is important for safety validation, and it also provides statistically significant insights for defining the tolerable limits for a functional failure.
Nowadays electric scooters are adopted as an eco-friendly mode of transportation worldwide. This increase in usage of e-scooters have critical impact on traffic safety. Currently many researches are done regarding traffic safety and researchers are trying to simulate the actual traffic. This has led to an increased demand for accurate e-scooter trajectory data. However, verifying the reliability and accuracy of e-scooter trajectory data creates a significant challenge due to the absence of a standardized reference vehicle and ground truth data. The focus of this thesis is to address this gap by proposing the design, implementation, and application of an e-scooter reference vehicle to record ground truth data.
The research begins by examining existing methods for recording the trajectory data and find their limitations and identifying the absence of ground truth data. To overcome these challenges, a novel e-scooter reference vehicle is developed, integrating various sensors, such as inertial measurement unit (IMU) and high precision GNSS, to capture accurate trajectory information. The implementation phase focuses on the construction and calibration of the e-scooter reference vehicle. Special attention is given to sensor integration, programming, and calibration techniques to ensure accurate and synchronized data collection. The developed system is tested in various real-world scenarios to evaluate its performance and reliability. An experiment was conducted in which the reference vehicle was driven through an inner-city intersection.
This intersection is equipped with an installed bird’s eye view camera that can generate trajectory data of road users. The generated trajectory data from the camera was compared with the ground truth data from the test vehicle to find anomalies and inaccuracies. In conclusion, the results of this research have a contribution on micro-mobility research and in this context on the development and improvement of traffic safety, urban planning and traffic research.
The Thesis focuses on tracking of multiple objects with fusion of sensor data from multiple Vision Cameras and Radars. The Vision Cameras (Monocular camera) and Radars has different working principles, and both sensors have their own key advantages. The camera has less processing power and high frequency rate compared to Radar, but the Radar has its advantages in different weather conditions compared to Camera. Vison Camera sensor is well suited for classification of Objects and can process 2D images. Both these sensors play key role in Autonomous vehicle perception.
Autonomous vehicle testing on road is expensive, need more human effort, time consuming and some scenarios are difficult to test. Testing of performance of sensors and algorithms can be done virtually using simulators. Real time maps are imported into MATLAB and scenarios are created above those maps and performance are tested. Virtual Simulations can be repeated several times, which is cost effective and less time consuming compared to on-road testing.
First part of work is on individual tasks like selection of different tracking algorithms, clustering algorithms, different procedures available for processing the raw sensor data from Radar. Selection of different sensor mounting positions on Ego vehicle with 360-degree field of view coverage. Build the functions required for processing the sensor data from Radar and other functions like multiple objects tracking etc. Creation of high-speed and low speed scenarios with multiple objects around Ego Vehicle.
Second part of work is to analyse the behaviour of the object track predictions in blind spots with high speed and low speed scenarios. Analysis of object tracks comparison with ground truth, where there is misalignment of sensor position. Analysing the behaviour of object tracks between the high speed and low speed scenarios with given gaps within 360-degree field of view. This type of analysis with KPIs are created to compare the performances of algorithm with different scenarios and with changes in sensor coordinate frames.
Remaining useful life prediction for lithium-ion batteries using time series forecasting models
(2023)
Automobiles are an important part of modern civilization, offering a variety of functions such as passenger and freight transportation, travel and delivery services, and emergency response. The vast majority of vehicles on the road today are fuelled by hydrocarbon fuels, specifically gasoline, and diesel.
With regard to air pollution, global warming, and the depletion of fossil fuel reserves, the use of hydrocarbon fuels to power electric vehicles as a result, greenhouse are released. So, there has been a discernible increase in the production of environmentally friendly, alternative automobiles that are powered by green energy. Electric vehicles typically utilize lithium-ion batteries (LIBs) as a substitute fuel source to produce electricity. Although LIBs are a desirable alternative to conventional hydrocarbon fuels, their longevity is constrained, and they should not be used past the point at which they are on the verge of losing their usefulness. An accurate evaluation of these batteries' health is necessary to assure their safe use and prevent potentially disastrous battery explosions.
Prediction models could help solve the previously mentioned problems. This study's objective is to assess how well various machine learning algorithms can foretell battery health. In order to get this outcome, time series forecasting techniques are used to application metrics, and it was discovered that Long Short-Term Memory (LSTM) models were effective at creating forecasts that could be relied upon. The LSTM model outperformed all other models on various metrics.
Accurate prediction using machine learning models can help boost the sales of EVs and ensure the safe usage of these batteries. This can significantly contribute to reducing air pollution, combating global warming, and preserving fossil fuels for future generations.
Detecting the unexpected
(2023)
The Automotive Industry has been investing vast resources towards the development of Automated Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS) in recent years. The competition between different Automobile OEMs in this direction has led to the fast growing research in both the development and validation of such complex systems. This evolution of mobility has been obviously reflected in the adaptation of many academic related automotive studies, with a growing focus on the technology and science assisting in the engineering of these systems, rather than the saturated knowledge in the scope of conventional automotive systems. On another side, and to cope with this huge expansion, different standards are evolving to support and collaborate the process of developing ADS and ADAS, shedding light on what the future of mobility would look like.
Without the proper validation and testing of released ADS and ADAS functions, they cannot reach the state of deployment in production level. Considering the different validation platforms in a test environment and the enormous number of test cases, test automation tools have been used to facilitate the process of testing, assigning verdicts and analysing the trace outputs of a test case execution. These tools are even capable of exporting test reports to databases for monitoring of the complete test process and providing feedback against test management tools. However, it has been highly inefficient and slow to have a custom implementation of test cases for a specific platform. Thus, it has always been fundamental to have test cases on an abstract level from the test environment platform, giving the opportunity to reuse test cases on different validation stages and cross checking their verdicts, which in return improves the tracking of defects and reporting them to the function developers.
The current validation process of ADS and ADAS starts with Hardware in the Loop (HiL) test environment using Tracetronic test automation tool (ECU-Test). A test management tool provides a database of test cases in a human readable form. According to the Test Design Specification (TDS) for the validation of a specific software release, only the required human readable test cases are used for creating ECU-Test runnable test cases (test packages).
In this work, a Software in the Loop (SiL) test environment with a different toolchain is utilized and made compatible for testing with ECU-Test test cases reusing the same test artifacts, i.e. scnearios, roads and catalogs, used for testing on HiL test environment. The SiL environment consists of all the required models, i.e. sensors’ models and vehicle dynamics model, in addition to the System Under Test (SUT) and the environment simulator. In an aim of having the test cases in an absolute abstract form, apart from the test platform, the concept of keyword-based test steps is embraced for creating test cases without any dependency to the test platform, i.e. HiL and SiL platforms. Furthermore, SiL implementation packages with SiL specific test steps are developed for mapping against their respective keywords. Finally, results from the developed abstract test case executed on the SiL platform are compared to the ones available from the existing HiL test case with the same test artifacts.
Out of scope for this research and future work includes the creation of HiL specific implementation packages for linking with the abstract test case, hence executing the same abstract test case developed in this work on the HiL platform.
On May 25th, 2018 -- The General Data Protection Regulation (EU) 2016/679 was implemented in order to secure the handling process of EU citizens' data by removing unauthorized disclosure of personal data without the referred individual's consent for general use. Modern computer vision technology can successfully blur images exposing faces or vehicle plates using deep learning convolutional neural networks (DLCNNs) via Graphics Processing Units (GPUs). However, due to the generic nature of the GPU processor, the digital routing is not optimized to efficiently process-specific application uses, such as the CNN algorithm. The goal of this research is to create a digital integrated circuit using a Field-programmable gate array (FPGA) implementing strategic functions of synthesizable parts of the You Only Look Once (YOLO) v4, OpenPose, and PoseNet algorithms. Optimization of the already extant GPU implemented Convolutional Neural Networks (CNNs) in terms of resource consumption using the FPGA device structure is implemented. Comparisons are carried out considering frame rate, latency, and energy consumption, for the following processing devices: the Intel® Core™ i7-10750H Processor Central Processing Unit (CPU), GeForce® Giga Texel Shader eXtreme (GTX) 1660 Ti, and GeForce® Ray Tracing Texel eXtreme (RTX) 2080 Graphics Processing Units (GPUs) and a ZCU104-XCZU7EV-2FFVC1156 MPSoC FPGA. The different neural network models are implemented from floating to a fixed point onto the cited platforms. Object detection CNNs are evaluated using the mean Average Precision (mAP) metric. Pose algorithms are evaluated using Object Keypoint Similarity (OKS). A network pruning algorithm has been developed that can, with a 50 % compression rate, improve the frame rate of the YOLO V4 algorithm from 4 FPS (CPU) and 45 FPS (GPU) to 179 FPS on the FPGA while reducing the energy consumption by 50.8\% (1.192 mJ/Frame) compared to the tested CPU (2.423 mJ/Frame) and a 32.4 % (1.763 mJ/Frame) means compared to the GPU with a slight accuracy loss. Using the aforementioned metrics and normalized accuracy, PoseNet has obtained a mean performance of 300 FPS, against 37 FPS on the CPU and 128 FPS on the GPU, and obtaining respectively 61.1 % and 51.9 % (0.429 mJ/Frame) energy consumption reductions related to the CPU (1.104 mJ/Frame) and GPU (0.872 mJ/Frame) implementations, respectively. The developed work proves that implementing the tested CNNs on FPGA devices by designing a customized logic circuit can significantly increase performance in terms of surface occupation, speed, and energy consumption; and reduce overall costs in project development.
The demand for Electric Vehicle (EV) is growing drastically such that even in pandemic times its sales percent doubled. The reasons for this include the need for reduction of CO2 emission to protect environment and the norms made by many countries to restrict the consumption of fossil fuels. This means the demand for EV will keep increasing in future also. To meet the demand and to hold the market position, it is necessary for engineers and automobile manufacturers to improve the EV in all possible aspects. One such important aspect is safety. Safety is always an important concern for EVs and hence several safety standards and regulations has been imparted on EV and its energy storage system- Batteries. One such safety standard GB38031 implemented on 2020 demands that there should be a 5-minute warning time for passengers when there is an event of thermal runway in an EV. We have to come up with a new idea to meet this standard. Since this safety standard demands safety criteria after the event of a failure, the area of research chosen in this thesis will be in the field of thermal insulation materials for traction batteries. Finding a new thermal insulation material for application in traction battery is the research approach of this thesis. The research started with analysis of thermal insulation materials used currently in traction battery thermal insulation application. By analyzing the behavior of influential parameters, new insulation materials are chosen that are either used in other industrial applications like construction, refractories etc. or which patented newly. The condition kept was it should not be used for thermal insulation application in traction battery. Based on research, list of few materials was chosen. The materials are either naturally occurred or artificially developed. The simulation analysis of all material based on the data acquired was done. Based on its result and the availability of raw materials experiment analysis of one material Rice Husk Ash (RHA) was done. RHA is a naturally occurring agricultural waste produced during cultivation of paddy. The rice husk is the base material of RHA. Their results tend to show excellent thermal behavior such that the performance of thermal insulation of this material seems to be better that 75 % of the currently used thermal insulation materials. The process of producing it is simple, economical and scope of further research is more for this material. The intention of this thesis is to find any new material that has never been used before in traction battery insulation and to find the suitability of that material for this application by means of its good thermal insulation performance. The outcome of the thesis is a new material with good suitability to traction battery application was found and analyzed. This material RHA also has good scope on further research to find the full potential or to extend its limit in traction battery application which is also discussed in this thesis.