•SSA is a holistic traffic safety framework with system-wide interventions.•A systematic review of 82 studies highlights the effectiveness of SSA.•Systematic review followed the PRISMA guidelines, ...coupled with SPIDEE criteria.•SSA's adaptable strategy focuses on road design, behavior change, and tailored measures.•This review offers insights into SSA's efficacy in reducing road fatalities and injuries.
The Safe System Approach (SSA) has emerged as a comprehensive framework for enhancing traffic safety through system-wide interventions. This systematic review, conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzes 82 relevant studies categorized based on the SSA pillars: safe road users, safe vehicles, safe speeds, safe roads, and post-crash care. The review provides insights into SSA's effectiveness in reducing road traffic fatalities and injuries, exploring implementation challenges and opportunities, including policy initiatives, institutional frameworks, and stakeholder collaborations. The findings highlight the potential for SSA to create a more forgiving and resilient transportation system, offering valuable guidance for policy decisions, future research, and interventions aimed at promoting safer road environments. SSA's comprehensive strategy for Safe Road Users encompasses considerations of road system design, behavior modification, and tailored measures for vulnerable users, showcasing its versatility in addressing diverse challenges. In the realm of Safe Vehicles, SSA actively involves manufacturers in a cycle of continuous improvement, rigorous testing, and collaborative efforts to establish new safety regulations. The emphasis on managing Safe Speeds, aligning with human parameters, and involving communities reflects SSA's adaptable nature and provides insights for establishing context-specific speed limits. SSA contributes significantly to Safe Roads through its implementation of innovative countermeasures, forgiving road designs, and the integration of emerging disciplines, resulting in a notable reduction in fatalities and injuries. In the domain of Post-Crash Care, SSA's integrated perspective fosters collaboration among emergency services, medical professionals, and the justice system. It addresses challenges through standardized approaches and information sharing, ensuring a comprehensive and unified approach to road safety. This review contributes to the ongoing efforts to prioritize safety and transform the transportation landscape on a global scale.
As agile software development is increasingly adopted in the software industry, the popularity of scaling frameworks supporting adoption in large development contexts is increasing rapidly. While ...several such frameworks exist, the most popular one at the moment is the Scaled Agile Framework (SAFe). Despite its popularity, there exists limited research on its usage and adoption. In this paper, we contribute by presenting a single case study in a large financial organization, studying the transformation reasons, transformation process, as well as the benefits, and challenges of SAFe adoption. We conducted 24 semi-structured interviews with 27 interviewees and analyzed the transcribed interviews using open and axial coding. We identified 17 reasons for SAFe adoption in this organization, of which the most salient ones were to shorten the time to market, improve collaboration, and use a well-described and comprehensive framework. An industry context-specific reason was the popularity of SAFe in the financial sector. The transformation in the case organization was top-down and proceeded step-wise. The most significant activities during the transformation were piloting, education, coaching, and the forming of agile release trains. Our case also implemented "Scrum tours" to increase the understanding of lean and agile principles. We identified 13 benefits of SAFe, of which improved collaboration, transparency, and shorter time to market were considered the most important. We identified a total of 16 challenges, with the most salient one being aligning the release trains with value streams. Failing with this led to cross-release train dependencies and coordination overhead, inhibiting agility. Further, the organization did not get rid of projects and project managers, which led to priority clashes and coordination overhead.
The combination of learning methods with Model Predictive Control (MPC) has attracted a significant amount of attention in the recent literature. The hope of this combination is to reduce the ...reliance of MPC schemes on accurate models, and to tap into the fast developing machine learning and reinforcement learning tools to exploit the growing amount of data available for many systems. In particular, the combination of reinforcement learning and MPC has been proposed as a viable and theoretically justified approach to introduce explainable, safe and stable policies in reinforcement learning. However, a formal theory detailing how the safety and stability of an MPC-based policy can be maintained through the parameter updates delivered by the learning tools is still lacking. This paper addresses this gap. The theory is developed for the generic robust MPC case, and applied in simulation in the robust tube-based linear MPC case, where the theory is fairly easy to deploy in practice. The paper focuses on reinforcement learning as a learning tool, but it applies to any learning method that updates the MPC parameters online.
MEDER 2018, the IFToMM International Symposium on Mechanism Design for Robotics, was the fourth event in a series that was started in 2010 as a specific conference activity on mechanisms for robots. ...The aim of the MEDER Symposium is to bring researchers, industry professionals, and students together from a broad range of disciplines dealing with mechanisms for robots, in an intimate, collegial, and stimulating environment. In the 2018 MEDER event, we received significant attention regarding this initiative, as can be seen by the fact that the Proceedings contain contributions by authors from all around the world.The Proceedings of the MEDER 2018 Symposium have been published within the Springer book series on MMS, and the book contains 52 papers that have been selected after review for oral presentation. These papers cover several aspects of the wide field of robotics dealing with mechanism aspects in theory, design, numerical evaluations, and applications.This Special Issue of Robotics (https://www.mdpi.com/journal/robotics/special_issues/MDR) has been obtained as a result of a second review process and selection, but all the papers that have been accepted for MEDER 2018 are of very good quality with interesting contents that are suitable for journal publication, and the selection process has been difficult.
Safe motion planning for autonomous vehicles is a challenging task, since the exact future motion of other traffic participant is usually unknown. In this article, we present a verification technique ...ensuring that autonomous vehicles do not cause collisions by using fail-safe trajectories. Fail-safe trajectories are executed if the intended motion of the autonomous vehicle causes a safety-critical situation. Our verification technique is real-time capable and operates under the premise that intended trajectories are only executed if they have been verified as safe. The benefits of our proposed approach are demonstrated in different scenarios on an actual vehicle. Moreover, we present the first in-depth analysis of our verification technique used in dense urban traffic. Our results indicate that fail-safe motion planning has the potential to drastically reduce accidents while not resulting in overly conservative behaviors of the autonomous vehicle.
Worldwide, approximately one in ten people acquire a foodborne disease due to eating contaminated food. This often occurs at home and young adults in particular often lack knowledge of and adherence ...to safe food-handling recommendations. Using an experimental design, we compared two groups to investigate whether increasing knowledge and self-efficacy would improve food safety behaviour in young adults in comparison to increasing knowledge alone. All participants (N = 221) completed questionnaires assessing safe food-handling knowledge, self-efficacy, and behaviour, and watched an educational safe food-handling campaign consisting of four short videos providing information on how to safely cook, clean, prepare, and chill food. The experimental group (N = 121) created an action plan, set a safe food-handling related goal, and made a commitment to change their safe food-handling behaviour. The control group (N = 100) completed a similar task not specific to safe food-handling. One week later, the questionnaires were repeated. Repeated measures ANOVAs showed that self-efficacy and behaviour increased in both groups but there was a significantly greater increase in the experimental group. Knowledge increased significantly in the experimental group, but did not in the control group. No mediation of self-efficacy on safe food-handling behaviour in the experimental group was found. The intervention was successful in improving the impact of the educational materials and further, self-efficacy increased significantly even when applied to a non-related topic. This has important implications for improvements in safe food-handling media campaigns.
•This study found improvements in self-efficacy and safe food-handling behaviour.•Increases in self-efficacy and behaviour were greatest in the experimental group.•Food safety knowledge significantly increased in the experimental group.
A challenging problem in robotics is how to control multiple robots cooperatively and safely in real-world applications. Yet, developing multi-robot control methods from the perspective of safe ...multi-agent reinforcement learning (MARL) has merely been studied. To fill this gap, in this study, we investigate safe MARL for multi-robot control on cooperative tasks, in which each individual robot has to not only meet its own safety constraints while maximising their reward, but also consider those of others to guarantee safe team behaviours. Firstly, we formulate the safe MARL problem as a constrained Markov game and employ policy optimisation to solve it theoretically. The proposed algorithm guarantees monotonic improvement in reward and satisfaction of safety constraints at every iteration. Secondly, as approximations to the theoretical solution, we propose two safe multi-agent policy gradient methods: Multi-Agent Constrained Policy Optimisation (MACPO) and MAPPO-Lagrangian. Thirdly, we develop the first three safe MARL benchmarks—Safe Multi-Agent MuJoCo (Safe MAMuJoCo), Safe Multi-Agent Robosuite (Safe MARobosuite) and Safe Multi-Agent Isaac Gym (Safe MAIG) to expand the toolkit of MARL and robot control research communities. Finally, experimental results on the three safe MARL benchmarks indicate that our methods can achieve state-of-the-art performance in the balance between improving reward and satisfying safety constraints compared with strong baselines. Demos and code are available at the link (https://sites.google.com/view/aij-safe-marl/).2
•The problem of safe multi-agent reinforcement learning is formulated.•Multi-agent constrained policy optimisation (MACPO) method is proposed.•MACPO ensures both safety constraints satisfaction and monotonic performance improvement guarantee.•Three safe MARL benchmarks are developed: Safe Multi-Agent MuJoCo (Safe MAMuJoCo), Safe Multi-Agent Robosuite (Safe MARobosuite) and Safe Multi-Agent Isaac Gym (Safe MAIG).•Experiments on multiple benchmark environments confirm the effectiveness of MACPO and MAPPO-Lagrangian.
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