We present a hybrid multi-robot coordination framework that combines decentralized path planning with centralized conflict resolution. In our approach, each robot autonomously plans its path and shares this information with a centralized node. The centralized system detects potential conflicts and allows only one of the conflicting robots to proceed at a time, instructing others to stop outside the conflicting area to avoid deadlocks. Unlike traditional centralized planning methods, our system does not dictate robot paths but instead provides stop commands, functioning as a virtual traffic light. In simulation experiments with multiple robots, our approach increased the success rate of robots reaching their goals while reducing deadlocks. Furthermore, we successfully validated the system in real-world experiments with two quadruped robots and separately with wheeled Duckiebots.

Vehicle-to-anything connectivity, especially for autonomous vehicles, promises to increase passenger comfort and safety of road traffic, for example, by sharing perception and driving intention. Cooperative maneuver planning uses connectivity to enhance traffic efficiency, which has, so far, been mainly considered for automated intersection management. In this article, we present a novel cooperative maneuver planning approach that is generalized to various situations found in urban traffic. Our framework handles challenging mixed traffic, that is, traffic comprising both cooperative connected vehicles and other vehicles at any distribution. Our solution is based on an optimization approach accompanied by an efficient heuristic method for high-load scenarios. We extensively evaluate the proposed planer in a distinctly realistic simulation framework and show significant efficiency gains already at a cooperation rate of 40%. Traffic throughput increases, while the average waiting time and the number of stopped vehicles are reduced, without impacting traffic safety.

The widespread use of e-bikes has facilitated short-distance travel yet led to confusion and safety problems in road traffic. This study focuses on the dual characteristics of e-bikes in traffic conflicts: they resemble pedestrians when interacting with motor vehicles and behave like motor vehicles when in conflict with pedestrians, which raises the right of way concerns when potential conflicts are at stake. Using the Quantal Response Equilibrium model, this research analyzes the behavioral choice differences of three groups of road users (vehicle-pedestrian, vehicle-e-bike, e-bike-pedestrian) at right-turn-on-red crossroads in right-turning lines and straight-going lines conflict scenarios. The results show that the behavior of e-bikes is more similar to that of motor vehicles than pedestrians overall, and their interactions with either pedestrians or motor vehicles do not establish a reasonable order, increasing the likelihood of confusion and conflict. In contrast, a mutual understanding has developed between motor vehicles and pedestrians, where motor vehicles tend to yield, and pedestrians tend to cross. By clarifying the game theoretical model and introducing the rationality parameter, this study precisely locates the role of e-bikes among road users, which provides a reliable theoretical basis for optimizing traffic regulations.

The world is heading towards a dangerous place where selective government outrage and "a la carte" application of international law are becoming the norm. The result is already damning: a crisis of credibility and the erosion of trust in international institutions and governments, putting in peril the rules-based world order. As the heads of Amnesty International and Center for Civilians in Conflict, two of the world's most prominent organisations for human rights and protection of civilians, we have a simple demand for the world leaders who will be coming together on Friday for the 2024 Munich Security Conference: Protect international humanitarian and international human rights laws which are the best tools we have for protecting civilians in times of conflict, and stop creating exceptions that weaken rights protection and endanger global security and stability. Unfortunately, in 2023, world leaders responded unevenly to the countless violations of international humanitarian and human rights law we witnessed in various conflicts across the world. They expressed outrage at the crimes committed by some warring parties while offering diplomatic cover for others.

On January 18, Time magazine published revelations that alarmed if not necessarily surprised many who work in Artificial Intelligence. The news concerned ChatGPT, an advanced AI chatbot that is both hailed as one of the most intelligent AI systems built to date and feared as a new frontier in potential plagiarism and the erosion of craft in writing. Many had wondered how ChatGPT, which stands for Chat Generative Pre-trained Transformer, had improved upon earlier versions of this technology that would quickly descend into hate speech. The answer came in the Time magazine piece: dozens of Kenyan workers were paid less than $2 per hour to process an endless amount of violent and hateful content in order to make a system primarily marketed to Western users safer. It should be clear to anyone paying attention that our current paradigm of digitalisation has a labour problem. We have and are pivoting away from the ideal of an open internet built around communities of shared interests to one that is dominated by the commercial prerogatives of a handful of companies located in specific geographies.

Western countries have been ramping up weapons and ammunition shipments to Ukraine as Kyiv fights off a Russian invasion, but arms trade experts warn some of the lethal assistance could end up falling into the wrong hands. Ukraine in particular has a history as a hub of the arms trade during the 1990s, setting off alarm bells for those who study illicit flows. "There are very significant risks associated to the proliferation of weapons in Ukraine at the moment, in particular regarding small arms and light weapons," said Nils Duquet, a researcher and director of the Flemish Peace Institute. Western nations, above all the US, have announced successive shipments of both light and heavy weapons for Kyiv's forces since Russian troops crossed the Ukrainian border on February 24. Washington alone has delivered or promised military gear including hundreds of Switchblade kamikaze drones, 7,000 assault rifles with 50 million rounds of ammunition, laser-guided missiles and radar systems to detect enemy drones and incoming artillery fire.

Humans and artificial intelligences (AI) will increasingly participate digitally and physically in conflicts, yet there is a lack of trusted communications across agents and platforms. For example, humans in disasters and conflict already use messaging and social media to share information, however, international humanitarian relief organisations treat this information as unverifiable and untrustworthy. AI may reduce the 'fog-of-war' and improve outcomes, however AI implementations are often brittle, have a narrow scope of application and wide ethical risks. Meanwhile, human error causes significant civilian harms even by combatants committed to complying with international humanitarian law. AI offers an opportunity to help reduce the tragedy of war and deliver humanitarian aid to those who need it. In this paper we consider the integration of a communications protocol (the 'Whiteflag protocol'), distributed ledger technology, and information fusion with artificial intelligence (AI), to improve conflict communications called 'Protected Assurance Understanding Situation and Entities' (PAUSE). Such a trusted human-AI communication network could provide accountable information exchange regarding protected entities, critical infrastructure; humanitarian signals and status updates for humans and machines in conflicts.

The nature of war continues to evolve through the 21st century with conflict zones shifting from jungles and deserts to coastal cities. Not to mention the rapidly increasing commercial availability of cutting-edge technologies including UAVs and wireless communications. To help the Marine Corps best prepare for these increased complexities and challenges, the Department of Defense tasked DARPA with developing a digital training and operations planning tool. The result is the Prototype Resilient Operations Testbed for Expeditionary Urban Scenarios (PROTEUS) system, a real-time strategy simulator for urban-littoral warfare. When the PROTEUS program first began in 2017, "there was a big push across DARPA under what we call a sustainment focus area, and that included urban warfare," Dr. Tim Grayson, director of DARPA's Strategic Technology Office, told Engadget, looking at how to best support and "sustain" US fighting forces in various combat situations until they can finish their mission.

Reinforcement learning (RL) has recently been used for solving challenging decision-making problems in the context of automated driving. However, one of the main drawbacks of the presented RL-based policies is the lack of safety guarantees, since they strive to reduce the expected number of collisions but still tolerate them. In this paper, we propose an efficient RL-based decision-making pipeline for safe and cooperative automated driving in merging scenarios. The RL agent is able to predict the current situation and provide high-level decisions, specifying the operation mode of the low level planner which is responsible for safety. In order to learn a more generic policy, we propose a scalable RL architecture for the merging scenario that is not sensitive to changes in the environment configurations. According to our experiments, the proposed RL agent can efficiently identify cooperative drivers from their vehicle state history and generate interactive maneuvers, resulting in faster and more comfortable automated driving. At the same time, thanks to the safety constraints inside the planner, all of the maneuvers are collision free and safe.

Reinforcement learning is nowadays a popular framework for solving different decision making problems in automated driving. However, there are still some remaining crucial challenges that need to be addressed for providing more reliable policies. In this paper, we propose a generic risk-aware DQN approach in order to learn high level actions for driving through unsignalized occluded intersections. The proposed state representation provides lane based information which allows to be used for multi-lane scenarios. Moreover, we propose a risk based reward function which punishes risky situations instead of only collision failures. Such rewarding approach helps to incorporate risk prediction into our deep Q network and learn more reliable policies which are safer in challenging situations. The efficiency of the proposed approach is compared with a DQN learned with conventional collision based rewarding scheme and also with a rule-based intersection navigation policy. Evaluation results show that the proposed approach outperforms both of these methods. It provides safer actions than collision-aware DQN approach and is less overcautious than the rule-based policy.