CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)
Refine
Document Type
- Article (44)
- Conference Paper (44)
- Part of Periodical (4)
- Preprint (4)
- Book (2)
- Part of a Compilation / Book Chapter (2)
Language
- English (96)
- German (3)
- Portuguese (1)
Institute
- CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS) (100)
- Fakultät Elektro- und Informationstechnik (62)
- Fakultät Informatik (40)
- Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur" (24)
- CARISSMA Institute of Automated Driving (C-IAD) (7)
- Fakultät Maschinenbau (7)
- Human-Computer Interaction Group (HCIG) (3)
- AImotion Bavaria (2)
- CARISSMA Institute of Safety in Future Mobility (C-ISAFE) (2)
- Fakultät Nachhaltige Infrastruktur (1)
Review
- peer-review (74)
- ja (9)
- nein (8)
- editorial review (1)
Version
- published (58)
Lithium–ion batteries are well established as traction batteries for electric vehicles. This has led to a growing market for second-life batteries that can be used in applications like home energy storage systems. Moreover, the recyclability and safe handling of aged or damaged cells and packs has become more important. While there are several indicators, like state of health (SOH), state of power (SOP), or state of safety (SOS), which describe the state of a battery before its defined end of life (EOL), there is no consistent classification methodology by which to describe the usability of a cell or pack after its EOL is reached. The proposed state of usability (SOU) provides a new indicator that accounts for the usability for second life, recyclability, and possible required safety handling of a lithium–ion battery after its first intended life cycle. This work presents a decision tree method, which in turn leads to five discrete usability levels enabling a fast and rough determination of the SOU for practical use. Further, a calculation methodology for reasonable continuous regions of the SOU is proposed. Both methods are based on a literature-based rating of all of the relevant defect and aging mechanisms displayed in a risk matrix. Finally, some experimental methods that can be used for SOU determination are proposed. The developed methodology and the hands-on approach using a decision tree are well-suited for real world application in recycling companies and battery test laboratories.
Influence of Blind Spot Assistance Systems in Heavy Commercial Vehicles on Accident Reconstruction
(2024)
Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRUs) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system’s performance. The lateral distance between the right side of the commercial vehicle and the VRU, as well as obstacles between them, along with the speed difference can have great influence on the reliability of the assistance system. Depending on the concrete time of the system’s warning signal, the accident can be avoided or not by the driver when reacting to this signal.
Cybersecurity as a Service
(2023)
Towards to Battery Digital Passport: Reviewing Regulations and Standards for Second-Life Batteries
(2024)
Greenhouse gas emissions from transportation harm the environment. In response to these environmental concerns, numerous countries encourage the adoption of electric vehicles (EVs) as a more environmentally friendly option than traditional gasoline-powered vehicles. Advances in battery technology have made batteries an alternative solution for energy storage in stationary applications and for electric mobility. Reduced lithium-ion batteries (LIBs) production costs due to economies of scale, electrode material and cell design developments, and manufacturing process improvements have driven this success. This trend is expected to increase the number of LIBs on the market that may be discarded in the environment at the end of their useful life if more sustainable alternatives are not technologically mature. This coming environmental concern can be mitigated by collecting wasted EV batteries, reconfiguring them, and reusing them for applications with less stringent weight, performance, and size requirements. This method would extend battery life and reduce environmental effects. The present work investigates the main regulatory structures of the second-life battery industry that require rules, technical standards, and laws. To achieve this objective, a systematic review was carried out following a strict protocol that includes identifying relevant studies, extracting data and information, evaluating, and summarizing information. This paper explains the primary rules and technical standards governing the second-life battery business. The findings highlight the need for universities, research institutions, and government agencies to evaluate the second-life battery industry objectively. This would enable the creation of new technological regulations and laws for this burgeoning industry.
Federated learning allows for cooperative training among distributed clients by sharing their locally learned model parameters, such as weights or gradients. However, as model size increases, the communication bandwidth required for deployment in wireless networks becomes a bottleneck. To address this, we propose a residual-based federated learning framework (ResFed) that transmits residuals instead of gradients or weights in networks. By predicting model updates at both clients and the server, residuals are calculated as the difference between updated and predicted models and contain more dense information than weights or gradients. We find that the residuals are less sensitive to an increasing compression ratio than other parameters, and hence use lossy compression techniques on residuals to improve communication efficiency for training in federated settings. With the same compression ratio, ResFed outperforms current methods (weight- or gradient-based federated learning) by over 1.4× on federated data sets, including MNIST, FashionMNIST, SVHN, CIFAR-10, CIFAR-100, and FEMNIST, in client-to-server communication, and can also be applied to reduce communication costs for server-to-client communication.
Collaborative perception in automated vehicles leverages the exchange of information between agents, aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However, these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap, we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly, it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features, and (ii) compressed orthogonal attention features shared between vehicles. Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above the results from single vehicles by over 30%, and (ii) models anchored on semantic occupancy outpace state-of-the-art collaborative 3D detection techniques in subsequent perception applications, showcasing enhanced accuracy and enriched semantic-awareness in road environments.
Bird's eye view (BEV) perception is becoming increasingly important in the field of autonomous driving. It uses multi-view camera data to learn a transformer model that directly projects the perception of the road environment onto the BEV perspective. However, training a transformer model often requires a large amount of data, and as camera data for road traffic are often private, they are typically not shared. Federated learning offers a solution that enables clients to collaborate and train models without exchanging data but model parameters. In this paper, we introduce FedBEVT, a federated transformer learning approach for BEV perception. In order to address two common data heterogeneity issues in FedBEVT: (i) diverse sensor poses, and (ii) varying sensor numbers in perception systems, we propose two approaches - Federated Learning with Camera-Attentive Personalization (FedCaP) and Adaptive Multi-Camera Masking (AMCM), respectively. To evaluate our method in real-world settings, we create a dataset consisting of four typical federated use cases. Our findings suggest that FedBEVT outperforms the baseline approaches in all four use cases, demonstrating the potential of our approach for improving BEV perception in autonomous driving.
Partially automated driving functions (SAE Level 2) can control a vehicle’s longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver’s ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The results show that none of the subjects could avoid leaving their lane and moving into the oncoming lane and, therefore, could not control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 s. The second scenario examined driver responses to phantom braking caused by AEBS. We found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered.
Partially automated driving functions (SAE Level 2) can control a vehicle's longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver's ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The second scenario examined driver responses to phantom braking caused by the AEBS. As a result, the first scenario shows that none of the subjects could control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 seconds. In the second scenario, we found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered.
Vehicles and road infrastructure are starting to be equipped with vehicle-to-everything (V2X) communication solutions to increase road safety and provide new services to drivers and passengers. In Europe, the deployment is based on a set of Release 1 standards developed by ETSI to support basic use cases for cooperative intelligent transport systems (C-ITS). For them, the capacity of a single 10 MHz channel in the ITS band at 5.9 GHz is considered sufficient. At the same time, the ITS stakeholders are working towards several advanced use cases, which imply a significant increment of data traffic and the need for multiple channels. To address this issue, ETSI has recently standardized a new multi-channel operation (MCO) concept for flexible, efficient, and future-proof use of multiple channels. This new concept is defined in a set of new specifications that represent the foundation for the future releases of C-ITS standards. The present paper provides a comprehensive review of the new set of specifications, describing the main entities extending the C-ITS architecture at the different layers of the protocol stack, In addition, the paper provides representative examples that describe how these MCO standards will be used in the future and discusses some of the main open issues arising. The review and analysis of this paper facilitate the understanding and motivation of the new set of Release 2 ETSI specifications for MCO and the identification of new research opportunities.
Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services. Federated learning (FL) overcomes privacy constraints by training ML models in distributed systems, exchanging model parameters instead of raw data. However, the dynamic states of connected vehicles affect the network connection quality and influence the FL performance. To tackle this challenge, we propose a contextual client selection pipeline that uses Vehicle-to-Everything (V2X) messages to select clients based on the predicted communication latency. The pipeline includes: (i) fusing V2X messages, (ii) predicting future traffic topology, (iii) pre-clustering clients based on local data distribution similarity, and (iv) selecting clients with minimal latency for future model aggregation. Experiments show that our pipeline outperforms baselines on various datasets, particularly in non-iid settings.
Simulation-Based Performance Optimization of V2X Collective Perception by Adaptive Object Filtering
(2023)
The efficiency of radio resource allocation and scheduling procedures in Cellular Vehicle-to-X (Cellular V2X) communication networks directly affects link quality in terms of latency and reliability. However, owing to the continuous movement of vehicles, it is impossible to have a centralized coordinating unit at all times to manage the allocation of radio resources. In the unmanaged mode of the fifth generation new radio (5G-NR) V2X, the sensing-based semi-persistent scheduling (SB-SPS) loses its effectiveness when V2X data messages become aperiodic with varying data sizes. This leads to misinformed resource allocation decisions among vehicles and frequent resource collisions. To improve resource selection, this study formulates the Cellular V2X communication network as a decentralized multi-agent networked markov decision process (MDP) where each vehicle agent executes an actor-critic-based radio resource scheduler. Developing further the actor-critic methodology for the radio resource allocation problem in Cellular V2X, two variants are derived: independent actor-critic (IAC) and shared experience actor-critic (SEAC). Results from simulation studies indicate that the actor-critic schedulers improve reliability, achieving a 15-20% higher probability of reception under high vehicular density scenarios with aperiodic traffic patterns.
Cooperative Intelligent Transport Systems have been deployed in mass-produced vehicles in Europe (Golf 8, ID.3, etc.) since 2019, promising major improvements in vehicle safety, efficiency, and comfort. Equipped with Vehicle-to-X communication, the vehicles directly send information about their driving status to the environment. The data are already used for many Vehicle-to-X communication use-cases specified by the Car-2-Car Communication Consortium to achieve the above-mentioned improvements. This study focuses on using Vehicle-2-X communication and its messages for accident analysis. For this purpose, we investigated the advantage in temporal resolution of the continuously sent Cooperative Awareness Message compared to the Event-Data-Recorder for accident reconstruction in the pre-crash. A high resolution of the temporal accident scene is essential for the doubtless clarification of accidents. Therefore, we analyzed the generation frequencies and the trigger conditions of the Cooperative Awareness Message of vehicles available on the European market while driving in real traffic and during different parking maneuvers. Also, the pseudonym change was analyzed. We found that the average generation frequency of the Cooperative Awareness Message is higher than the 2.00 Hz recording frequency of the event data recorder for all scenarios investigated. Especially on rural roads and highways, the temporal resolution is advantageous. Since many serious accidents occur on rural roads, the use of Cooperative Awareness Messages could significantly benefit the reconstruction of the accident scene. Thus, we recommend increasing efforts to use the Cooperative Awareness Message as digital data for accident analysis and encourage more scientists to deal with this topic.
Radio Resource Allocation for Collective Perception in 5G-NR Vehicle-to-X Communication Systems
(2023)
The deployment of battery-powered electric vehicles in the market has created a naturally increasing need for the safe deactivation and recycling of batteries. Various deactivating methods for lithium-ion cells include electrical discharging or deactivation with liquids. Such methods are also useful for cases where the cell tabs are not accessible. In the literature analyses, different deactivation media are used, but none include the use of calcium chloride (CaCl2) salt. As compared to other media, the major advantage of this salt is that it can capture the highly reactive and hazardous molecules of Hydrofluoric acid. To analyse the actual performance of this salt in terms of practicability and safety, this experimental research aims to compare it against regular Tap Water and Demineralized Water. This will be accomplished by performing nail penetration tests on deactivated cells and comparing their residual energy against each other. Moreover, these three different media and respective cells are analysed after deactivation, i.e., based on conductivity measurements, cell mass, flame photometry, fluoride content, computer tomography and pH value. It was found that the cells deactivated in the CaCl2 solution did not show any signs of Fluoride ions, whereas cells deactivated in TW showed the emergence of Fluoride ions in the 10th week of the insertion. However, with the addition of CaCl2 in TW, the deactivation process > 48 h for TW declines to 0.5–2 h, which could be an optimal solution for real-world situations where deactivating cells at a high pace is essential.
Small unmanned aerial vehicles are a threat for manned aviation. Their increased use by hobby pilots and within the commercial sector has been accompanied by an increase in incidents involving manned aircraft. The problem is that current aircraft structures are designed to resist collisions with birds. They are not designed to withstand drone impacts. The composition of drones differs significantly from previously known load cases. Drones consist of several components with various materials. This means, that there is no analytic model to determine the impact force of such drone strikes with aircraft structures. Within this work, a novel reduced order model for drone impacts is developed. It is validated with high velocity impact test data and explicit finite element simulations. The impact of fragmenting components of the drone are modelled with the aircraft impact model. The impact of non-fragmenting components like motors are described with a spring-mass model. The results show that the approach of superimposing a spring-mass model with the aircraft impact model leads to good results. In case of a rigid target only minor deviations occur within the validity range of the model. Damage and degradation of the target is not included in the model what leads to larger deviations in case of an impact with deformable structures. Nevertheless, the model is very well suited for rapid load estimation and can qualitatively reproduce contact force curves. It can be used for preliminary design of aircraft structures without conducting time and cost intensive tests and simulations.
Inclusive Security by Design
(2022)
Structured methodology and survey to evaluate data completeness in automotive digital forensics
(2021)
A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 kmh (0.046 ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 kmh (0.24 ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments.
In this analysis, Cooperative Intelligent Transportation System relevant scenarios are created to investigate the need to differentiate Vehicle-to-X transmission technologies on behalf of accident analysis. For each scenario, the distances between the vehicles are calculated 5 s before the crash. Studies on the difference between Dedicated Short-Range Communication (IEEE 802.11p) and Cellular Vehicle-to-X communication (LTE-V2C PC5 Mode 4) are then used to assess whether both technologies have a reliable connection over the relevant distance. If this is the case, the transmission technology is of secondary importance for future investigations on Vehicle-to-X communication in combination with accident analysis. The results show that studies on freeways and rural roads can be carried out independently of the transmission technology and other boundary conditions (speed, traffic density, non-line of sight/line of sight). The situation is different for studies in urban areas, where both technologies may not have a sufficiently reliable connection range depending on the traffic density.
Calculating Available Charge and Energy of Lithium-Ion Cells Based on OCV and Internal Resistance
(2022)
The design and operation of performant and safe electric vehicles depend on precise knowledge of the behavior of their electrochemical energy storage systems. The performance of the battery management systems often relies on the discrete-time battery models, which can correctly emulate the battery characteristics. Among the available methods, electric circuit-based equations have shown to be especially useful in describing the electrical characteristics of batteries. To overcome the existing drawbacks, such as discrete-time simulations for parameter estimation and the usage of look-up tables, a set of equations has been developed in this study that solely relies on the open-circuit voltage and the internal resistance of a battery. The parameters can be obtained from typical cell datasheets or can be easily extracted via standard measurements. The proposed equations allow for the direct analytical determination of available discharge capacity and the available energy content depending on the discharge current, as well as the Peukert exponent. The fidelity of the proposed system was validated experimentally using 18650 NMC and LFP lithium-ion cells, and the results are in close agreement with the datasheet.
Possibilities for a Quick Onsite Safety-State Assessment of Stand-Alone Lithium-Ion Batteries
(2022)
Electric vehicles’ high-voltage lithium-ion batteries are complex systems and can be sources of several hazards for interacting people. Sophisticated battery management systems (BMS) therefore constantly monitor their characteristics and varying states, to keep the battery within desired operational conditions and to mitigate safety risks as well as excessive degradation. However, there can be several situations where the battery is not in normal operation (e.g., a stand-alone battery) and a fully functional BMS monitoring function is not available. When necessary to interact with the system, its safety state must be deduced to ensure the safety of interactors. This can be a challenging task depending on a situation’s characteristics (time pressure, technical knowledge of involved people). Thus, this article discusses how the safety state of electric vehicle batteries can be evaluated quickly even by untrained people. To develop a solution, different scenarios, which require a battery’s state assessment, and the options for collecting relevant information are motivated and discussed, respectively. Finally, a mobile interface that can evaluate and display the safety state by using BMS-internal data is described and demonstrated.