The U.S. Department of Transportation awarded Southern California's commuter rail system 1.3 million to develop an artificial intelligence-powered security system to detect unexpected movement on Metrolink tracks. The technology would aim to automatically slow down or stop a train when cameras and sensors verified the presence of a person, vehicle or debris, Metrolink said about the proposed "track intrusion detection" system. The technology would integrate with existing GPS that notifies train crew about a possible track danger, such as a homeless encampment or a pedestrian. "If it succeeds, this project will not only improve the safety of our passengers and crew, it will directly benefit pedestrians, cyclists, drivers and everyone else who interacts with our system," Los Angeles City Council President and Metrolink Board member Paul Krekorian said in a statement. The current system, which is also linked to the U.S. earthquake-warning system, relies heavily on what people see and report in real time.

This technical report presents research results achieved in the field of verification of trained Convolutional Neural Network (CNN) used for image classification in safety-critical applications. As running example, we use the obstacle detection function needed in future autonomous freight trains with Grade of Automation (GoA) 4. It is shown that systems like GoA 4 freight trains are indeed certifiable today with new standards like ANSI/UL 4600 and ISO 21448 used in addition to the long-existing standards EN 50128 and EN 50129. Moreover, we present a quantitative analysis of the system-level hazard rate to be expected from an obstacle detection function. It is shown that using sensor/perceptor fusion, the fused detection system can meet the tolerable hazard rate deemed to be acceptable for the safety integrity level to be applied (SIL-3). A mathematical analysis of CNN models is performed which results in the identification of classification clusters and equivalence classes partitioning the image input space of the CNN. These clusters and classes are used to introduce a novel statistical testing method for determining the residual error probability of a trained CNN and an associated upper confidence limit. We argue that this greybox approach to CNN verification, taking into account the CNN model's internal structure, is essential for justifying that the statistical tests have covered the trained CNN with its neurons and inter-layer mappings in a comprehensive way.

In this paper, a quantitative risk assessment approach is discussed for the design of an obstacle detection function for low-speed freight trains with grade of automation (GoA)~4. In this 5-step approach, starting with single detection channels and ending with a three-out-of-three (3oo3) model constructed of three independent dual-channel modules and a voter, a probabilistic assessment is exemplified, using a combination of statistical methods and parametric stochastic model checking. It is illustrated that, under certain not unreasonable assumptions, the resulting hazard rate becomes acceptable for specific application settings. The statistical approach for assessing the residual risk of misclassifications in convolutional neural networks and conventional image processing software suggests that high confidence can be placed into the safety-critical obstacle detection function, even though its implementation involves realistic machine learning uncertainties.

Drone footage from Coconino County Emergency Management shows the aftermath of a train derailment in Williams, Arizona. A drone video captured the aftermath of a massive train derailment in Arizona involving a freight train that emergency officials say was "carrying a variety of new cars, vans and trucks." The train, operated by BNSF, derailed around midnight Wednesday in Williams, located outside of Flagstaff, according to Coconino County Emergency Management. "A total of 23 cars derailed and sustained heavy damage. The train cars involved were carrying a variety of new cars, vans and trucks," Coconino County officials said.

It's clear that AI art is growing fast and will continue to capture the imaginations of people around the world – especially now it's in the hands of TikTokers. It's a clear indication of how fast the disruptive technology is growing in popularity and hitting the mainstream. Verdict has contacted TikTok and Open AI for comment. GlobalData is the parent company of Verdict and its sister publications.

Autonomous technology continues to make an impact on the supply chain. The autonomous supply chain, applies to moving goods without human intervention (to some degree at least) or aiding in achieving inventory accuracy. One of the more interesting examples is the Belgian brewery De Halve Maan, which in an effort to reduce congestion on the city streets, built a beer pipeline under the streets. The pipeline is capable of carrying 1,500 gallons of beer an hour at 12 mph to a bottling facility two miles away. Autonomous technology is seen in warehouses and stores, on highways and in mines, and in last mile deliveries.

Autonomous technology continues to make an impact on the supply chain. The autonomous supply chain, as I am writing about it here, applies to moving goods without human intervention (to some degree at least). One of the more interesting examples I have seen is from the Belgian brewery De Halve Maan, which in an effort to reduce congestion on the city streets, built a beer pipeline under the streets. The pipeline is capable of carrying 1,500 gallons of beer an hour at 12 mph to a bottling facility two miles away. As we've written about here quite often, autonomous technology is mainly seen in warehouses, on highways, and in last mile deliveries.

Last summer, a 30-car freight train led by three diesel locomotives rumbled down the tracks for 48 miles through the Colorado desert -- with nobody at the controls. But this was no runaway train. In fact, the experiment could be a preview of the rail industry's future. The demonstration at the Transportation Technology Center -- a research and testing facility owned by the Association of American Railroads -- was the debut of driverless train software produced by one of the oldest companies in the industry. Along for the ride were representatives from some of America's largest freight railroads who in recent years have been intrigued by the many ways artificial intelligence (AI) could be applied to one of the nation's oldest industries.

As marketers, we are always trying to predict what is going to happen in the future. The only thing we know about all predictions is that, more often than not, they will be wrong – and sometimes embarrassingly so.

French train engineering giant Alstom is to test automated freight trains in the Netherlands this year. The automated train prototype can travel for about 100km (60 miles) without driver intervention. Automation will free the train driver to focus on supervising the train's progress. The test's purpose is to provide a live demonstration that the train and the signal system can communicate effectively to drive the train. Alstom signed an agreement with the the Dutch infrastructure operator ProRail and Rotterdam Rail Feeding (RRF) to carry out the tests along the Betuweroute - a 150km double track freight railway line connecting Rotterdam to Germany.