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Battery safety is a prominent concern for the deployment of electric vehicles (EVs). The battery powering an EV contains highly energetic active materials and flammable organic electrolytes. Usually, an EV battery catches fire due to its thermal runaway, either immediately at the time of the accident or can take a while to gain enough heat to ignite the battery chemicals. There are numerous battery abuse testing standards and regulations available globally. Therefore, battery manufacturers are always in dilemma to choose the safest one. Henceforth, to find the optimal outcome of these two major issues, six standards (SAE J2464:2009, GB/T 31485-2015:2015, FreedomCAR:2006, ISO 12405-3:2014, IEC 62660-2:2010, and SAND2017-6295:2017) and two regulations (UN/ECE-R100.02:2013 and GTR 20:2018), that are followed by more than fifty countries in the world, are investigated in terms of their abuse battery testing conditions (crush test). This research proves that there is a need for (a) augmenting these standards and regulations as they do not consider real-life vehicle crash scenarios, and (b) one harmonised framework should be developed, which can be adopted worldwide. These outcomes will solve the battery manufacturers dilemma and will also increase the safety of EV consumers.
Electrically propelled cars will play a major role on the streets, not only in the far, but also in the near future. In this work, an overview on the possible risks arising from electric vehicles is given that involves electrical, chemical and thermal hazards. Additionally, an analysis on different scenarios involving damaged electric vehicles, the involved parties, as well as their risk assessment in terms of hazard rating is presented. Furthermore, two scenarios are explained and addressed with more detail. With the derived risks of the battery and with possible crash scenarios presented, the still existing uncertainties for handling damaged electric vehicles are addressed, followed by a discussion on ways to overcome this problem.
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.
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.
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.
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.
With the upcoming new legislative rules in the EU on Event Data Recorder beginning 2022 the question is whether the discussed data base is sufficient for the needs of clarifying accidents involving automated vehicles. Based on the reconstruction of real accidents including vehicles with ADAS combined with specially designed crash tests a broader data base than US EDR regulation (NHTSA 49 CFR Part 563.7) is proposed. The working group AHEAD, to which the authors contribute, has already elaborated a data model that fits the needs of automated driving. The structure of this data model is shown. Moreover, the special benefits of storing internal video or photo feeds form the vehicle camera systems combined with object data is illustrated. When using a sophisticate 3D measurement method of the accident scene the videos or photos can also serve as a control instance for the stored vehicle data. The AHEAD Data Model enhanced with the storage of the video and photo feeds should be considered in the planned roadmap of the Informal Working Group (IWG) on EDR/ DSSAD (Data Storage System for Automated Driving) reporting to UNECE WP29. Also, a data access over the air using technology already applied in China for electric vehicles called Real Time Monitoring would allow a quantum leap in forensic accident reconstruction.
Cooperative Intelligent Transportation Systems have achieved a mature technology stage and are in an early phase of mass deployment in Europe. Relying on Vehicle-to-X communication, these systems were primarily developed to improve traffic safety, efficiency, and driving comfort. However, they also offer great opportunities for other use cases. One of them is forensic accident analysis, where the received data provide details about the status of other traffic participants, give insights into the accident scenario, and therefore help in understanding accident causes. A high accuracy of the sent information is essential: For safety use cases, such as traffic jam warning, a poor accuracy of the data may result in wrong driver information, undermine the usability of the system and even create new safety risks. For accident analysis, a low accuracy may prevent the correct reconstruction of an accident. This paper presents an experimental study of the first generation of Cooperative Intelligent Transportation Systems in Europe. The results indicate a high accuracy for most of the data fields in the Vehicle-to-X messages, namely speed, acceleration, heading and yaw rate information, which meet the accuracy requirements for safety use cases and accident analysis. In contrast, the position data, which are also carried in the messages, have larger errors. Specifically, we observed that the lateral position still has an acceptable accuracy. The error of the longitudinal position is larger and may compromise safety use cases with high accuracy requirements. Even with limited accuracy, the data provide a high value for the accident analysis. Since we also found that the accuracy of the data increases for newer vehicle models, we presume that Vehicle-to-X data have the potential for exact accident reconstruction.