In this paper we present a safety argument fragment to contribute towards solutions to several key factors of relevance towards deployment of safe Automated Driving Systems (ADSs). Firstly, we address the need for exhaustive safety requirements by considering vehicle level, quantitative safety requirements. Secondly, situation awareness is employed to dynamically adapt the ADS’ decision-making. Thirdly, the ADS’ situation awareness is extended with constraints following Precautionary Safety (PcS) principles to ensure the fulfilment of the quantitative safety requirements. Fourthly, the models and assumptions supporting steps two and three are ascertained through the use of an operational design domain, which the ADS is designed to operate within. Furthermore, the paper contrasts the proposed argument with the state of the art in safety assurance to identify the key challenges still remaining.

The autonomous vehicle industry faces significant challenges in validating safety performance, as traditional approaches require extensive testing to demonstrate reliability for rare safety-critical events. This paper addresses this limitation by introducing a framework that enables statistically rigorous safety assessment from limited testing data. We analyze statistical patterns of near-collision events using the Brake Threat Number (BTN) metric to predict the likelihood of potential collisions. Our methodology leverages Extreme Value Theory (EVT) with a multi-criteria optimization approach for threshold determination. Testing with real field data from Volvo Cars Corporation vehicles demonstrates the framework’s ability to establish quantitative Mean Time Between Failures (MTBF) estimates with defined confidence intervals. These results provide a foundation for evidence-based deployment decisions for Autonomous Driving/Advanced Driver Assistance Systems (AD/ADAS) while reducing the validation burden compared to conventional methods, offering a practical path toward balancing technological advancement with safety requirements.

The growing adoption of deep learning, particularly supervised learning, in the manufacturing highlights the need for large labeled datasets. However, generating domain-specific labeled data is costly. Focusing on smart tightening diagnosis in manufacturing, prior research introduced the tightening diagnosis transformer (TDT), which leverages self-supervised transformers to reduce dependency on labeled data. Despite its advancements, TDT has two key limitations: (1) reliance solely on torque sensor data, ignoring angle sensor data, and (2) added computational overhead from self-supervised learning, which is problematic in resource-limited shop-floor environments. This study presents a novel transformer-based multi-label classification method that integrates sensor fusion and reduces needs for both computation and labeled data. We enhance the state-of-the-art TDT by introducing the angle positional encoder (APE), enabling feature-level sensor fusion for supervised learning. Additionally, we propose a self-supervised learning method for APE-enhanced TDT to reduce the need for extensive labeled datasets. We also introduce the random sequence patchifier (RSP), a transformer-specific data augmentation technique that improves generalization and reduces computational cost. Finally, we adopt annealing augmentation scheduling to mitigate the risk of learning “fake” feature representations (unrealistic artifacts created by the augmentations). Compared with previous TDT, our experiment evaluation demonstrates that the these introduced techniques improve Subset Accuracy and F1 scores by 10% and 7%. Moreover, the RSP-based augmentation reduces the training time by 12% for supervised learning and 15% for self-supervised learning.

In this chapter, we explore how new technologies and requirements affect current design methodologies for mechatronics. We investigate gaps and directions needed for the methodologies of tomorrow in view of trends affecting mechatronics and current state of the art. To fully reap the opportunities of mechatronics with advances in materials, sensors, additive manufacturing, AI, computing and communication, but also to handle new requirements and regulations, there is a need for new methodologies and architectures. We introduce the concept of “MechaOps” and related considerations that promise to assist in enhancing scalability, smartness, performance and sustainability for extended mechatronic products that collaborate with a smart infrastructure, humans and other mechatronic systems. MechaOps refers to the integration of the concepts of Mechatronics and DevOps. As opposed to DevOps in software engineering, MechaOps encompasses data gathering, upgrades/downgrades as well as reconfigurations considering both mechanics and/or software in a mechatronic product. With the life-cycle view implied by the MechaOps concept, it becomes essential to design for upgrading, downgrading, maintenance, reuse and refurbishment. The development of new methodologies requires overcoming disciplinary gaps, with specific considerations of novel architectures including digital twins, interactions with humans, other systems and a smart infrastructure, the role of AI in mechatronics, and in assuring trustworthiness and sustainability. We believe that new methodologies and architectures will initially be especially relevant for high-end systems, supporting the creation of adaptable and flexible mechatronics products and services with improved performance and reduced environmental footprint.

This study focuses on the locomotion capability improvement in a tendon-driven soft quadruped robot through an online adaptive learning approach. Leveraging the inverse kinematics model of the soft quadruped robot, we employ a central pattern generator to design a parametric gait pattern, and use Bayesian optimization (BO) to find the optimal parameters. Further, to address the challenges of modeling discrepancies, we implement a multi-fidelity BO approach, combining data from both simulation and physical experiments throughout training and optimization. This strategy enables the adaptive refinement of the gait pattern and ensures a smooth transition from simulation to real-world deployment for the controller. Compared to previous result using a fixed gait pattern, the multi-fidelity BO approach improves the robot’s average walking speed from 0.14 m/s to 0.214 m/s, an increase of 52.7%. Moreover, we integrate a computational task off-loading architecture by edge computing, which reduces the onboard computational and memory overhead, to improve real-time control performance and facilitate an effective online learning process. The proposed approach successfully achieves optimal walking gait design for physical deployment with high efficiency, effectively addressing challenges related to the reality gap in soft robotics.

In recent years, enormous investments in Automated Driving Systems (ADSs) have distinctly advanced ADS technologies. Despite promises made by several high profile auto-makers, it has however become clear that the challenges involved for deploying ADS have been drastically underestimated. Contrary to previous generations of automotive systems, common design, development, verification and validation methods for safety critical systems do not suffice to cope with the increased complexity and operational uncertainties of an ADS. Therefore, the aim of this paper is to provide an understanding of existing methods for providing safety evidence and, most importantly, identifying the associated challenges and gaps pertaining to the use of each method. To this end, we have performed a literature review, articulated around four categories of methods: design techniques, verification and validation methods, run-time risk assessment, and run-time (self-)adaptation. We have identified and present eight challenges, collectively distinguishing ADSs from safety critical systems in general, and discuss the reviewed methods in the light of these eight challenges. For all reviewed methods, the uncertainties of the operational environment and the allocation of responsibility for the driving task on the ADS stand-out as the most difficult challenges to address. Finally, a set of research gaps is identified, and grouped into five major themes: (i) completeness of provided safety evidence, (ii) improvements and analysis needs, (iii) safe collection of closed loop data and accounting for tactical responsibility on the part of the ADS, (iv) integration of AI/ML-based components, and (v) scalability of the approaches with respect to the complexity of the ADS.

Introducing connectivity and collaboration promises to address some of the safety challenges for automated vehicles (AVs), especially in scenarios where occlusions and rule-violating road users pose safety risks and challenges in reconciling performance and safety. This requires establishing new collaborative systems with connected vehicles, off-board perception systems, and a communication network. However, adding connectivity and information sharing not only requires infrastructure investments but also an improved understanding of the design space, the involved trade-offs and new failure modes. We set out to improve the understanding of the relationships between the constituents of a collaborative system to investigate design parameters influencing safety properties and their performance trade-offs. To this end we propose a methodology comprising models, analysis methods, and a software tool for design space exploration regarding the potential for safety enhancements and requirements on off-board perception systems, the communication network, and AV tactical safety behavior. The methodology is instantiated as a concrete set of models and a tool, exercised through a case study involving intersection traffic conflicts. We show how the age of information and observation uncertainty affect the collaborative system design space and further discuss the generalization and other findings from both the methodology and case study development.

Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These challenges become more evident in highly dynamic environments. This work proposes a probabilistic framework to jointly infer which parts of an environment are statically and which parts are dynamically occupied. We formulate the problem as a Bayesian network and introduce minimal assumptions that significantly reduce the complexity of the problem. Based on those, we derive Transitional Grid Maps (TGMs), an efficient analytical solution. Using real data, we demonstrate how this approach produces better maps than the state-of-the-art by keeping track of both static and dynamic elements and, as a side effect, can help improve existing SLAM algorithms.

As simulation is increasingly used in scenario-based approaches to test Automated Driving Systems, the credibility of simulation results is a major concern. Arguably, credibility depends on the validity of the simulation setup and simulation models. When selecting appropriate simulation models, a trade-off must be made between validity, often connected to the model's fidelity, and cost of computation. However, due to the large number of test cases, expert-based methods to create sufficiently valid simulation setups seem infeasible. We propose using design contracts in order to semi-automatically compose simulation setups for given test cases from simulation models and to derive requirements for the simulation models, supporting separation of concerns between simulation model developers and users. Simulation model contracts represent their validity domains by capturing a validity guarantee and the associated operating conditions in an assumption. We then require the composition of the simulation model contracts to refine a test case contract. The latter contract captures the operating conditions of the test case in its assumption and validity requirements in its guarantee. Based on this idea, we present a framework that supports the compositional configuration of simulation setups based on the contracts and a method to derive runtime monitors for these simulation setups.