For robots to interact socially, they must interpret human intentions and anticipate their potential outcomes accurately. This is particularly important for social robots designed for human care, which may face potentially dangerous situations for people, such as unseen obstacles in their way, that should be avoided. This paper explores the Artificial Theory of Mind (ATM) approach to inferring and interpreting human intentions. We propose an algorithm that detects risky situations for humans, selecting a robot action that removes the danger in real time. We use the simulation-based approach to ATM and adopt the 'like-me' policy to assign intentions and actions to people. Using this strategy, the robot can detect and act with a high rate of success under time-constrained situations. The algorithm has been implemented as part of an existing robotics cognitive architecture and tested in simulation scenarios. Three experiments have been conducted to test the implementation's robustness, precision and real-time response, including a simulated scenario, a human-in-the-loop hybrid configuration and a real-world scenario.

Intersection crossing represents one of the most dangerous sections of the road infrastructure and Connected Vehicles (CVs) can serve as a revolutionary solution to the problem. In this work, we present a novel framework that detects preemptively collisions at urban crossroads, exploiting the Multi-access Edge Computing (MEC) platform of 5G networks. At the MEC, an Intersection Manager (IM) collects information from both vehicles and the road infrastructure to create a holistic view of the area of interest. Based on the historical data collected, the IM leverages the capabilities of an encoder-decoder recurrent neural network to predict, with high accuracy, the future vehicles' trajectories. As, however, accuracy is not a sufficient measure of how much we can trust a model, trajectory predictions are additionally associated with a measure of uncertainty towards confident collision forecasting and avoidance. Hence, contrary to any other approach in the state of the art, an uncertainty-aware collision prediction framework is developed that is shown to detect well in advance (and with high reliability) if two vehicles are on a collision course. Subsequently, collision detection triggers a number of alarms that signal the colliding vehicles to brake. Under real-world settings, thanks to the preemptive capabilities of the proposed approach, all the simulated imminent dangers are averted.

NCAA athlete Macy Petty says A.I. chatbot ChatGPT'promoted inclusivity' when asking it a prompt about transgender athletes competing in women's sports. My entire high school life I worked to achieve the prized title of "NCAA athlete." But now, through a series of regulatory decisions, the almighty organization that controls college sports has drained the title of its honor. In elementary school, I spent hours outside my house learning to overhand serve a volleyball. By the end of middle school, I decided I was willing to make significant sacrifices to extend my volleyball career into college and hopefully earn a scholarship. This was no easy feat!

Suspense is an important tool in storytelling to keep readers engaged and wanting to read more. However, it has so far not been studied extensively in Computational Literary Studies. In this paper, we focus on one of the elements authors can use to build up suspense: dangerous situations. We introduce a corpus of texts annotated with dangerous situations, distinguishing between 7 types of danger. Additionally, we annotate parts of the text that describe fear experienced by a character, regardless of the actual presence of danger. We present experiments towards the automatic detection of these situations, finding that unsupervised baseline methods can provide valuable signals for the detection, but more complex methods are necessary for further analysis. Not unexpectedly, the description of danger and fear often relies heavily on the context, both local (e.g., situations where danger is only mentioned, but not actually present) and global (e.g., "storm" being used in a literal sense in an adventure novel, but metaphorically in a romance novel).

The development of Automated Vehicles (AVs) is progressing quickly and the first robotaxi services are being deployed worldwide. However, to receive authority certification for mass deployment, manufactures need to justify that their AVs operate safer than human drivers. This in turn creates the need to estimate and model the collision rate (failure rate) of an AV taking all possible errors and driving situations into account. In other words, there is the strong demand for comprehensive Mean Time Between Failure (MTBF) models for AVs. In this paper, we will introduce such a generic and scalable model that creates a link between errors in the perception system to vehicle-level failures (collisions). Using this model, we are able to derive requirements for the perception quality based on the desired vehicle-level MTBF or vice versa to obtain an MTBF value given a certain mission profile and perception quality.

When considering the accuracy of sensors in an automated vehicle (AV), it is not sufficient to evaluate the performance of any given sensor in isolation. Rather, the performance of any individual sensor must be considered in the context of the overall system design. Techniques like redundancy and different sensing modalities can reduce the chances of a sensing failure. Additionally, the use of safety models is essential to understanding whether any particular sensing failure is relevant. Only when the entire system design is taken into account can one properly understand the meaning of safety-relevant sensing failures in an AV. In this paper, we will consider what should actually constitute a sensing failure, how safety models play an important role in mitigating potential failures, how a system-level approach to safety will deliver a safe and scalable AV, and what an acceptable sensing failure rate should be considering the full picture of an AV's architecture.

Drowning is the third leading cause of accidental death, according to World Health Organization (WHO) data, with an estimated 320,000 fatalities each year globally. While lifeguards play a crucial role in helping safeguard beaches and pools, the human eye struggles to spot swimmers in distress in large crowds or at a distance -- with or without the help of binoculars. Sightbit is harnessing AI to alert lifeguards to potential drowning incidents, as well as flagging other hazardous situations, such as unattended children and rip currents. Founded in 2019, Israel-based Sightbit is a spinout from Ben-Gurion University of the Negev (BGU). The public research university invests in alumni via its Cactus Capital VC fund and has provided pre-seed funding to Sightbit, which is currently raising additional funds as part of a seed round.

The advent of self-driving cars revived the decades-old philosophical conundrum known as the "trolley problem." The basic setup is this: A vehicle is hurtling toward a group of five pedestrians, and the only way to save them is to swerve and run over a single pedestrian instead. For philosophers and psychologists, it's pure thought experiment -- a tool to tease out and scrutinize our moral intuitions. Most people will never face such a stark choice, and even if they did, studies suggest their reaction in the moment would have little to do with their views on utilitarianism or moral agency. Self-driving cars have given the problem a foothold in the real world.

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If you think regular snakes are scary, try a robotic serpent that can scale ladders. Japanese researchers at Kyoto University have unveiled a prototype'robot snake' that can slither and curl around surfaces and climb just like the real thing. Rather than just creating it to trigger nightmares, they believe it could one day be used to save lives. Kyoto University researchers say the robot snake could be used to enter dangerous situations that are unsafe for humans. It may also be able to rescue humans that are stuck in hard to reach places. They developed an advanced gait for the device that could enable it to crawl through narrow pipes that would otherwise be inaccessible by search and rescue teams.