Operational Context-Based Design Method of Autonomous Vehicles Logical Architectures

Abstract[Context and motivation] For automated driving systems, the operational context needs to be known in order to state guarantees on performance and safety. The operational design domain (ODD) is an abstraction of the operational context, and its definition is an integral part of the system development process. [Question/problem] There are still major uncertainties in how to clearly define and document the operational context in a diverse and distributed development environment such as the automotive industry. This case study investigates the challenges with context definitions for the development of perception functions that use machine learning for automated driving. [Principal ideas/results] Based on qualitative analysis of data from semi-structured interviews, the case study shows that there is a lack of standardisation for context definitions across the industry, ambiguities in the processes that lead to deriving the ODD, missing documentation of assumptions about the operational context, and a lack of involvement of function developers in the context definition. [Contribution] The results outline challenges experienced by an automotive supplier company when defining the operational context for systems using machine learning. Furthermore, the study collected ideas for potential solutions from the perspective of practitioners.KeywordsArtificial intelligenceContextMachine learningOperational design domainRequirements engineeringSystems engineering

Introduction: With the Safety Ensuring Lives Future Deployment and Research in Vehicle Evolution (SELF DRIVE) Act in the United States, there is a growing interest in autonomous vehicles (AVs). One avenue of innovation would be to use them to mobilize and coordinate response efforts during natural disasters. This study uses an earthquake response in an urban, developed setting as a hypothetical example case study. In this hypothetical scenario, private AVs would be mobilized to help rescue victims from collapsed structures. Methods: A Markov model compared an intervention arm with AVs to a status quo arm using a hypothetical cohort of American earthquake victims. The three possible health states were trapped but alive, rescued and alive, and dead. The cycle length of the Markov model was 6 h. Results: The cost of deploying AVs was $90,139 relative to $87,869 in status quo arm. Using AVs produced an incremental cost of $2269 (95% credible interval (CI) = $−12,985–$8959). Victims have 7.33 quality-adjusted life years (QALYs) in the intervention arm compared to 7.20 QALYs in the status quo arm, resulting in an incremental gain of 0.13 (95% CI = −0.73–2.19) QALYs. The incremental cost-effectiveness ratio (ICER) was $16,960/QALY gained (95% CI = cost-saving–$69,065/QALY). Discussion: The mobilization of private AVs in the setting of an earthquake has the potential to save money and reduce the loss of life. AVs may advance emergency management competencies.

Autonomous vehicles are gradually entering the transportation system. The traffic will become more heterogeneous since both autonomous and human-driven vehicles will share the roads. Cooperative driving, by promoting synchronized actions and shared situational awareness among vehicles, can significantly enhance driving safety. On the other hand, understanding human drivers is a pivotal step for cooperative driving in such mixed traffic environments, which facilitates effective interaction between human drivers and their vehicles. This paper presents a testbed that can be used to conduct research in intelligent and cooperative driving. The testbed consists of driving simulators, custom-designed copilots with an Artificial Intelligence engine, an optimization server, and a cloud database. The copilot is capable of sensing and understanding the human driver, the vehicle and the traffic. It can assist the driver by providing timely alerts on potential risks. Most importantly, it can communicate with other nearby vehicles for cooperative driving. Two case studies are presented to validate and evaluate the testbed. The first case study demonstrates the performance of the copilot in human distraction detection and driving assistance. The second case study focuses on cooperative driving between one human-driven vehicle and two connected autonomous vehicles in a lane-changing scenario. We expect this research testbed to be used in various research projects that involve human-driven vehicles and connected autonomous vehicles.

The Government of Qatar is investing considerable resources in the development of integrated transport infrastructure as a solution to traffic congestion and environmental degradation. Autonomous vehicles are expected to be one of the methodologies for enhancing the quality of the built environment and fostering a more sustainable and environmentally conscious urban milieu. While autonomous cars have the potential to address urban difficulties and be utilized for personal, group, and public transit purposes, their implementation in the specific context of Doha has not been thoroughly analyzed. This research explores the potential impacts that autonomous vehicles are expected to have on travel demands, parking needs, and urban planning. It discusses the benefits and challenges associated with autonomous vehicles and predicts their likely implementation and development based on the different precedents. In addition, it explores how it will affect planning decisions such as drop-off and pick-up areas, parking and public transit. The research utilizes a qualitative approach through literature content analysis, field observations, and mobility analysis. Several case studies are analyzed to explore how autonomous vehicles can operate within a limited zone. The study uncovers the issue of ownership and mode of operation as essential factors that can affect the impact of autonomous vehicles (AVs). In addition, implementing AVs may create complex environmental concerns when greener and more sustainable transportation models are not integrated. It is recommended that Doha adopt integrated planning and implementation of AV technologies to ensure that AVs align harmoniously with the environmental stewardship requirement. The research study also presents urban design frameworks for how existing urban spaces can accommodate autonomous vehicles.

In this study, performance evaluation of an autonomous vehicle storage and retrieval system (AVS/RS) is completed under various pre-defined design scenarios. Design scenarios are considered based on the rack configuration of the warehouse, namely the number of aisles, bays, and tiers, number of autonomous vehicles (AVs), and number of lifts. The system is simulated using ARENA 12—a commercial software. Seven performance measures are observed from the simulation results. These are: the average cycle time, average utilization of the AVs, average utilization of the lifts, average waiting time in the AVs’ queue, average waiting time in the lifts’ queue, average number of transactions waiting in the AVs’ queue, and average number of transactions waiting in the lifts’ queue. Total cost of the system is also integrated into the analysis. The results are analyzed via graphs. Various design options and their costs facilitate the evaluation of the best design for the warehouse manager. The case study is completed for a company that utilizes AVS/RS in France.

The integration of autonomous vehicles (AVs) into public transportation systems has the potential to transform urban mobility by enhancing efficiency, accessibility, and sustainability. As cities continue to expand, traditional transit systems face challenges such as traffic congestion, high operational costs, and limited coverage. AVs, with their ability to optimize routes, reduce human error, and operate continuously, offer a promising solution to improve public transit networks. The adoption of AV technology in public transportation can redefine mobility patterns, particularly by bridging first- and last-mile connectivity gaps, enhancing commuter convenience, and improving mobility for elderly and disabled passengers. Unlike conventional transit modes, autonomous buses, shuttles, and taxis can dynamically adjust routes based on real-time data, reducing travel time and increasing overall system efficiency. Additionally, AVs can function seamlessly with multi-modal transport networks, integrating with metro, rail, and bus services to create a more cohesive and accessible urban transit system. Beyond improving passenger experience, AVs offer significant economic and environmental benefits. Their reduced dependence on human drivers leads to lower labor costs, while advanced energy-efficient systems contribute to lower fuel consumption and carbon emissions. Autonomous public transit solutions also help alleviate traffic congestion by minimizing unnecessary stops and optimizing roadway usage through vehicle-to-infrastructure (V2I) and vehicle-to vehicle (V2V) communication. However, the transition to a fully autonomous public transport system is not without challenges. Cities and governments must address issues such as infrastructure readiness, regulatory frameworks, liability concerns, cyber security risks, and public acceptance to ensure a smooth and secure transition. By analyzing real-world case studies and pilot projects, this chapter provides insights into how different cities worldwide are integrating AVs into their transit systems. Several pilot programs have demonstrated the feasibility of self driving shuttle services, particularly in urban areas with high commuter demand and limited transit accessibility. Furthermore, technological advancements in artificial intelligence (AI), machine learning, and sensor-based navigation continue to refine AV capabilities, making them increasingly viable for large-scale deployment in public transportation. This chapter also explores future trends, policy recommendations, and best practices that can accelerate AV adoption in public transport. Governments, urban planners, and transportation authorities must collaborate to design regulatory policies that encourage innovation while ensuring passenger safety and data security. Investment in smart infrastructure, including intelligent traffic management systems and dedicated AV lanes, will be crucial for optimizing AV operations in public transit. Ultimately, the successful integration of AVs into public transportation can lead to a more efficient, inclusive, and sustainable urban mobility ecosystem. With the right combination of technological innovation, policy support, and public engagement, AVs have the potential to redefine how people commute in the cities of the future, offering safe, affordable, and environmentally friendly solutions.

Safety certification of Autonomous Vehicles (AVs) require guarantees on AVs' safety at design time. To this effect, this paper proposes modeling abstractions that allow architectural representation of AVs and their surroundings, i.e. representation of different components, and enable safety analysis from such representation without requiring any expertise on formal methods. Toward this direction, AVs are considered as Cyber-Physical Systems with Mobile computing nodes (MCPS), where each node (i.e. an AV) can have intentional (as determined by AVs' controller) and unintentional (e.g., in case of skids) motion characteristics depending on the physical environment (e.g. road condition). The modeling abstractions are used to analyze safety of passengers in an AV that collides with guard rail due to skid along a curved segment on the AZ-83 highway.

Technological development and the massive investment in 'intelligent' vehicle technologies are likely to turn autonomous vehicles into reality in a few years. The insertion intelligence in road vehicles aims to obtain a reduction in traffic accidents due to the mitigation of errors committed by human drivers, thanks to their replacement by machines. In addition, autonomous vehicles should be able to mitigate hazards in road transportation systems without creating new risks. Thus, It is important to study how to ensure safety in this new scenario. Some research in this area has already been developed, but they do not show how to design properly an autonomous vehicle system in which existing methods can be applied to analyze and guarantee adequate levels of safety in such vehicles. As a result, this master's work develops a proposal that aims to facilitate the development and analysis of this new class of vehicles, in addition to ensuring levels of critical safety appropriate to autonomous vehicles. The proposal is represented by a system called Autonomous Vehicle Control (CVA), which was developed under the concept of Intelligent Transport Systems (STI). The CVA system is formed by two layers, one of operation (Autonomous Vehicle Operation -OVA), responsible for the driving of the vehicle and another one of protection (Autonomous Vehicle Protection -PVA). The main idea is to use the PVA layer for the prevention of accidents. The PVA layer was developed and tested in a simulation environment, considering a Case Study. It was observed that, as predicted, the CVA system, because it has a layer aimed at vehicular protection, was able to avoid several collision situations between vehicles.

Different perspectives discuss autonomous vehicle (AV) as predicted future transportation that includes design, engineering, economics, computer science, social, economic, legislation, and ethical aspects. On the other hand, AV technology offers new problems such as new engineering aspects, social and ethical problems, and presents in the form of an idealized decision-making problem. There are many benefits to AV vehicles, which are at the forefront of technology development. However, the level of cybersecurity provided by this technology is poorly understood. Cyber-attacks against AV can have severe implications for safety, privacy, manufacturers' reputation, and overall perception. In this research, we examine public opinion regarding the implications and challenges that might face AV technology in the United Arab Emirates (UAE). We claim that the successful adoption of AV in future transport systems will be achieved by reasonable risk management approaches that deal with different potential threats of cybersecurity. This survey produced usable responses from 204 participants. The significant result was that the respondents had not previously heard of vehicle-related technology; however, most had a positive first opinion on technology. Around 85% of participants felt that the expected benefits were likely to occur. They expressed a high level of concern regarding security and performance issues. Respondents stated that safety is the most crucial aspect of driverless vehicles. Mostly, it stated that personal communication devices should integrate with the AV's internet connection. The primary outcomes are that the population feels positive about AV and have optimistic expectations regarding its benefits. While maintaining some concerns, most of the people are willing to drive the AV.

Problem definition: Autonomous vehicles (AVs) are predicted to enter the consumer market in less than a decade. There is currently no consensus on whether their presence will have a positive impact on users and society. The skeptics of automation foresee increased congestion, whereas the advocates envision smoother traffic with shorter travel times. We study the automation controversy and advise policymakers on how and when to promote AVs. Academic/practical relevance: The AV technology is advancing rapidly and there is a need to study its impact on social welfare and the likelihood of its adoption by the public. Methodology: We use supply-demand theory to find the equilibrium number of trips for autonomous and regular households. We develop a simulation model of peer-to-peer AV sharing. We compare the socially optimal level of automation with the selfish adoption patterns where households independently choose their vehicle type. Results: We establish that the optimal social welfare is influenced by: (i) the network connectivity, that is, the ability of the infrastructure to serve AVs, (ii) the additional comfort provided by AVs that allows passengers to engage in other productive activities instead of driving, and (iii) the AV sharing patterns that reduce ownership costs, but create empty vehicle trips that increase congestion. Managerial implications: We investigate the impact of AVs in a case study of Toronto and show that partial automation maximizes social welfare. We show that the comfort of AVs may add traffic that compromises social welfare. Moreover, although traffic increases with automation, travel times may decrease because of significant improvements in traffic flow caused by AV connectivity in the network.

To embrace or not to embrace? Understanding public’s dilemma about autonomous mobility services: A case study of Singapore

Despite their promise, there have been discussions surrounding the technological risks of autonomous vehicles (AVs) and the extent to which AVs are ready for large-scale deployment. Using a case study approach, this article examines the development and implementation of AVs in Singapore. Our findings reveal that AV regulatory sandboxes, the formalisation of safety assessments and the release of technical guidelines are some of the most adaptive and innovative instruments that have been adopted to govern AVs in Singapore. Furthermore, Singapore's approach to AVs has applied an adaptive strategy that is both pre-emptive and responsive. The accelerated expansion of trials and regulatory provisions for AVs demonstrates Singapore's aspiration to be nimble, and showcases the simultaneous adoption of two contrasting implementation approaches – prescriptive and experimentalist – to guide AV adoption. The regulatory lessons derived from the governance of AVs in Singapore could provide useful policy guidance, and could inform policy discussions of AVs as well as other autonomous systems.

After-sales maintenance services can be a very profitable source of income for original equipment manufacturers (OEMs) due to the increasing interest of assets’ users on performance-based contracts. However, when it concerns the product value-adding process, OEMs have traditionally been more focused on improving their production processes, rather than on complementing their products by offering after-sales services, consequently leading to difficulties in offering them efficiently. Furthermore, due to both the high uncertainty of the assets’ behaviour and the inherent challenges of managing the maintenance process (e.g. maintenance strategy to be followed or resources to be deployed), it is complex to make business out of the supply of after-sales services. Aiming at helping the business and maintenance decision-makers at this point, this paper proposes a framework for optimising the income of after-sales maintenance services through: (a) implementing advanced multi-objective opportunistic maintenance strategies that systematically consider the assets’ operational context in order to perform preventive maintenance during the most favourable conditions, (b) considering the specific OEMs’ and users’ needs and (c) assessing both endogenous and exogenous uncertainties that might condition the after-sales services’ profitability. The developed case study for the wind energy sector demonstrates the suitability of the presented framework for optimising the after-sales services.

This article examines the transformative impact of Large Language Models (LLMs) on autonomous vehicle technology, analyzing how these advanced AI systems are reshaping the fundamental architecture of self-driving systems. Moving beyond traditional modular pipelines, LLM-powered autonomous vehicles demonstrate enhanced contextual awareness, flexible decision-making, and intuitive human-machine interaction capabilities previously unattainable with conventional approaches. The integration of language model capabilities enables vehicles to process multimodal data streams cohesively, reason about complex driving scenarios, and communicate more effectively with passengers and other road users. Through case studies on industry implementations like Waymo's EMMA and research innovations such as DriveMLM, we identify key methodological advances, performance improvements, and remaining challenges in computational requirements, safety validation, and regulatory compliance. The article highlights promising research directions including hybrid AI architectures, edge computing optimization, and human-centric interaction models that will likely shape the future development of autonomous transportation systems. This convergence of language understanding and physical navigation represents a paradigm shift that promises to accelerate progress toward more capable, adaptable, and socially-aware autonomous vehicles.

As autonomous driving technology matures, the focus shifts to enhancing the safety and reliability of these systems. Simulation testing is a critical method for efficiently and rapidly validating the performance of autonomous vehicles (AVs). A robust AV system requires extensive testing across a wide range of scenarios and iterative improvements. However, current simulation systems have limitations in supporting diverse scenarios, often relying on expert-designed situations. To address these challenges, we introduce DTTF-Sim, a novel simulation system based on Digital Twin technology for traffic flow. DTTF-Sim aims to accurately replicate real-world traffic flow conditions, offering continuous long-term simulation capabilities for AV testing. The system can simulate detailed dynamic traffic scenarios with a focus on interactions between multiple vehicles and between AVs and background traffic vehicles, modeling the strategic decision-making processes that occur in these encounters. This paper outlines the architecture and functionalities of DTTF-Sim, highlighting its ability to overcome the shortcomings of existing simulation platforms. We demonstrate the effectiveness of DTTF-Sim through case studies and experimental results, showing its potential to significantly advance the development and testing of autonomous driving technologies.