Influence of Blind Spot Assistance Systems in Heavy Commercial Vehicles on Accident Reconstruction.

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.

Influences of Vehicle Communication on Human Driving Reactions: A Simulator Study on Reaction Times and Behavior for Forensic Accident Analysis.

Cooperative intelligent transport systems (C-ITSs) are mass-produced and sold in Europe, promising enhanced safety and comfort. Direct vehicle communication, known as vehicle-to-everything (V2X) communication, is crucial in this context. Drivers receive warnings about potential hazards by exchanging vehicle status and environmental data with other communication-enabled vehicles. However, the impact of these warnings on drivers and their inclusion in accident reconstruction remains uncertain. Unlike sensor-based warnings, V2X warnings may not provide a visible reason for the alert, potentially affecting reaction times and behavior. In this work, a simulator study on V2X warnings was conducted with 32 participants to generate findings on reaction times and behavior for accident reconstruction in connection with these systems. Two scenarios from the Car-2-Car Communication Consortium were implemented: "Stationary Vehicle Warning-Broken-Down Vehicle" and "Dangerous Situation-Electronic Emergency Brake Lights". Volkswagen's warning concept was utilized, as they are the sole provider of cooperative vehicles in Europe. Results show that V2X warnings without visible reasons did not negatively impact reaction times or behavior, with average reaction times between 0.58 s (steering) and 0.69 s (braking). No significant distraction or search for warning reasons was observed. However, additional information in the warnings caused confusion and was seldom noticed by subjects. In this study, participants responded correctly and appropriately to the shown false-positive warnings. A wrong reaction triggering an accident is possible but unlikely. Overall, V2X warnings showed no negative impacts compared with sensor-based systems. This means that there are no differences in accident reconstruction regarding the source of the warning (sensors or communication). However, it is important that it is known that there was a warning, which is why the occurrence of V2X warnings should also be saved in the EDR in the future.

Analysis of Deactivation of 18,650 Lithium-Ion Cells in CaCl2, Tap Water and Demineralized Water for Different Insertion Times.

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.

Impact of Partially Automated Driving Functions on Forensic Accident Reconstruction: A Simulator Study on Driver Reaction Behavior in the Event of a Malfunctioning System Behavior.

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.

C-ITS Relevant Critical Vehicle-to-Vehicle Accident Scenarios for Accident Analysis.

The relevance of scientific investigations, whether simulative or empirical, is strongly related to the environment used and the scenarios associated with it. Within the field of cooperative intelligent transport systems, use-cases are defined to describe the benefits of applications. This has already been conducted in the available safety-relevant Day 1 applications longitudinal and intersection collision risk warning through the respective technical specifications. However, the relevance of traffic scenarios is always a function of accident severity and frequency of a retrospective consideration of accident databases. In this study, vehicle-to-vehicle scenarios with high frequency and/or severe personal injuries are therefore determined with the help of the CISS database and linked to the use-cases of the safety-relevant Day 1 applications. The relevance of the scenarios thus results on the one hand from the classical parameters of retrospective accident analysis and on the other hand from the coverage by the named vehicle-to-x applications. As a result, accident scenarios with oncoming vehicles are the most relevant scenarios for investigations with cooperative intelligent transport systems. In addition, high coverage of the most critical scenarios within the use-cases of longitudinal and intersection collision risk warning is already apparent.

Does Vehicle-2-X Radio Transmission Technology Need to Be Considered within Accident Analysis in the Future?

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.

Mechanical Properties of Raw Filaments and Printed Specimens: Effects of Fiber Reinforcements and Process Parameters.

Fused Deposition Modeling (FDM) is a well-established manufacturing method for producing both prototype and functional components. This study investigates the mechanical properties of FDM components by material and process-related influencing variables. Tensile tests were conducted on seven different materials in their raw filament form, two of which were fiber-reinforced, to analyze their material-related influence. To cover a wide range from standard to advanced materials relevant for load-carrying components as well as their respective variations, polylactic acid (PLA), 30% wood-fiber-reinforced PLA, acrylonitrile butadiene styrene (ABS), polycarbonate (PC), a blend of ABS and PC, Nylon, and 30% glass-fiber-reinforced Nylon were selected. The process-related influencing variables were studied using the following process parameters: layer thickness, nozzle diameter, build orientation, nozzle temperature, infill density and pattern, and raster angle. The first test series revealed that the addition of wood fibers significantly worsened the mechanical behavior of PLA due to the lack of fiber bonding to the matrix and significant pore formation. The polymer blend of ABS and PC only showed improvements in stiffness. Significant strength and stiffness improvements were found by embedding glass fibers in Nylon, despite partially poor fiber-matrix bonding. The materials with the best properties were selected for the process parameter analysis. When examining the impact of layer thickness on part strength, a clear correlation was evident. Smaller layer thicknesses resulted in higher strength, while stiffness did not appear to be affected. Conversely, larger nozzle diameters and lower nozzle temperatures only positively impacted stiffness, with little effect on strength. The part orientation did alter the fracture behavior of the test specimens. Although an on-edge orientation resulted in higher stiffness, it failed at lower stresses. Higher infill densities and infill patterns aligned with the load direction led to the best mechanical results. The raster angle had a significant impact on the behavior of the printed bodies. An alternating raster angle resulted in lower strengths and stiffness compared to a unidirectional raster angle. However, it also caused significant stretching due to the rotation of the beads.

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries.

Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxide, Lithium Iron Phosphate, Lithium Manganese Oxide, Lithium Titanium Oxide, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. Results: Prediction results with overall Mean Absolute Percentage Error of 0.0126 have been obtained for XGBoost algorithm. Among these results, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide type cell chemistries stand out with their mean absolute percentage errors of 0.0035 and 0.0057 respectively. Also, algorithm fitting performance is relatively better for these chemistries at 100% state of charge and 60°C temperature compared to ANN results. ANN algorithm predicts with mean absolute error of approximately 0.0472 overall and 0.0238 and 0.03825 for Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. The fitting performance of ANN for Nickle Manganese Cobalt Oxide at 100% state of charge and 60°C temperature is especially poor compared to XGBoost. Conclusions: For an electric vehicle battery calendar ageing prediction application, XGBoost can establish itself as the primary choice more easily compared to ANN. The reason is XGBoost's error rates and fitting performance are more usable for such application especially for Nickel Cobalt Aluminum Oxide and Nickel Manganese Cobalt Oxide chemistries, which are amongst the most demanded cell chemistries for electric vehicle battery packs.

Comparison of several methods for determining the internal resistance of lithium ion cells.

The internal resistance is the key parameter for determining power, energy efficiency and lost heat of a lithium ion cell. Precise knowledge of this value is vital for designing battery systems for automotive applications. Internal resistance of a cell was determined by current step methods, AC (alternating current) methods, electrochemical impedance spectroscopy and thermal loss methods. The outcomes of these measurements have been compared with each other. If charge or discharge of the cell is limited, current step methods provide the same results as energy loss methods.