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.

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.

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.

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).

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.

This paper conducts a thorough exploration of vehicletoeverything (V2X) communication in the realm of intelligent connected vehicles (ICVs). It initiates by tackling challenges across three pivotal phases of cooperative communication: precommunication, duringcommunication, and postcommunication. The discourse delves into a spectrum of concepts and strategies to surmount these challenges. Furthermore, it meticulously scrutinizes diverse communication scenarios and associated techniques, evaluating their significance and feasibility. Moreover, an indepth analysis of various datasets is undertaken, considering their distinctive attributes and suitability for diverse communication tasks. The paper critically examines and debates the platforms and frameworks used in the experiments, providing valuable insights into their performance. Following a comprehensive review of existing methods and datasets, the paper identifies potential research directions and challenges that warrant further exploration in the realm of V2X communication for intelligent connected vehicles. This comprehensive examination contributes to a deeper understanding of the subject, paving the way for future advancements in this dynamic field.

A new type of transportation vehicle, the flying car, is attracting increasing attention in the automotive and aviation industries to meet people's personalized transportation needs for urban air traffic and future travel. With its vertical takeoff and landing capability, flying cars can expand its feasible routes into 3D space. The above process, however, requires sufficient path planning to obtain optimal 3D path. To solve the above issue, the inspiration was drawn from animals in the natural world to design a type of flying car that can travel in various urban environments such as land and low altitude by using different components like wheels and propellers. Incorporating the motion characteristics of flying cars in the future urban environment, segmenting the energy consumption and time models of various stages of flying cars is conducted. The introduction of temporal A* algorithm into the new field of flying cars for the first time, the priority planning algorithm for multiple flying car groups based on an improved A* algorithm utilizing safety intervals is proposed. The proposed strategy is validated on different sizes of urban environment maps. The results indicate that on a complex map with 452 nodes, the strategy effectively reduces distance by 4.5 m, decreases energy consumption by 85.8% and improves planning speed. Compared with the strategy based on multicommodity network flow integer linear programming, the planning results are roughly the same, but the weighted cost of employing this strategy is decreased by 5.2%, and the path distance is reduced by 0.34 m.

In the field of autonomous vehicles, accurately predicting steering angle and speed is a pivotal task. This task affects the accuracy of the final decision of the autonomous vehicle and is the basis for ensuring the safe and efficient operation of the autonomous vehicle. Previous studies have often relied on data from only one or two modalities to make predictions for steering angle and vehicle speed, which were often inadequate. In this paper, the authors propose a MultiModal FusionBased EndtoEnd Steering Angle and Vehicle Speed Prediction Network (MFESSNet). The network innovatively extends the onestream and twostream structure to a threestream structure and cleverly extracts features of images, steering angles, and vehicle speeds using HRNet and LSTM layers. In addition, in order to fully fuse the feature information of different modal data, this paper also proposes a local attentionbased feature fusion module. This module improves the fusion of different modal feature vectors by capturing the interdependencies in the local channels. Experimental results demonstrate that MFESSNet outperforms the current stateoftheart model on the publicly available Udacity dataset.

VehicletoEverything (V2X) communication is expected to accomplish a longstanding goal of the Connected and Autonomous Vehicle (CAV) community to bring connected vehicles to roads on a large scale. A major challenge, and perhaps the biggest hurdle on the path towards this goal, is the scalability issues associated with it, especially when vehicular safety is concerned. As a major stakeholder, Cellular V2X (CV2X) community, which is based on the 3rd Generation Partnership Project (3GPP), has long been trying to research on whether vehicular networks are able to support the safetycritical applications in highdensity vehicular scenarios. This paper attempts to answer this question by first presenting an overview on the scalability challenges faced by 3GPP Release 14 Long Term Evolution CV2X (LTEV2X) using the PC5 sidelink interface for low and heavydensity traffic scenarios. Next, it demonstrates a series of solutions that address network congestion, packet losses, and other scalability issues associated with LTEV2X to enable this communication technology for commercial deployment. In addition, a brief survey is provided into 3GPP Release 16 5G New Radio V2X (NRV2X) that utilizes the NR sidelink interface and works as an evolution of CV2X towards better performance for V2X communications, including new enhanced V2X (eV2X) scenarios that possess ultralowlatency and highreliability requirements.

With the spread adoption of artificial intelligence, the great challenges confronted by the intelligent safety concernsafety of the intended functionality has become the biggest roadblock to the mass production of highlevel automated vehicles, notably arising from perception algorithm deficiencies. This paper focuses a cutin scenario, dividing this scenario into lowrisk and highrisk segments predicated on the kinetic energy field, and the mental activities of passengers on prefrontal cortex, are analyzed within these delineated segments. Two experiments are then conducted, leveraging driving simulators and realworld vehicles, respectively. Experiment results indicate that high risk may result in the passengers' mental activity on prefrontal cortex change. This revelation posits a potential avenue for augmenting the intended functionality of automated vehicle by using passengers' physiological state.