The process of verification and validation of automated vehicles poses a multi-faceted challenge with far-reaching societal, economical and ethical consequences. In particular, fully automated vehicles at SAE Level 4 and 5 will be expected to operate safely in an arbitrarily complex, infinite-dimensional domain called open context. In order to give structure to the open context, we propose a methodical criticality analysis that maps an infinite-dimensional domain onto a finite and manageable set of artifacts that capture and explain the emergence of critical situations for automated vehicles. We propose a combined approach of expert-based and data-driven methods to identify relevant phenomena and explain the underlying causalities. Leveraging on abstraction, we define a clearly laid out process that converges towards a manageable set of artifacts based on two assumptions on the nature of traffic. A criticality analysis precedes the design phase of an automated vehicle and is therefore located outside the V-model. As the open context is analyzed independently of a concrete realization, it is relevant for any automated vehicle operating within that domain. Therefore, its results can subsequently be used to derive safety principles and mitigation mechanisms for automated driving and to set up a coherent safety argument for the homologation process.

Cooperative driving, a technology domain that allows for autonomous as well as manually driven vehicles to cooperatively coordinate their maneuvers with the aid of inter-vehicular communication, represents nowadays a highly active scientific topic for numerous research projects, notably such as German funded project IMAGinE. In the development process of the corresponding cooperative driving functions, a big challenge is posed by their extensive and complex testing as well as verification and validation procedures, which is reasoned by the vast amount of relevant scenarios needed to consider, even when using modern simulation-based methods. In the work at hand, we introduce our novel co-simulation framework, involving a coupling of traffic flow simulation with vehicle dynamics simulation, as well as an integrated machine learning classification module, which is able to detect, generate and evaluate test scenes and scenarios. As a result, with our approach, we achieve an intelligent way to test and to evaluate the cooperative driving functions practically solely on relevant test scenarios, organized in a systematical workflow with reasonable effort.

Cooperative Maneuver Coordination (CMC) is one of the cornerstone technologies on the way to automated and connected driving of the future. The goal of this technology is to increase traffic safety and efficiency, by solving potential conflict situations on the road, based on cooperative maneuver planning, negotiation and decision-making, with the aid of inter-vehicular communication. This innovative topic represents a wide area for research projects, e.g., such as German funded project IMAGinE. A variety of different approaches for CMC has already been implemented and evaluated in the related work of the recent past. However, due to the high system complexity in general and vast testing effort in particular of these solutions, their actual impact on the traffic quality has not yet been extensively addressed, and therefore must be further investigated. In order to fulfill this task, one needs a methodology with appropriate evaluation metrics alongside with a suitable testing environment, in order to obtain comprehensive results. The scientific contribution of the work at hand involves a simulation-based evaluation methodology for CMC. For this, we will propose a novel CMC algorithm, which is built upon a direct trajectory exchange via inter-vehicular communication and a decentralized decision-making process, suitable for both manually operated as well as autonomous vehicles. In order to examine this algorithm, we will introduce a co-simulation environment with automatic scenario generation and multi-instances capability, which consists of a coupled simulation of traffic flow and vehicle dynamics. Eventually, we will present a set of metrics, which we determined in order to evaluate effectiveness and efficiency of our CMC algorithm, considering its impact on various aspects of the traffic quality.

The technical development of highly automated driving functions (SAE level ≥ 3) has progressed so far that a market launch is anticipated in the short to medium term. The biggest challenge for a fleet approval is the validation and safety proof. Existing approaches such as the statistical, distance-based proof of safety would require billions of test kilometres under representative conditions prior to market launch. This cannot be accomplished by physical testing. Therefore, new methods for the release of highly automated driving functions are currently under development. One of these methods is the so-called scenario-based approach, as proposed by project PEGASUS. It is based on the assumption that a reduction of the test effort is to be expected when testing exclusively critical scenarios. However, this test effort still exceeds the existing capacity in practice by far. In the scope of the present work approaches for the above-mentioned reasons are investigated, which have the potential to further reduce the parameter space of critical scenarios by using the scenario-based approach. The basis is a simulation framework, which consists of a coupled traffic and vehicle dynamics simulation. The core of the solution is a statistical evaluation of relevant influence parameters describing a logical scenario for the derivation of discretization stages of these parameters. It is shown that for the application of the Traffic-Jam-Chauffeur, there is a parameter space reduction of factor 45 for 3-wise and of factor 30 for 10-wise test coverage in case of the considered functional scenarios. In addition, the potential of a simulation-based determination of occurrence probabilities and value ranges of the influence parameters becomes clear. This manifests itself for the functional scenario cut-in in form of a time saving factor of 2000 compared to real-world testing.

One of the major challenges for the automotive industry will be the release and validation of cooperative and automated vehicles. The immense driving distance that needs to be covered for a conventional validation process requires the development of new testing procedures. Further, due to limited market penetration in the beginning, the driving behavior of other human trafc participants, regarding a mixed trafc environment, will have a signifcant impact on the functionality of these vehicles. In this article, a generic simulation-based toolchain for the model-in-the-loop identifcation of critical scenarios will be introduced. The proposed methodology allows the identifcation of critical scenarios with respect to the vehicle development process. The current development status of the cooperative and automated vehicle determines the availability of testable simulation models, software, and components. The identifcation process is realized by a coupled simulation framework. A combination of a vehicle dynamics simulation that includes a digital prototype of the cooperative and automated vehicle, a trafc simulation that provides the surrounding environment, and a cooperation simulation including cooperative features is used to establish a suitable comprehensive simulation environment. The behavior of other trafc participants is considered in the trafc simulation environment. The criticality of the scenarios is determined by appropriate metrics. Within the context of this article, both standard safety metrics and newly developed trafc quality metrics are used for evaluation. Furthermore, we will show how the use of these new metrics allows for investigating the impact of cooperative and automated vehicles on trafc. The identifed critical scenarios are used as an input for X-in-the-Loop methods, test benches, and proving ground tests to achieve an even more precise comparison to real-world situations. As soon as the vehicle development process is in a mature state, the digital prototype becomes a “digital twin” of the cooperative and automated vehicle.

We address the question of feasibility of tests to verify highly automated driving functions by optimizing the trade-off between virtual tests for verifying safety properties and physical tests for validating the models used for such verification. We follow a quantitative approach based on a probabilistic treatment of the different quantities in question. That is, we quantify the accuracy of a model in terms of its probabilistic prediction ability. Similarly, we quantify the compliance of a system with its requirements in terms of the probability of satisfying these requirements. Depending on the costs of an individual virtual and physical test we are then able to calculate an optimal trade-off between physical and virtual tests, yet guaranteeing a probability of satisfying all requirements.

The release and safeguarding of cooperative and highly automated vehicles is often reduced to proper operation of technical systems. As a matter of fact, the proper operation is just a necessary but not sufficient condition. There are a lot of other requirements that have to be taken into a count, which are not just functional. Requirements like social acceptance, customer preferences, potential conflicts, traffic efficiency have to be taken into a count. All these requirements have to be considered in early stages of the vehicle development process. Otherwise, there will be a large time delay before it is possible to implement these automotive technologies in general. It is possible that some of the non-functional requirements are conflicting each other in some cases. The correlation and principal incompatibilities of some requirements have to be addressed and as a matter of fact, such a behaviour is not unusual. A popular example is the correlation between travel time and fuel efficiency. These requirements are often in direct contradiction to each other and can only be balanced. This publication contains an overview about these requirements and their correlations. It can be shown that the release of cooperative and highly automated vehicles is very complex.

The GM HydroGen4 (also known as Opel HydroGen4 in continental Europe and as Vauxhall HydroGen4 in the United Kingdom) is a fuel cell electric vehicle (FCEV) based on the Chevrolet Equinox cross-over platform. The technical specifications and performance values of the car's propulsion and energy storage system are provided, as well as the required modifications to the standard Chevrolet Equinox platform. In addition, "Project Driveway," the world's largest FCEV demonstration program is briefly discussed.

In automotive designs, low cost and low weight are major requirements. A benchmark analysis revealed high pressure hydrogen storage (pressures up to 70 MPa) to be the most technically and commercially viable solution. Structural materials play an important role in commercialization of such a technology. The use of Cr-Ni austenitic stainless steels, ferritic steels and aluminium alloys is reviewed. Testing methodologies for the qualification of materials is discussed in the context of hydrogen diffusion and solubility in each material. To utilize the huge innovation potential of both original equipment manufacturers (OEMs) and suppliers, a widely accepted test standard for the qualification of materials for use in high purity/high pressure H-2 applications, especially under S-N fatigue load, is urgently needed. Consequently, a robust fatigue design model covering the influence of gaseous high pressure hydrogen is required to reduce weight and cost in automotive designs without compromising safety.

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The energy storage system is of decisive importance for all types of electric vehicles, in contrast to the case of vehicles powered by a conventional fossil fuel or bio-fuel based internal combustion engine. Two major alternatives exist and need to be discussed: on the one hand, there is the possibility of electrical energy storage using batteries, whilst on the other hand there is the storage of energy in chemical form as hydrogen and the application of a fuel cell as energy converter [I]. Considering the latter concept, hydrogen is a promising energy carrier in future energy systems. However: storage of hydrogen is a substantial challenge, especially for applications in vehicles with fuel cells that use proton-exchange membranes (PEMs). Different methods for hydrogen storage are discussed, including high-pressure and cryogenic-liquid storage, adsorptive storage on high-surface-area adsorbents, chemical storage in metal hydrides and complex hydrides, and storage in boranes. For the latter chemical solutions, reversible options and hydrolytic release of hydrogen with off- board regeneration are both possible. Reforming of liquid hydrogen-containing compounds is also a possible means of hydrogen generation. The advantages and disadvantages of the different systems are compared [2].

The energy storage system is of decisive importance for all types of electric vehicles, in contrast to the case of vehicles powered by a conventional fossil fuel or bio-fuel based internal combustion engine. Two major alternatives exist and need to be discussed: on the one hand, there is the possibility of electrical energy storage using batteries, whilst on the other hand there is the storage of energy in chemical form as hydrogen and the application of a fuel cell as energy converter. The advantages and limitations, and also the impact of both options are described. To do so, existing GM concept vehicles and mass production vehicles are presented. Eventually, an outlook is given that addresses cost targets and infrastructure opportunities as well as requirements.

This chapter focuses on the energy storage technologies in cell electric vehicles and battery electric vehicles and discusses the latest vehicle projects like the GM HydroGen4 and the Chevrolet Volt as well as the respective VOLTEC powertrain system. There are two major options of energy storage systems in electric vehicles (EVs), which include one where the storage of electrical energy is done by using batteries and the other where the storage of energy is in the form of hydrogen. The Volt is an EV equipped with an additional gasoline engine that is used to extend the vehicle range beyond the electric range when required (E-REV). The main energy storage in the Volt is a Li–ion battery with a nominal energy content of 16 kWh and a pure battery-electric range of 60 km. This leads to reduced fuel consumption, reduced emissions, and also to increased energy security via geographic diversification of the available energy sources. The ultimate objective of the GM strategy is to produce zero-emission vehicles that use an electric powertrain system based on hydrogen fuel cells or purely battery–electric systems and that are also fully competitive to conventional vehicles with regards to performance and ease-of-use. E-REVs such as the Chevrolet Volt or the Opel Ampera are perfectly suited for people who may have to cover longer ranges of up to 500 km and for those who are willing to accept a small ICE in order to ensure the range beyond the initial 60 km of pure EV operation. On the other hand, hydrogen fuel cell vehicles are always operated as zero-emission vehicles that can be refueled within 3–5 min, and offer a long range of about 500 km at full performance, even for family-sized cars.