Scenariobased testing plays a pivotal role in the development and validation of automated vehicles. Its main challenge is to efficiently generate realistic and relevant test scenarios to identify and analyze shortcomings of automated driving systems. The Scenario Factory 2.0 unifies several scenario generation techniques from the opensource CommonRoad framework and introduces simulation modes for coupling with the traffic simulators OpenTrafficSim and SUMO. The simulation modes enable generating scenarios with a tunable similarity to existing ones. As existing approaches, the Scenario Factory 2.0 integrates scenario generation from formal specifications and falsification techniques. Scenario Factory 2.0 has a modular structure and the modules can be easily rearranged for creating required scenarios. We evaluate the effectiveness of the novel simulation modes for various traffic scenarios and demonstrate the scenario generation with Scenario Factory 2.0 in a use case. The opensource code is provided at https://commonroad.in.tum.de/tools/scenariofactory.

The advancement of autonomous vehicles (AVs) requires robust evaluation methods to ensure both safety and efficiency. To incorporate multiple dimensions in designing test scenarios, this paper proposes a multidimensional evaluation framework for AV test scenarios based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. The evaluation considers three dimensions: risk, complexity, and rarity. First, the test scenario is deconstructed into its constituent elements. Then, the weights of these elements are determined from both subjective and objective perspectives using the Analytic Hierarchy Process (AHP) and Entropy Weight Method. Then, game theory is employed to optimize these weights, deriving the optimal balance between subjective and objective weights. Next, three different scenario libraries are utilized as case studies, and a comprehensive evaluation index is calculated using the TOPSIS model. Subsequently, the scenarios are categorized into four levels using Kmeans clustering algorithm. Finally, the accuracy and reliability of the framework are verified through simulation. The simulation results demonstrate the effectiveness of the framework in identifying critical scenarios and providing valuable insights for AV testing.

Efficient exploration and understanding of an autonomous driving system's capabilities and functional boundaries are crucial for ensuring safety performance. This paper offers a comprehensive examination of safety verification and test case generation for autonomous driving function stacks, enhancing their safety and reliability. Firstly, we introduce a holistic approach that synergizes operational floworiented Hazard and Operability Study (HAZOP) with cascaded SystemTheoretic Process Analysis (STPA) processes. Secondly, we propose a test case generation procedure that begins with an expansion to discrete parameters using tree search, followed by heterogeneous sampling in the continuous parameter space. Additionally, this paper features a realworld case study with WATonoBus, showcasing the practicality and effectiveness of the proposed methods in securing autonomous vehicles safe operation in complex urban settings. Our findings make a substantial contribution to the autonomous vehicle safety field, offering critical insights for ongoing research and development in this rapidly advancing area.

Interactive autonomous driving is an evolving research domain that demands an an autonomous vehicle (AV) to exhibit adaptability to new environments, cognizance of surrounding traffic conditions, and proficient decisionmaking ability in complex humandominated scenarios to guarantee safe navigation and promote social compatibility. This paper reviews the diverse methodologies utilized in interactive driving for AVs. Various techniques will be investigated for their unique contributions and capabilities in developing AV systems, such as long shortterm memory (LSTM), transformer, artificial potential field (APF), game theory, reinforcement learning (RL)/deep reinforcement learning (DRL), and partially observable Markov decision processes (POMDP), among others. Recent advancements based on these methodologies are summarized to elucidate their application rationale in interactive driving scenarios. The strengths and challenges inherent to each approach within the context of interactive driving are further assessed. Additionally, the resolution of these challenges is explored through integrating different methods. Therefore, a comparative analysis offers crucial perspectives for advancing autonomous driving technologies. This review exclusively focuses on the interactions between AVs and humandriven vehicles (HDVs).

As a promising clean energy conversion device, solid oxide fuel cells (SOFCs) could efficiently utilize multiple fuels and have been applied in stationary power generation and other fields. However, the high operating temperature limits the use of SOFCs in the transportation sector. Consequently, reducing the operating temperature is a key focus in SOFCs development. In recent years, metalsupported SOFCs (MSSOFCs) have received significant attention because of intermediate operating temperatures. MSSOFCs offer advantages such as simple and reliable sealing, rapid startup, and high power density, making MSSOFCs a promising option for transportation applications. However, for commercial applications, the methods to enhance performance, improve durability and mitigate degradation need to be further studied. This paper comprehensively reviews the state of the art approaches to performance improvement, durability under various conditions and different fuel reforming methods. Finally, it highlights the prospects and challenges for future advancements in this field.