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.

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.

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.

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.

The progression toward automated driving and the latest advancement in vehicular networking have led to novel and natural humanvehicleroad systems, in which affective humanvehicle interaction is a crucial factor affecting the acceptance, safety, comfort, and traffic efficiency of connected and automated vehicles (CAVs). This development has inspired increasing interest in how to develop affective interaction framework for intelligent cockpit in CAVs. To enable affective humanvehicle interactions in CAVs, knowledge from multiple research areas is needed, including automotive engineering, transportation engineering, humanmachine interaction, computer science, communication, as well as industrial engineering. However, there is currently no systematic survey considering the close relationship between humanvehicleroad and human emotion in the humanvehicleroad coupling process in the CAV context. To facilitate progress in this area, this paper provides a comprehensive literature survey on emotionrelated studies from multiaspects for better design of affective interaction in intelligent cockpit for CAVs. This paper discusses the multimodal expression of human emotions, investigates the human emotion experiment in driving, and particularly emphasizes previous knowledge on human emotion detection, regulation, as well as their applications in CAVs. The promising research perspectives are outlined for researchers and engineers from different research areas to develop CAVs with better acceptance, safety, comfort, and enjoyment for users.

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.

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.

With the rapid development of autonomous vehicles, more and more functions and computing requirements have led to the continuous centralization in the topology of electrical and electronic (E/E) architectures. While certain Tier1 suppliers, such as BOSCH, have previously proposed a serial roadmap for E/E architecture development, implemented since 2015 with significant contributions to the automotive industry, lingering misconceptions and queries persist in actual engineering processes. Notably, there are concerns regarding the perspective of zoneoriented E/E architectures, characterized by zonal concentration, as successors to domainoriented E/E architectures, known for functional concentration. Addressing these misconceptions and queries, this study introduces a novel parallel roadmap for E/E architecture development, concurrently evaluating domainoriented and zoneoriented schemes. Furthermore, the study explores hybrid E/E architectures, amalgamating features from both paradigms. To align with the evolution of E/E architectures, networking technologies must adapt correspondingly. The networking mechanisms pivotal in E/E architecture design are comprehensively discussed. Additionally, the study delves into modeling and verification tools pertinent to E/E architecture topologies. In conclusion, the paper outlines existing challenges and unresolved queries in this domain.

Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multitask sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an indepth understanding and accurate modelling of driving steering behaviours. Regarding different testing scenarios, two driving modes, namely, bothhand and singlerighthand modes, are studied. For each driving mode, three different driving postures are further evaluated. Next, a multitask timeseries transformer network (MTSTrans) is developed to predict the future steering torques and driving postures based on the multivariate sequential input and the selfattention mechanism. To evaluate the multitask learning performance and informationsharing characteristics within the network, four distinct twobranch network architectures are evaluated. Empirical validation is conducted through a driving simulatorbased experiment, encompassing 21 participants. The proposed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both twohand and singlehand driving modes. These findings hold significant promise for the advancement of driver steering assistance systems, fostering mutual comprehension and synergy between human drivers and intelligent vehicles.