Nowadays, traffic management in urban areas is one of the major economic problems. In particular, when faced with emergency situations like firefighting, timely and efficient traffic dispatching is crucial. Intelligent coordination between multiple departments is essential to realize efficient emergency rescue. In this demo, we present a framework that integrates techniques for collaborative learning methods into the well-known Unity Engine simulator, and thus these techniques can be evaluated in realistic settings. In particular, the framework allows flexible settings such as the number and type of collaborative agents, learning strategies, reward functions, and constraint conditions in practice. The framework is evaluated for an emergency rescue scenario, which could be used as a simulation tool for urban emergency departments.

Firefighters came to the rescue of delivery robots that found their path blocked by crews tackling a building blaze. The robots, now common in parts of Cambridge, are used by the Co-op in the city for customers to order home or workplace deliveries. But they found their path blocked by a fire engine and hoses in the city on Saturday night. Posting on X, Cambridgeshire Fire and Rescue said: "Sorry @coopuk our hoses and fire engines confused your delivery robots in Cambridge this evening as we tackled a building fire, but firefighters helped them on their way - hopefully not too many delays!" The supermarket thanked the crew for its assistance and posted it was " glad to hear they were able to help".

Despite substantial progress in applying neural networks (NN) to a wide variety of areas, they still largely suffer from a lack of transparency and interpretability. While recent developments in explainable artificial intelligence attempt to bridge this gap (e.g., by visualizing the correlation between input pixels and final outputs), these approaches are limited to explaining low-level relationships, and crucially, do not provide insights on error correction. In this work, we propose a framework (VRX) to interpret classification NNs with intuitive structural visual concepts. Given a trained classification model, the proposed VRX extracts relevant class-specific visual concepts and organizes them using structural concept graphs (SCG) based on pairwise concept relationships. By means of knowledge distillation, we show VRX can take a step towards mimicking the reasoning process of NNs and provide logical, concept-level explanations for final model decisions. With extensive experiments, we empirically show VRX can meaningfully answer "why" and "why not" questions about the prediction, providing easy-to-understand insights about the reasoning process. We also show that these insights can potentially provide guidance on improving NN's performance.

Samsung has shown off an 8K QKED bezel-less TV that is 99 per cent screen and ultra-thin – only 15mm. Fellow South Korean rival LG has its own set of OLED TVs that double as'a piece of art' thanks to an outer edge that mimics a picture frame and the ability to display HD art pieces when not in use. Sony unveiled a concept connected car loaded with sensors and technology from its audio/visual business as part of its own push into mobility. Panasonic had as part of its CES showcase a miniature, battery-powered prototype fire engine that can transport the same level of equipment as a full-sized fire engine but at a fraction of the cost and energy. Lenovo has showcased its foldable PC with a 13.3-inch screen that it says is more durable than Samsung's Galaxy Fold.

As we get closer to the widespread deployment of autonomous vehicles, there's still discussions about what sensors will be required to make these vehicles safe – for the passengers and other pedestrians and vehicles. Those discussions usually revolve around cameras, radar, LiDAR (Light Distance and Ranging), and ultrasonic. There's one other sensor that isn't discussed – an audio microphone. I recently experienced a situation that caused me concern over how well an autonomous car would handle emergency vehicle awareness. I was stopped at a red light in the left-hand turn lane on a local road.

You need just two eyes and two ears to drive. Those remarkable sensors provide all the info you need to, say, know that a fire engine is coming up fast behind you, so get out of the way. Autonomous vehicles need a whole lot more than that. They use half a dozen cameras to see everything around them, radars to know how far away it all is, and at least one lidar laser scanner to map the world. Yet even that may not be enough.