This paper addresses the lack of empirical knowledge on which demographic groups are most likely to use autonomous buses in the Munich Metropolitan Region. We analyze this question through a large-scale online survey capturing demographics, mobility behavior, and accessibility needs. Results show that younger, multimodal, and well-educated individuals form the core of potential users, while older and car-dependent groups remain hesitant. The findings highlight that successful deployment requires inclusive design, improved accessibility, and targeted communication strategies.

Autonomous driving is a promising technology for public transportation to solve two main challenges: The driver shortage and the reduction of environmental impact. This contribution investigates if already existing requirements in standards, laws, regulations and guidelines for accessibility, safety, security and bus drivers’ tasks of transit buses in Europe and Germany can also be complied to with an autonomous transit bus or if the requirements need an adaption. 54 impactful requirements on autonomous transit buses have been found and their impact and opening design space will be discussed.

This paper addresses the lack of empirically grounded user typologies for understanding acceptance of autonomous buses in the Munich Metropolitan Area. We close this gap through a large-scale online survey and a clustering approach based on mobility preferences and subjective expected utility. The results identify five distinct clusters of users with varying acceptance levels, showing that successful autonomous bus adoption requires tailored communication, service design, and integration strategies.

Connected and Autonomous Vehicles (CAV) are increasingly complex, making resilience hard to guarantee. Resilience means maintaining functionality and availability despite disruptions while ensuring safety. Designing a resilient system requires system-level analysis early in the concept phase to identify and mitigate risks, thereby securing reliability and availability. This paper introduces the Automotive Resilience Maturity Model (ARMM), which evaluates automotive systems’ resilience levels based on their system architecture.

This paper provides a comprehensive case study of Israel’s emerging regulatory sandbox frameworks for AI applications. Drawing from policy white papers, legislative materials, and implemented frameworks, it documents how Israel is adopting sector-specific regulatory sandboxes as experimental mechanisms to balance innovation with public interest concerns. The paper maps the growing appeal of regulatory sandboxes across Israeli AI policy documents since 2018 and provides a detailed analysis of three key implementations: autonomous vehicles, fintech, and general legislative guidelines. It examines Israel’s pioneering AV regulatory sandbox, which grants specific exemptions from traffic regulations (including steering wheel control requirements and international standards compliance) while maintaining safety oversight through control centers and incident reporting. The paper analyzes the proposed fintech sandbox’s multi-regulator committee structure and dual-track system for licensing and oversight relief. It also evaluates the recently published general regulatory sandbox legislative guidelines that establish a technology-neutral four-phase lifecycle framework. The analysis reveals Israel’s preference for adapting existing regulatory structures rather than creating comprehensive AI-specific governance mechanisms. While this sector-specific approach facilitates targeted innovation, it may inadequately address broader AI governance concerns such as algorithmic accountability and human oversight. The paper critically examines institutional design challenges, including inter-agency coordination difficulties in the fintech framework and the tension between innovation facilitation and consumer protection. It concludes that Israel’s approach represents a pragmatic response to technological uncertainty, though questions remain about implementation effectiveness and the adequacy of sector-specific frameworks for comprehensive AI governance. The study contributes to comparative regulatory literature by documenting an understudied jurisdiction’s approach to regulatory experimentation.

Expert drivers possess the ability to execute high sideslip angle maneuvers, commonly known as drifting, during racing to navigate sharp corners and execute rapid turns. However, existing model-based controllers encounter challenges in handling the highly nonlinear dynamics associated with drifting along general paths. While reinforcement learning-based methods alleviate the reliance on explicit vehicle models, training a policy directly for autonomous drifting remains difficult due to multiple objectives. In this paper, we propose a control framework for autonomous drifting in the general case, based on curriculum reinforcement learning. The framework empowers the vehicle to follow paths with varying curvature at high speeds, while executing drifting maneuvers during sharp corners. Specifically, we consider the vehicle’s dynamics to decompose the overall task and employ curriculum learning to break down the training process into three stages of increasing complexity. Additionally, to enhance the generalization ability of the learned policies, we introduce randomization into sensor observation noise, actuator action noise, and physical parameters. The proposed framework is validated using the CARLA simulator, encompassing various vehicle types and parameters. Experimental results demonstrate the effectiveness and efficiency of our framework in achieving autonomous drifting along general paths. The code is available at https://github.com/BIT-KaiYu/drifting.

Innovation plays a vital role in ensuring sustainable mining operations. Electrification and autonomy are two significant trends, but their implementation brings complexity at vehicle and site levels. Therefore, it is crucial to understand how these technologies impact the overall site value creation. This paper suggests a hybrid approach that combines Agent-Based Simulation and vehicle dynamics modeling to explore site configurations. By regarding a mining site as a System-of-Systems, designers can concurrently test different designs to find the optimal combination for a specific scenario.

This article reviews existing state laws related to autonomous vehicle (AV) safety, equity, and automobile insurance. Thirty states were identified with relevant legislation. Of these, most states had one or two relevant laws in place. Many of these laws were related to safety and insurance requirements. Data are needed to evaluate the effectiveness of these laws in order to guide further policy development.

The increased development in automated driving systems (ADS) has opened up significant opportunities to revolutionize mobility and to set the path for technologies, such as electrification. The proposed methodology is a simulation model backed by a multi-objective optimization algorithm. This research investigates the adoption of future technologies in earthmoving application and explores its implications on the design of future machine concepts in terms of equipment size. The shift from “elephant to ants” in the machine selection, resulted in improved feasibility.

Electric vertical take-off and landing aircraft (eVTOLs) have been accessed on various configurations over the past decade. This literature review deals with the issue of determining the appropriate design for an Autonomous Passenger Drone (APD). APDs have been compared with VTOLs on their pros and cons. The authors analysed aerodynamics and propulsion systems of multiple APDs. Further, the comparative analysis aids in designing the best framework for the exterior form of APDs based on human capacity, flying technology, fuel type, travel distance, door type, size, material, safety, cost, etc.

This research focuses on evaluating a user interface (UI) for an autonomous vehicle (AV) with the goal to determine the most suitable layout for persons with visual acuity loss. The testing procedure includes a Wizard of Oz AV for simulating an automated ride. Several participants are included in the study and the visual impairments are simulated by specially designed glasses. The conclusions help to determine the optimal graphic design of the UI that can be independently used by persons with blurred vision. The results can be applied to improve the inclusiveness and ergonomics of vehicle UIs.

The field of autonomous vehicles is gaining wide recognition in the industry, academia as well as social media. However, there is a lack of knowledge on expectations of people regarding this topic. To this end, this paper analyses extant research on perceptions of people in various countries about semi-autonomous and autonomous vehicles. Secondly, based on the findings of this analysis, we developed a questionnaire to gauge the perceptions of the people in Sweden regarding such vehicles. The findings have important implications for the design of AVs in Sweden, and possibly other countries.